Alzheimer’s disease (AD) is the most common form of dementia. Recently, anti-amyloid-β (Aβ) antibody therapy has been applied to treat early AD. Therefore, the diagnosis of early AD has become more important than ever before. Cerebrospinal fluid (CSF) biomarkers such as amyloid-β (Aβ) and tau proteins are widely recognized indicators of AD pathology. Lumbar puncture to obtain CSF is invasive, and this procedure tends to be unfavorable for patients and their families. Although amyloid positron emission tomography (PET) is another method to confirm AD pathology, it is expensive. Single photon emission computed tomography (SPECT), which shows regional cerebral blood flow, has the advantage of being relatively inexpensive and can be used for repeated examination under medical insurance coverage in dementia clinical practice. To address this limitation, we present an image-to-biomarker deep learning framework for non-invasive prediction of CSF Aβ levels by leveraging multimodal neuroimaging. Our method integrates structural information from T1-weighted MRI and functional information from brain perfusion SPECT. All images are spatially normalized using DARTEL and processed using a ResNet-based 3D convolutional neural network (CNN). A multitask regression model is employed to simultaneously predict Aβ42, Aβ40, and the Aβ42/40 ratio, which is clinically valued for its robustness to individual variability, with age incorporated as an auxiliary input to improve prediction performance. The model is trained using a weighted mean squared error loss function and evaluated through five-fold cross-validation on a dataset of 153 patients with dementia. Ablation studies demonstrate that including both brain perfusion SPECT and age information significantly improves prediction performance, achieving root mean squared errors (RMSEs) of 500 pg/mL for Aβ42, 3,838 pg/mL for Aβ40, and 0.0355 for the Aβ42/Aβ40 ratio. These findings underscore the potential of our image-to-biomarker approach as a practical and accurate solution for CSF biomarker prediction, offering promising applications in improving the accuracy of early AD diagnosis, clinical decision-making support, and reducing the psychological, physical, and financial burden on patients and their families.

This study proposes a self-extubation early warning system based on autoencoder anomaly detection with capacitive touch sensor arrays and introduces a novel simulator for quantitative evaluation. The detection system uses a smart dressing material embedded with 5 × 5 capacitive sensor array to continuously monitor tube displacement and contact conditions without compromising patient privacy. An unsupervised spatio-temporal autoencoder is trained on normal interaction patterns to detect deviations associated with self-extubation risk. Additionally, a heatmap-based visualization is employed to provide intuitive spatial feedback. To enable reproducible performance assessment, we developed a dedicated simulator that replicates realistic pulling scenarios under controlled conditions. The simulator incorporates an acceleration sensor to quantify pulling dynamics and supports repeatable evaluations of system behavior. Experiments demonstrated that the system achieved high detection accuracy, with a recall of 0.972 and an F1 score of 0.882 in simulated early warning scenarios. The combined approach provides a deployable detection system and a standardized evaluation framework, contributing to a proactive and privacy-preserving solution for self-extubation prevention in ICU and elderly care settings.

Swallowing disorders have become a major problem among the aging population. A chin-up posture, along with curvature of the back, is considered unfavorable for swallowing and may lead to aspiration of saliva or food. To prevent this, a chin-down posture has been widely adopted; however, it has not been consistently defined. Therefore, we propose an innovative aspiration risk assessment system based on swallowing posture, using computer-based image analysis. The main contribution of this study is that the proposed system can assess aspiration risk with a high Area Under the Curve (AUC) of 0.799. This result was achieved solely by training a Light Gradient Boosting Machine (LightGBM) using total 113 features on lateral-view images of swallowing posture. In addition, we identified three novel predictive indexes: shoulder angle, the angle formed by tragus, acromion, and posterior and superior iliac spine (PSIS), and the angle formed by acromion, 4th lumbar vertebrae (L4), and PSIS. The proposed system was evaluated using a dataset of 68 participants over 65 years of age, using leave-one-out cross-validation (LOOCV). Its performance showed improvement compared to previous manual evaluations based on head angle, cervical angle, shoulder angle, pelvic angle, kyphosis index, and cervical range of motion. The experimental results indicated that cervical range of motion was not a particularly important factor. Moreover, it is expected that this assessment system will be applicable in the future. This will require only lateral-view photographs of swallowing posture taken with a smartphone.

Bronchopulmonary dysplasia (BPD) in preterm infants is a major concern in neonatal intensive care, necessitating early and accurate detection for improved outcomes. Despite the use of clinical information in previous studies to assess BPD severity, there is a gap in early prediction through imaging techniques. This paper proposed a novel method using convolutional neural networks (CNNs) for the early prediction of BPD from neonatal chest X-rays. We employed two strategies: first, analyzing chest X-ray images taken on specific days post-birth to evaluate day-wise BPD predictive performance using CNN models; and second, aggregating these images to enhance the training dataset. Thirteen specific CNN architectures were evaluated using a five-fold cross-validation method on a dataset acquired at four distinct time points: 3, 7, 14, and 28-days post-birth. The dataset included 115 preterm infants, 51 with BPD and 64 normal, classified based on their condition at 36 weeks of post-menstrual age. MobileNetV2 demonstrated consistent and fairly above-moderate performance among the networks used, with calculated metrics showing an accuracy of 0.665 ± 0.045, an AUC of 0.736 ± 0.053, a recall of 0.635 ± 0.042, a precision of 0.647 ± 0.046, and an F1-score of 0.641 ± 0.042. The results highlight the potential of CNNs in enhancing early diagnostic accuracy for BPD in neonatal patients using chest X-ray images.

Ureteral stones, a prevalent type of urinary stone, form within the ureter, causing severe pain and hematuria. Extracorporeal shock wave lithotripsy (ESWL) and transurethral lithotripsy (TUL) are the primary treatments. Although ESWL is less invasive and requires shorter hospital stays, its lower success rate compared to TUL, often necessitates additional treatments, increasing both the physical and financial burdens on patients. This study aims to enhance the accuracy of predicting ESWL outcomes by using advanced machine learning methods to analyze both CT and X-ray images together with clinical findings. Particularly, X-ray images, which have been less frequently in past studies, are anticipated to provide detailed analytical features. Addressing the limitations of current predictive methods, which often suffer from excessive irrelevant features and low interpretability, this study introduces three sophisticated feature selection approaches: P-value, AUC, and SHAP value. These approaches aim to enhance both model interpretability and predictive performance. Several machine learning algorithms were evaluated, including decision tree, K-nearest neighbors, random forest, logistic regression, SVM, adaboost, and Gaussian NB. Results from 139 subjects with ESWL show that logistic regression and SVM perform optimally, with SHAP-based feature selection significantly boosting outcomes, demonstrated by the increase in the AUC from 0.791 to 0.845 for logistic regression, and from 0.750 to 0.764 for SVM. Furthermore, the study decreases the number of features used in the model from 55 to 19, simplifying the prediction process. However, features extracted from X-ray images showed limited effectiveness.

The incidence of falls in hospital facilities is high and can lead to a decrease in patients' quality of life and an increase in medical expenses. Therefore, the development of a system that can predict getting-up from a bed is necessary. This study proposes a get-up detecting sensor using 6-axis inertial sensor. This system can detect getting-up from a bed in real-time using machine learning with Edge AI. To evaluate the basic performance of the proposed system, a protocol was applied for four subjects, and data were collected. Alert accuracy rate and false alert rate were used as evaluation metrics, and a model was built using data from three of the subjects and evaluated with the remaining subject, which was repeated for all four subjects. For high-risk bed-leaving behavior, medium-risk pre-bed-leaving behavior, and low-risk get-up behavior, the alert accuracy rate (i.e., Recall) was 89.4%, 97.5%, and 86.3%, respectively, and the false alert rate (1-Precision) was 6.3%, 10.2%, and 0.0%, respectively. This confirmed the possibility of predicting rising behavior with high accuracy. Furthermore, as a proof of concept, a real-time get-up detection system was developed, and its practicality was demonstrated. Future challenges include reevaluating feature extraction and evaluating the performance of the proposed system with a diverse range of subjects of different ages, genders, and health statuses.

Chronic lung disease (CLD) is the most common and serious lung disease in premature infants. In this study, we predict the severity (mild or severe) of neonatal chest X-ray images using a convolutional neural network (CNN) to enable early intervention to provide personalized treatment and improve prognosis. Thirty subjects were tested in a leave-one-out cross validation experiment using 30 chest X-ray images of 11 patients with mild disease and 19 patients with severe disease at 7 days of age. To improve the prediction accuracy, we proposed to limit the input image of the CNN to the lung field region and to use a pre-training model for transfer learning. Four different experiments were conducted, comparing the results with different input images (whole image or lung field region) and with and without transfer learning. The results showed that the best accuracy was obtained when the entire image was used as input and no transfer learning was performed, with an Accuracy of 0.667.

Baseball elbow is a pitching elbow disorder caused by repeated pitching movements. Osteochondritis dissecans (OCD) is one of baseball elbow disorders, and is an intractable osteochondral injury that tends to occur in elementary and junior high school students. If it can be found in the early stages, it will be completely cured by conservative treatment, which is to set a period to stop playing baseball. Since there is almost no pain in the early stage, the hurdles for consultation are high and there are many cases in which the condition becomes severe. Periodical medical check of baseball elbow is effective, however, the number of implementations is several times a year due to the shortage of specialists who can make a diagnosis. In this study, for the purpose of developing computer-aided diagnosis (CADx) of early-stage OCD, we propose an OCD detection method using ultrasound images of the elbow. The proposed method first segments the humerus capitellum using fully convolutional network (FCN). Secondly, the segmented region is classified into OCD +/- classes using fine-tuning VGG16 to detect OCD. The proposed method was applied to 125 child baseball players including 61 OCD children and 64 healthy children. 5-fold cross-validation was conducted. The average detection results were 76.8% for accuracy, 100% for precision, 52.3% for recall, F1-score was 0.673, and AUC was 0.851.

In recent years, development and spread of medical imaging devices such as X-ray Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) have facilitated acquisition of medical images with low invasiveness and high resolution. At present, some research is being actively conducted to segment a specific area in the image by performing image analysis using the medical image data. In this study, we proposed a method to segment the abdominal organ area using U-Net for cancer radiotherapy support. We investigated segmentation performance of two methods; the first method segments the bladder, the prostate and the rectum, individually, and the second method segments their composite region. As the results, the first method achieved the better performance, and the Dice coefficient was 0.96 on average.

The number of Rheumatoid Arthritis (RA) patients increases recently in Japan. Early treatment improves patient's prognosis and Quality of Life. The appropriate treatment in accordance with RA progression is required for the better prognosis. The hand X-ray image based modified Total Sharp Score (mTSS) is widely used for the diagnosis of RA progression. The mTSS measurement is essential to achieve the appropriate treatment, but its assessment is time consumed. There are some finger joint detection and mTSS estimation methods for the fully automated mTSS measurement, which focus on the mild RA patients. This paper proposes the automatic joint detection method and discusses about the mTSS estimation for the mild-to-severe RA patients. Experimental results on 90 RA patients' hand X-ray images showed that the proposed method detected finger joints with accuracy of 91.8%, and estimated the erosion and JSN score with accuracy of 53.3% and 60.8%, respectively.

During the early developmental stage, the brain undergoes more changes in size, shape, and appearance than at any other stage in life. A better understanding of brain development can decrease the symptom of development disorder through very early detection and application of remedial education. In this paper, we present a computer-aided diagnosis (CAD) system, which estimates onset probability of brain development disorder using neonatal brain MR images. The CAD system first constructs spatiotemporal statistical shape model (stSSM) of neonatal brain, extracts static and dynamic morphological features, and estimates the probability using machine learning techniques. This paper proposes the stSSM construction method which produces temporally continuous Eigenvectors by extending previous EM-based-stSSM construction method. The method has been validated by applying it to 12 neonatal brains whose revised ages are between - 5 to 730 days.

At present, orthopedic surgery has a large variety of surgical techniques. Procedures are complicated, and many types of equipment have been using in the surgery. So, operating room nurses who deliver surgical instruments to surgeon are supposed to be forced to incur a heavy burden. Although there is a navigation system for assisting surgeons in artificial joint replacement surgery, but no system exists for assisting operating room nurses. This work proposes a computer-aided navigation system that indicates the current procedure and procedure progress for nurses, and also instructs nurses to prepare surgical instruments to be used in the next procedure using smart glasses. Firstly, the system estimates the current status of the surgery procedure using a convolutional neural network (CNN) by utilizing real-time video images taken from smart glasses which was worn by operating surgeon. Then, the system indicates nurses the surgical instrument to be used at the next procedure in the smart glass worn by the nurses. The system was implemented with the object detection technology and the augmented reality. Experiment results demonstrated a satisfactory performance of our proposed system of recognizing surgery procedures.

Total knee arthroplasty (TKA) improves patient's Quality of Life (QoL) whose knee has pain caused by aging and diseases. During the TKA surgery, the physician subjectively selects the size and type of the TKA prosthesis. The implanted knee kinematics in-vivo is essential for the evaluation of its function after the surgery. The 2-D/3-D still image registration based conventional methods do not consider the temporal continuity of the knee kinematics. This study proposes a kinematics analysis method for implanted knee using particle filter. Particle filter algorithm requires high computational cost for the accurate outcome. This paper proposes the new prediction model which evaluates the relative pose/position of the femoral and the tibial implants. The experimental results showed that the smooth estimation results were obtained with low computational time.

The paper introduces a proposal for an automated magnetic resonance (MR) image segmentation called Case-Based Genetic Algorithm Location-Dependent Image Classification (CBGA-LDIC) and presents its evaluation results. This method finds an appropriate cell set towards efficient image segmentation. It uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC is a local heuristic, which defines multiple location-dependent classifiers. Each classifier is trained by Gaussian mixture model. CBGA-LDIC decomposes the whole image into some cells, makes a set of cells, and then trains classifiers. The method is applied to knee bones, because these bone formations are similar in their location. Therefore, good combinations of cells are useful and stored in case bases. To show, that this method produces better results that other ones and to find optimal parameters, some experiments have been performed and their results are presented in this paper.

Solar flare has various influences on the global environment, in particular on the magnetic storm and the likelihood of natural disasters. Specifically, it may have serious impacts on the Earth such as failure of satellite communication and navigation (GPS), satellite damage, increased radiation exposure to astronauts, geomagnetic storm and aurora, and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, which is basically a daily forecast of the solar flare. In the work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used to forecast the space weather. We extend this technology by integrating Genetic Algorithm (GA) elaborately combined with Case Based Reasoning for more precise forecast or imbalanced data classification. Finally, basic evaluation of this architectural idea called CBGALO shows it is promising in improving solar flare prediction.

Anterior cruciate ligament (ACL) injury causes knee joint instability, and affects on sports performance. Therefore, ACL reconstruction is essential to keep their performance high and to prevent osteoarthrosis. It is well known that the outcome of ACL reconstruction is strongly related to the placement and orientation of the bone tunnel. 2-D X-ray radiograph and CT images have been used to evaluate the placement and orientation of the bone tunnel. Quadrant method evaluates the bone tunnel placement based on the Blumensaat's line which has high intensity on 2-D X-ray lateral radiograph. There is problem of invasiveness using X-ray radiograph or CT image. Therefore, we have proposed an MR image based computer-aided surgical planning of ACL reconstruction. The system evaluates the bone tunnel placement and orientation based on Quadrant method. The remained problem of our system is Blumensaat's line is manually determined. This paper proposes that a method to synthesize the pseudo lateral radiograph from MR images, and extract the Blumensaat's line on the synthesized pseudo lateral radiograph. The experimental results showed that the proposed method successfully determined the Blumensaat's line on the pseudo lateral radiograph.

In this paper, an application of self-organizing maps (SOM's) in classifying and predicting data of female subjects with unhealthy-level visceral fat is discussed. The proposed method chooses subjects fulfilling the standard specified by body mass index and abdominal circumference. It defines the class with subjects of which hemoglobin A1c (HbA1c) values and item values associated with a liver deteriorate, that with subjects having HbA1c and triglyceride values deteriorate, and that with remaining subjects. Normal SOM learning is conducted, using data generated from original values of twelve items such as HbA1c and glutamic-oxaloacetic. The constructed map consists of neurons with labels. The label of a winner determines the class of the presented unknown data. The prediction depends on the label of a winner for the presented unknown data, a set of original data that determine the label, and a set of next year's data of the subjects with the above original data. Experimental results reveal that the proposed method achieves the reasonably favorable accuracies in classifying data and in predicting HbA1c values.

In the infertility treatment, medical examinations using ultrasonic devices, which can diagnose the mother's body safely on real time, are major. It is very difficult to judge the existence of an ovum before carrying out the paracentesis. A system which can distinguish the existence of the ovum in the ovarian follicle using ultrasonic devices is required. In this paper, several features of the deformation of ovarian follicle are defined and extracted from the ultrasonic moving image in a paracentesis operation. These features are extracted from the ultrasonic moving image obtained at the time of an ovum extraction operation. We investigate whether some clusters according to the existence of the ovum are formed using the defined features. Moreover, we also investigate whether some tendency exists in these features by the existence of an ovum.

Solar activity has various influences on the global environment. Specifically, it may have serious impacts on the Earth such as satellite damage, etc. and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, a daily forecast of the solar flare. In our work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used. We extended this technology by integrating Case Based Genetic Algorithm (CBGA) for a more precise forecast. It was shown experimentally that triple mutation rate on the slowdown of evolution in our CBGA improves considerably (e.g. another 5%) more than original mutation rate in the True Skill Statistics TSS. For further obtaining the optimality towards more imbalanced data analysis applicable to the recognition of serious disaster or medical disease, Restart CBGA is proposed with its expected effect. Here GA integrating SVM is restarted using highly optimized but diversified solutions in the case base as initial individuals. Further this restart CBGA is repetitively and evolutionary performed, evolving and maintaining the case base by the result of each (restarted) GA.

It is known that lifestyle habit and genetic factor are main reasons that occur cerebral aneurysms. In addition, some studies suggest that cerebral artery shape might be correlated with a risk of occurring aneurysms. For the purpose of preemptive medical care of the cerebral aneurysm, this study proposes a method to estimate a risk of occurring cerebral aneurysms based on the cerebral artery structure. The method extracts morphometric features of the Wills ring such as 3-D artery shape and bifurcation angle in 3-D magnetic resonance angiography (MRA) images. It then estimates the risk of occurring cerebral aneurysms from the extracted features using support vector machines (SVM). To validate the proposed method, we employed 40 subjects with cerebral aneurysms, and 40 subjects without cerebral aneurysms. Leave-one-out cross validation test was performed, and the method using 3-D artery shape achieved a sensitivity of 75% and a specificity of 75%; one using bifurcation angle did a sensitivity of 33% and a specificity of 71%; one using all features did a sensitivity of 68% and a specificity of 89%. The results showed that 3-D shape is effective for cerebral aneurysm occurrence risk prediction.

Magnetic resonance (MR) images are widely used to diagnose cerebral diseases. The diseases may deform the brain shape, and the deformed region differs among types of diseases. To evaluate the brain shape deformation, MR image registration (IR) has been used. There are some IR methods for brain MR images but they mainly use MR signal based likelihood. We cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal distribution and brain shape. This paper focuses on neonatal brain MR images, and introduces a sulcus extraction method using Hessian matrix based on a feature called sulcal-distribution index (SDI). SDI is calculated from MR signal on the cerebral surface. Next, this paper proposes an iterative closest point (ICP) based brain shape registration method using the extracted sulci. The proposed method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the correspondence of cerebral sulci distribution. Results in seven neonates (modified age was between 3 weeks and 2 years) showed that the method registered one brain with the other brain successfully.

Anterior cruciate ligament (ACL) injury causes knee joint instability, and effects on sports performance. Therefore, ACL reconstruction is essential to keep their high performance. It is well known that the outcome of ACL reconstruction is strongly related to the placement and orientation of the bone tunnel. Therefore, optimization of tunnel drilling technique is an important factor to obtain satisfactory surgical results. Current procedure relies on arthroscopic evaluation and there is a risk of damaging arteries and ligaments during surgery. The damages may reduce the accuracy and reproducibility of ACL reconstruction. As a postoperative evaluation method, a quadrant method has been used to evaluate the placement and orientation of the bone tunnel in X-ray radiography. This study proposes a computer-aided surgical planning system for evaluating ACL insertion site and orientation using magnetic resonance (MR) images. We first introduce MR image based the quadrant method to determine the ACL insertion site for preoperative patients. It also evaluates the 3-D spatial relationship between the planning femoral drilling hole and arteries around the femoral condyle. This system has been applied to ACL injured patients, it may increase the accuracy and reproducibility of ACL bone tunnel, and it can evaluate a risk of damaging the surrounding arteries and ligaments.

The neonatal cerebral disorders severly languish the quality of life (QOL) of patients and also their families. It is required to detect and cure in their early stage for the sake of decreasing the degree of symptoms. However, it is difficult to evaluate neonatal brain disorders based on morphological analysis because the neonatal brain grows quickly and the brain development progress is different from person to person. Previously, we proposed a method of calculating growth index using Manifold learning. The growth index is effective to evaluate the brain morphological development progress, although, it does not directly correspond to the brain development delay. To evaluate brain development delay, this paper proposes an estimation method of neonatal brain age using Manifold learning, principal component analysis, and multiple regression model. The regression model is trained using a 4-D standard brain, which is constructed using training subjects with growth index. To evaluate the proposed method, we constructed a multiple regression model using 11 normal subjects (revised age: 0-4 month old), and estimated brain age of 4 normal subjects. And, we estimated brain age of 4 abnormal subjects to evaluate the detection accuracy of brain development abnormality. The results showed that the method found the differences of brain development for abnormal subjects.

To develop a computer-aided diagnosis system for neonatal cerebral disorders, some literatures have shown atlas-based methods for segmenting parenchymal region in MR images. Because neonatal cerebrum deforms quickly by natural growth, we desire an atlas growth model to improve the accuracy of segmenting parenchymal region. This paper proposes a method for generating fuzzy object growth model (FOGM), which is an extension of fuzzy object model (FOM). FOGM is composed of some growth index weighted FOMs. To define the growth index, this paper introduces two methods. The first method calculates the growth index from revised age. Because the growth index will be different from person to person even through the same age, the second method estimates the growth index from cerebral shape using Manifold learning. To evaluate the proposed methods, we segment the parenchymal region of 16 subjects (revised age; 0-2 years old) using the synthesized FOGM. The results showed that FOGM was superior to FOM, and the Manifold learning based method gave the best accuracy. And, the growth index estimated with Manifold learning was significantly correlated with both of revised age and cerebral volume (p<;0.001).

Total hip arthroplasty (THA) is an orthopaedic surgery which replaces the damaged hip joint with implants. The acetabular cup is implanted to the acetabulum. Some studies show that the outcome of THA is strongly correlated to the orientation of the acetabular cup. This paper proposes a fully automated method for measuring the orientation using multidetector-row computed tomography (MDCT) images. The method defines the pelvic anatomical coordinate system using anterior pelvic plane (APP), and measures angles between the cup implanting axis and the pelvic anatomical coordinate axis. The angles are inclination angle and anteversion angle. The method was applied to two phantoms in which the acetabular cup was implanted to the artificial bone. We acquired multiple set of MDCT image for the same phantom with changing the pelvic pose in the MDCT scanner to evaluate the reproducibility. The standard deviations of measured angles in multiple acquisitions were less than 2.0 deg for both of the inclination and the anteversion angles.

Patient specific instruments (PSI) system has been attracting considerable attention for navigation-free surgical operation of total knee arthroplasty (TKA). PSI is a jig which guides a cutting section of the femoral or the tibia bones where TKA implant is attached to ensure accurate and reproducible surgery, and is prepared for a specific patient. However, another problem has been raised because the attachment of PSI will cause errors of TKA guidance. This paper proposes a novel system to evaluate the accuracy of attaching PSI during TKA operation using cone-beam CT. The system acquires 3-D sectional images of PSI attached to knee, and evaluates the attachment accuracy by means of image registration techniques with computer-aided design (CAD). It calculates the position and the angle of guidance pin, and compares with the preoperative planning. The system has been applied to three subjects which had been operated TKA with PSI. The results produced 3- D renderings of the attached PSI and of the planned PSI, and calculated angle differences between the attached and the planned guidance pin. By using them, we can evaluate the attachment accuracy of PSI, and also evaluate the implanting accuracy of TKA.

Image segmentation plays a fundamental work to analyze medical images. Although many literatures studied automated image segmentation, it is still difficult to segment region-of-interest in any kind of images. Thus, manual delineation is important yet. In order to shorten the processing time and to decrease the effort of users, this paper introduces two approaches of interactive image segmentation method based on fuzzy connectedness image segmentation (FCIS). The first approach interactively updates object affinity of FCIS according to users' additional seed voxels. The second approach models the profile of the object affinity using radial-basis function network (RBFN), and applies online training for users' additional seed voxels. The proposed methods updates segmentation results for not only the seed voxels but also the other miss-classified voxels. The methods had been applied to neonatal brain magnetic resonance (MR) images. The experimental results showed the second approach produced the best results.

Bone tissue engineering techniques have become new approaches in bone regeneration. Before clinical implantation, the preconditioning is needed. Therefore, we implement the ultrasonic evaluation system without cellular destruction. This study focuses the cellular proliferation into the composites of bone marrow stromal cells (BMSCs) / β-tricalcium phosphate (β-TCP) and composes the ultrasonic cell quantity determination on frequency domain for the BMSCs / β-TCP composites after being cultured: 4 types BMSCs to 24 β-TCP scaffolds. This system aims viscous attenuation because viscosity is proportional to frequency-squared. On frequency domain, we confirmed the attenuation in the immediate vicinity of 1.0 MHz, which is the center frequency of the probe. Moreover, it is discussed and concluded, the findings in this work illustrate that the frequency properties of BMSCs / ß-TCP composites have the prominent osteoconductive activity and the potential for applications/approaches in future regenerative medicine.

Asthma causes the bronchus inflammation, and makes breathing impossible. In worst case, asthma leads to death due to dyspnea. If we can predict that children cause asthmatic attacks, they can prevent from asthmatic attacks with minimum attention. Therefore, asthmatic attacks prediction system is desired. As a prediction system using time series data, there is Fuzzy-AR model that can consider multi factors. In this paper, we propose a prediction method of the number of asthmatic attacks on next month based on Fuzzy-AR model. The proposed method considers weather factors; temperature, atmospheric pressure and humidity data. This method is applied to asthmatic attacks data from Himeji city Medical Association. As a comparison method, AR model is applied to same data. The experimental results shown that the proposed method predicts the number of asthmatic attacks better than AR model.

This paper describes a seminiferous tubules evaluation using an ultrasonic probe. In this system, we evaluate a diameter of seminiferous tubules for azoospermia patients. We employ a 5.0MHz ultrasonic single probe. In the experiment, we employ large and small nylon lines as the healthy and unhealthy seminiferous tubules. We made ball shape phantom from small and large lines in total 24. We acquire the waveforms by the ultrasonic probe and calculate amplitude values from the data that band pass filters applied. We then calculate cumulative relative frequency of amplitude values. Fuzzy if-then rules are made for the cumulative relative frequency of large and small lines. We evaluate a rate of large lines among all lines by using the fuzzy MIN-MAX center-of-gravity method. In the result, the mean absolute error was 5.98%. The correlation coefficient was 0.98. The proposed method thus successfully evaluated the rate of the large lines.

This paper proposes a body weight prediction method using Fuzzy-autoregressive (AR) model. New Fuzzy-AR model is formed by including fuzzy membership function which changes AR parameter in autoregressive (AR) model. We employed 452 volunteers, and collected their body weight time-series data during 730 days. We use body weight data from 1st to 365th day as learning data to determine the Fuzzy-AR models. After AR parameters are determined by Yule-Walker equation, we calculate the order, p, of the AR model for each volunteer based on Akaike's Information Criterion (AIC). In our experiment, we predicted body weight change for next p days for those subjects. In the Fuzzy-AR model, we make a fuzzy membership function based on the order of the AR model. As the result, the Fuzzy-AR model obtained higher correlation coefficient between predicted and truth values than the AR model on all volunteers. In addition, the Fuzzy-AR model obtained smaller mean absolute prediction error than the AR model.

Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and fuzzy object models. The proposed method evaluates object affinity and homogeneity using the MR signal, and employs a fuzzy object model, which is built from training datasets. We have evaluated the proposed method based on 10 newborn MR images with subject revised age between -1 month and 2 months. These studies indicate that the use of a fuzzy object model is effective in improving the segmentation accuracy.

This paper describes a testicular tubules evaluation using 1.0MHz ultrasonic array probe. In this system, we evaluate a diameter of testicular tubules. We employ an ultrasonic array probe with the center frequency of 1.0MHz. We employ evaluation index that cumulative relative frequency of amplitude values. In the experiment, we employ 24 nylon lines as the testicular tubules. Amplitude of large nylon line echo is larger than that of small nylon echo. For the evaluation, we calculate cumulative relative frequency amplitude of acquisition data. Fuzzy if-then rules are made by the cumulative relative frequency of large and small lines. We evaluate a rate of large lines among all lines by using the fuzzy MIN-MAX center-of gravity method. In this experiment, the proposed method successfully evaluated the rate of the large lines. We changed the rate of large lines in 24 nylon lines, and tested our method 20 times for each rate. We evaluated the rate with 5.77% in mean absolute error.

This paper describes a personal verification method based on fuzzy logic using dynamic sole pressure distribution while walking. The method employs a pair id sole pressure distribution change, and it is acquired by a mat type load distribution sensor. As a preliminary experiment for shoes, we take sole pressure data by bare foot and two kind of slippered foot. We extract thirty nine gait features from each sole pressure distribution change. We calculate a fuzzy degree of a feature from two fuzzy if-then rules and them fuzzy membership functions for a feature. These fuzzy membership functions are statistically determined by learning data. The fuzzy degree of acquisition sole pressure data is calculated by total of fuzzy degree of all features. The method verifies the walking person by using the fuzzy degree of the acquisition sole pressure data. When the fuzzy degree of acquisition data higher than a threshold, we verify the walking person as the target person. In our experiments, we employed 11 volunteers and took sole pressure data six times for each volunteer and foot situation. When the learning data included same kind of test data, we obtained low equal error rate. We obtained low false acceptance rate.

This paper proposes a body weight prediction method using auto regressive (AR) model and Fuzzy-AR model. First, we employ 6 persons body weight change data of 365 days. AR model predicts body weight of a day from these time-series data. We calculate an order of AR model for each person by Akaike's Information Criterion. In the experiment, we predicted body weight change of next day for those subjects. The AR model obtained 0.798 in correlation coefficient between predicted and truth values. Second, we propose a Fuzzy-AR model that predicts body weight of next p days from last p days, where p is the order of AR model. In this method, we propose a Fuzzy-AR model with the fuzzy membership function using last p days data. In the experiment, the Fuzzy-AR model obtained 0.558 in correlation coefficient on 2 subjects.

Implanted knee kinematic analysis plays one of important role in clinical and research fields of total knee arthroplasty. Although there are some studies to analyze X-ray images for estimating 3-D knee kinematics, most of them cannot analyze dynamic video because they strongly depend on manual interaction of giving initial pose/position. This paper utilizes particle filter for analyzing dynamic radiograph video of implanted knee. By using particle filter, the proposed method does not require not only user interaction but also computational iteration of parameter optimization. As the result, we shorten the computation time and improved the estimation accuracy. The estimation error was lower than 0.7 mm for rotation, and 0.5 mm for translation including out-plane, and the computation time was 1.27 sec per frame using a cluster computer.

This paper describes a trans-skull ultrasonic system that measures the blood flow velocity in brain under skull. In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the blood flow and locate blood vessel by Doppler effect. This Doppler effect is examined by the center of gravity shift in the frequency domain. We use three silicon tubes of different thickness imitated to blood vessel. We confirmed the change of frequency quantity of Doppler effect according to the water current velocity. The experimental result shows that the system detects the flow velocity by Doppler effect under skull and do automatic extracting method of water current.

This paper describes a foot age estimation system using fuzzy logic. The method employs sole pressure distribution change data. The sole pressure data is acquired by a mat type load distribution sensor. The proposed method extracts step length, step center of sole pressure width, distance of single support period and time of double support period as gait features. The fuzzy degrees for young age, middle age and elderly groups are calculated from these gait features. The foot age of the walking person on the sensor is estimated by fuzzy MIN-MAX center of gravity method. In the experiment, the proposed method estimated subject ages with good correlation coefficient.

This paper describes a method for a heartbeat and respiratory rate monitoring system using air pressure sensors and ultrasonic oscillosensor. By using these sensors, we propose a detection method of the state of human and an extraction method of heartbeat and respiratory rate in bed by fuzzy logic. Our method was examined on four healthy volunteers. We successfully detected the state of human and extracted heartbeat and respiratory signals. In our method, fuzzy logic plays a primary role in the detection of the state and extraction of heartbeat and respiratory signals. An experiment on four healthy volunteers was done. Consequently, our proposed method noninvasively and successfully detects the state of human and extracted heartbeat and respiratory rate in the bed by using the unconstrained sensors.

For estimating 3-D pose position of artificial knee implants in vivo, there are some studies based on 2-D/3-D image registration of 2-D fluoroscopy images and 3-D geometrical model. Knee implant mainly consists of femoral component and tibial component. Most conventional studies estimate 3-D pose position of femoral component and tibial component individually. Rather, they don't evaluate relative position between the femoral and tibial components. This paper proposes a method for estimating 3-D pose position of implanted knee based on particle filter. A priori knowledge on the relational position of the components are utilized by using fuzzy membership functions. The experimental results for a patient and simulation DR images showed that the proposed method adequately estimate 3-D pose position of the femoral and tibial components with respect to relational position between the components.

This paper proposes a biometric personal authentication method based on one step foot pressure distribution change. We acquire the foot pressure distribution change by mat type load distribution sensor and use it as a personal authentication. We employ twelve features based on shape of footprint, and twenty seven features based on movement of weight while walking. A classifier for each feature is developed on the basis of fuzzy inference. The classifier is trained by a clonal selection algorithm in artificial immune system. A personal authentication system for one step is made every classifier for all features. We employed 10 volunteers, and we took the step data five times. We evaluated our method by five-fold cross validation method. We obtained low false rejection and acceptance rates in identification and verification.

Automated morphometric analysis using human brain magnetic resonance (MR) images is an effective approach to investigate the morphological changes of the brain. However, even though many methods for adult brain have been studied, there are few studies for infantile brain. Same as the adult brain, it is effective to measure cerebral surface and for quantitative diagnosis of neonatal and infantile brain diseases. This article proposes a skull stripping method that can be applied to the neonatal and infantile brain. The proposed method can be applied to both of T1 weighted and T2 weighted MR images. First, the proposed method estimates intensity distribution of white matter, gray matter, cerebrospinal fluid, fat, and others using a priori knowledge based Bayesian classification with Gaussian mixture model. The priori knowledge is embedded by representing them with fuzzy membership functions. Second, the proposed method optimizes the whole brain by using fuzzy active surface model, which evaluates the deforming model with fuzzy rules. The proposed method was applied to 26 neonatal and infantile subjects between -4 weeks and 4 years 1 month old. The results showed that the proposed method stripped skull well from any neonatal and infantile MR images.

This paper describes a trans-skull ultrasonic system that measures the blood flow velocity through the brain's blood vessel under skull. In this system, we use an ultrasonic array probe with the center frequency of 1.0MHz. The system determines the blood flow by Doppler effect. This Doppler effect is examined by the center of gravity shift in the frequency domain. We test the system in the condition of the water flow in silicon tube under the cow scapula. The experimental result shows that the system detects the flow velocity by Doppler effect and confirms the usefulness of the method under skull.

In this paper, we analyze human gait pattern and estimate her/his foot age. We acquire foot pressure distribution change as gait pattern by a mat type load distribution sensor. From the foot pressure distribution data, duration of gait cycle and center of foot pressure (CFP) changes are determined for each stride. We employ four estimation indexes such as step length, step CFP width, the time of double supporting period and distance of step CFP changes. We employ 87 volunteers, and divided them to young, middle age and elderly groups. By comparing of three groups, we found that elderly had shorter step length and larger step CFP width than young and middle age people. Besides, the double supporting time of the elderly was longer, and distance of step CFP changes was longer than those of young and middle age people. From these facts, sixteen fuzzy IF-THEN rules are made. We determine a fuzzy degree for her/his foot age by fuzzy MIN-MAX center-of-gravity method. In our experiment on 87 volunteers, we compared these results with regression method.

Quantitative diagnosis of the knee joint dynamics is required to decrease the inner- and intra-observer variability. This paper proposes a noninvasive, unconstrained and free field of measurement system of 3 degree-of-freedom knee joint angles. The proposed system employs a compound sensor of inertial and magnetic sensors. Based on a rigid-body link model, the proposed method enables a measurement system. The experimental results showed that the proposed method estimated the flexion of knee joint angle with a mean displacement of 1.3 deg.

It is effective to evaluate magnetic resonance (MR) images for diagnosing neonatal cerebral disorders because they often accompany the deformation of the brain shape. However, there are many difficulties when radiologists manually extract cerebral surface from the MR images. Therefore, it requires to extract the cerebral surface from neonatal MR images automatically. There are many methods to extract cerebral surface from adult MR images, but there are few methods for neonatal MR images. This paper proposes a new extraction method based on particle method. The proposed method introduces three kinds of particles corresponding to cerebrospinal fluid, gray matter and white matter. First, particles are assigned according to the cerebral shape. Second, particles are moved to form the homogeneous particles, and are transited to the other particles with respect to MR signal. The proposed method was applied to neonatal MR images. The results showed that the proposed method extracted cerebral surface with high accuracy.

This paper describes a method for a respiratory rate monitoring system by an air pressure sensor. By using this sensor, we propose a detection method of a respiratory rate for human in bed by fuzzy logic. Our method was examined on four healthy volunteers. We successfully detected the respiratory rate and the time of apnea state. In our method, fuzzy logic plays a primary role in the detection of respiratory points. The experimental results showed that the error ratio of respiratory rate was 1.3% and the error of time of apnea state was 1.1 seconds. Consequently, this system can noninvasively detect the respiratory rate and the time of apnea state by using an unconstrained device.

Cerebral surface extraction plays a fundamental role of computer aided diagnosis (CAD) for neonatal brain magnetic resonance (MR) images. However, cerebral sulci of the neonatal brains is complexity folded, and it is difficult to extract complete cerebral contour from MR images due to the limitation of spatial resolution and partial volume effect (PVE). This paper proposes a novel method to extract the cerebral contour based on fuzzy thick rubber model (TRM). The TRM is deformed by using fuzzy control schemes so that the digitally synthesized MR images from the deforming TRM are identical to the given MR images. By synthesizing the MR images with respect to PVE, the proposed method is able to extract the cerebral contour with sub-voxel accuracy. The proposed method was applied to 7 subjects whose revised ages were from -17 days to 34 days. The root-mean-squared-error between the extracted contour and the manually delineated contour by two physicians was 1.09 plusmn 0.48 mm from the truth contour. And, to demonstrate the clinical effective, gyral index was calculated using the extracted cerebral contour.

This paper proposes a biometric personal authentication based on the pressure distribution while one step walking. We extract one step from a walk on a mat type load distribution sensor and use it to personal authentication. With this method, features which are based on weight movement and foot shape during walking are calculated, then a classifier is developed on the basis of fuzzy inference. We employed 30 volunteers. All volunteers are ranged from 20 to 85 years old. For each volunteer, we took walk data six times. Then, we evaluated this method by five training data and one test data. We obtained 6.1% EER (Equal Error Rate) and 13.9% FRR (False Rejection Rate) in verification (1:1 collation) and identification (1:N collation), respectively.

Recently, the increase of care burden due to the increase of number of the elderly dementia patients is a matter of concern in Japan. However, dementia of the elderly tends to be wrongly recognized as the effect of aging, and there are many cases in which early detection are difficult. In this paper, we focus on the cognitive impairment as one of the core symptoms of dementia, and propose the fuzzy estimation system to detect the level of dementia through monitoring the participants' sleep using air pressure and ultrasonic sensor systems which were developed by our laboratory. As a result of applying this method to twenty-three women in a nursing home, we could confirm the high correlation between the degree of dementia and the truth value, the score of Revised Hasegawa's dementia scale.

In this paper, we propose a trans-skull imaging system of the human brain using an ultrasonic array probe. In it, we employ a cow scapula imitated to human skull and a steel with ditches imitated to cerebral sulci. We scan the phantom consisting of the bone and steel ditches by a 32channel array probe and obtain the B-mode image. From the B-mode image, we extracted the bone thickness by fuzzy inference, and visualize the ditches by filtering techniques. Experimental result shows that the mean error of bone thickness is less than 1mm and that the mean errors of the ditch width and depth are 6.9mm and 2.8mm, respectively.

Total knee arthroplasty (TKA) is an orthopedic surgery which replaces the damaged knee joint with the artificial one. To diagnose the function of the implanted knee joint, it is effective to estimate 3-D knee kinematics in vivo. There are some conventional methods for estimating kinematics of the implanted knee using 2-D/3-D image registration for X-ray fluoroscopic images and 3-D geometrical models of the knee implant. However, these methods are based on static image analysis although the knee joint continuously moves. This paper proposes an analysis method of the knee kinematics using digital radiography images with Kalman filter. Use of Kalman filter enables us to take into account the continuous knee movement. The experimental results showed that the proposed method estimated the knee joint angles within a mean error of 0.31 deg.

The brain shape is deformed regionally by kinds of cerebral diseases and the degree of progress. Therefore quantitative evaluation of the deformation using MR images is effective for diagnosis of cerebral diseases. To evaluate the cerebral deformation, almost conventional methods are based on normalization of the brain shape which deforms the evaluating brain into the standardized brain. Because the normalization process does not take into account anatomical features such as the cerebral sulci and gyri, in some cases the normalization process produces that one sulcus of the evaluating brain miss-corresponds to the other sulcus of the standardized brain. This paper proposes a homologous brain shape modeling method for quantitative evaluation of the brain shape in MR images. We define a new image feature called sulcal-distribution index (SDI) to represent the 3-D distribution of sulci, and the proposed method deforms a template brain model so that SDI of the deformed brain model calculated from the evaluating brain MR images is similar to SDI of the template brain model. By using SDI, the proposed method can take into account anatomical features of the cerebral sulci. The experimental results showed that the proposed method homologically modeled the brain shape with a mean displacement of 1.3 mm.

This paper proposes functional assessment system of autonomic nervous system by the heart rate variability using an air cushion sensor. The air cushion sensor can unconstraintly detect vital information by sitting down on the sensor. We perform functional assessment of autonomic nervous system by heart rate variability obtained by the system. We built the real time display system for visualizing the autonomic nervous system functions. In this system, we employ fuzzy membership functions with dynamic parameter to detect RR intervals. The experimental results show that we detect RR intervals with the correlation coefficient of 0.846 with comparison to that of electrocardiograph. Then, the errors of the HF (index of parasympathetic system) and the LF/HF (index of sympathetic system) are 18.34% and 16.99%, respectively.

In this paper, a fuzzy identification method for cellular quantity of bone marrow stromal cells (BMSCs) in artificial culture bones is proposed. We attempt to identify for cellular quantity with an ultrasonic system and fuzzy inference approach. We employ two characteristics: the amplitude and the frequency; the amplitude is obtained from the raw ultrasonic wave, and the frequency is calculated from frequency spectrum obtained by applying cross-spectrum method. The experimental results show that the proposed method identifies cellular quantity in artificial culture bone with high accuracy.

We propose a thickness determination method of the intestine with the ultrasonic probe of 15 MHz. We determine the surface point by calculating correlation coefficient between surface echo and an acquisition waveform. Next, we determine the bottom point by calculating amplitude of bottom echo, correlation with surface echo and bottom echo, and interval distance between surface echo and bottom echo. Finally, we calculate the thickness between the surface and the bottom points. As the result, we have obtained the thickness within an error rate of 5.09%.

This paper proposes a functional assessment system of autonomic nervous system using an air pressure sensor. The air pressure sensor can unconstraintly detect vital information by placing it under the mattress in bed. We perform functional assessment of autonomic nervous system by heart rate variability obtained by the system. In this system, we employ fuzzy membership functions with dynamic parameter to detect RR intervals. The experimental results show that we detect RR intervals with the correlation coefficient of 0.851 with comparison to that of electrocardiograph. Then the errors of the HF (index of parasympathetic system) and the LF/HF (index of sympathetic system) are 11.98% and 22.18%, respectively.

Analyzing knee kinematics after total knee arthroplasty (TKA) has been attracting considerable attentions because the knee kinematics can be used to evaluate TKA patients and to evaluate TKA operations and design of knee implants. Knee kinematics can be estimated by 2-D/3-D image registration from 3-D computer-aided design (CAD) models of knee implants to 2-D X-ray image. Although there are many studies for estimating knee kinematics, they have common problems that are dependency on initial pose/position and falling into local maxima. This study proposes a robust 2-D/3-D image registration method based on evolutional computing. The evolutional computing has both characteristics of global search performance and of local search performance. The characteristics are suitable for solving the problems of 2-D/3-D image registration. The proposed system has been evaluated by applying it to computer-synthesized images, X-ray images of phantoms, and X-ray images of TKA patients.

In this paper, we describe an ultrasonography system for locating screw holes of intramedullary nail by one-direction freehand scanning using an ultrasonic array probe. Although conventional X-ray method can visualize the nail in the femur, it has serious problem of X-ray exposure. We propose a locating method of the nail screw holes by an ultrasonic array probe. We extract screw hole regions by calculating two fuzzy degrees: average of the intensity and variance of the intensity using fuzzy inference. Next, we do a registration between the obtained image with the true image, where the true image is the nail image obtained by scanning an array probe to the nail exactly. As the result, we could calculate the center distance of two screw holes within an error of 1.0 mm.

We propose an imaging system of brain surface and skull by considering the ultrasonic refraction of the skull. We do an experiment by using a cow scapula to imitate the skull bone and a biological phantom to imitate cerebral sulcus. We first visualize the shape of skull. We second calculate the thickness of the skull aided by fuzzy logic. Finally, we calculate the refractive angle of ultrasonic wave and visualize the image referring to the refraction of ultrasonic wave. In the result of applying this method, we can successfully visualize the phantom surface image.

Measuring visual axis on the eye closure will play one of important roles to investigate the brain function during sleep. It has been investigated using a simultaneous measurement system composed of functional MRI and infrared-video which takes palpebra images with eye closure. Although there are some methods for measuring visual axis from video images, they cannot be applied to estimate visual axis with eye closure because their methods are based on tracing pupil reflection or Purkinje image. This paper proposes a novel method for fully- automatically estimating visual axis with eye closure using infrared-video. The method evaluates intensity profile on palpebra using artificial neural network (ANN). The ANN is preliminary trained using visual axes detected from MR image of eyeball. The experimental results showed that the proposed method detected visual axes of right and left eyes within the errors of 1.30±3.34 (RMSE±SD) deg and 1.12±3.70 deg, respectively.

In this paper, we propose a personal identification method using sole information. We employ pressure changes of sole in walking. First, we do a preliminary examination using a load distribution sensor. We employ a neural network for a personal identification method. As the result, we show a possibility of an identification system by sole information using the load distribution sensor. Based on the result, we propose a personal identification method by sole pressure changes using three air pressure sensors. This method could identify one of five volunteers at 85.4% recognition rate.

This paper describes a design method for fuzzy ultrasonic medical system and its applications. First, we describe a design method for manipulating features of ultrasonic data. Second we apply this design method to the system identifying a surface roughness. A fuzzy inference system is designed with the average of amplitudes and the standard derivation of the echo duration for the learning waves. The system identifies the degree of roughness: one of smooth, medium and rough. In the experiments on phantoms, it has successfully identified their surface roughness. Third, we apply this design method to an location system for the screw hole position of the intramedullary nail in a bone. The screw hole position is determined by applying the fuzzy inference system. As the results, the accuracy can guarantee the clinical practice usage.

Based on principals of blood oxygenation level dependent (BOLD) effect, MR signals raised according to brain neural activity. Such temporal change of MR signal is called Hemodynamic response function (HRF). Because HRF varies among activation sites and among subjects, we have been considering that estimating HRF will be available for analyzing neurological condition of cerebrum and for investigating and diagnosing cerebral diseases. Many conventional methods for analyzing functional MR images have been proposed, however, there are few methods that can detect activation areas in the cerebral cortex and can estimate HRF. This paper proposes a method that detects activation areas and estimate HRF simultaneously. The performance of estimating HRF is evaluated by phantom study, and results are compared with statistical parametric mapping (SPM).

Hypoxic ischemic encephalopathy (HIE) caused by asphyxia in the womb causes the decrement of the white matter (WM). Therefore, calculating the volume of the cerebral tissues for the infant with such symptom helps us for the purpose of quantifying the acuteness of symptom. Many methods for classifying the adult brain tissues with magnetic resonance (MR) images have been studied. However, these methods cannot be applied to classify the infantile brain tissues because the WM undergoes a myelination process in infantile brain, and the infantile brain image features are very different from adult one. This paper aims to propose a method for classifying the brain tissues in the myelination process. The proposed method addresses the intensity nonuniformity (INU) artifact by locally adapting a fuzzy spatial model of MR signals. The fuzzy model represents transition of MR signals on a line from the cerebral contour to inside the cerebrum. By using the fuzzy spatial model, the proposed method assigns fuzzy degree belonging to the cerebral cortex into voxels dependent of their locations.

Measurement of volume and surface area of the frontal, parietal, temporal and occipital lobes from magnetic resonance (MR) images shows promise as a method for use in diagnosis of dementia. This article presents a novel computer-aided system for automatically segmenting the cerebral lobes from 3T human brain MR images. Until now, the anatomical definition of cerebral lobes on the cerebral cortex is somewhat vague for use in automatic delineation of boundary lines, and there is no definition of cerebral lobes in the interior of the cerebrum. Therefore, we have developed a new method for defining cerebral lobes on the cerebral cortex and in the interior of the cerebrum. The proposed method determines the boundaries between the lobes by deforming initial surfaces. The initial surfaces are automatically determined based on user-given landmarks. They are smoothed and deformed so that the deforming boundaries run along the hourglass portion of the three-dimensional shape of the cerebrum with fuzzy rule-based active contour and surface models. The cerebrum is divided into the cerebral lobes according to the boundaries determined using this method. The reproducibility of our system with a given subject was assessed by examining the variability of volume and surface area in three healthy subjects, with measurements performed by three beginners and one expert user. The experimental results show that our system segments the cerebral lobes with high reproducibility.

This paper describes a dentistry support system for root canal treatment using ultrasonic. Presently, no complete method of root canal treatment has proposed. Dentist empirically removes the dental pulp using measures of length of root canal. Therefore, the support system for root canal treatment is required to precisely remove the dental pulp. This paper solves the problem in root canal treatment by our ultrasonic device and fuzzy logic techniques. We determine the dentin-dental pulp junction by calculating two fuzzy degrees of amplitude of the echo and approximate distance of the surface and the junction. As the result, our system can determine the thickness of dental pulp within error of 0.289 mm.

Most of the methods for investigating brain function observe the localization of brain activity responded by the external stimuli which are executed with simply and repeatedly. On the contrary, when brain function is rest state such as falling asleep, a spontaneous brain signal measured by EEG or the other modalities is used as a trigger of the stimulus. This paper proposes an analysis method for detecting activation areas and activation times from fMRI time series data without giving tasks or stimuli for a subject. The results indicated that the proposed method detected activation areas with similar accuracy to those obtained by SPM99. Also, the proposed method was applied to a subject who did not conduct any tasks. The results showed that the method detected various brain activations, which would be spontaneous brain event, while we give no stimuli or tasks

This paper proposes a fuzzy rule-based system for estimating the surface roughness by ultrasonic waveform. Our estimation method consists of three steps. The first step extracts characteristic values from an object with known surface roughness. The second step constructs fuzzy membership functions with respect to characteristic values. At the final step, the fuzzy rule-based system with estimates the surface roughness of an object with unknown surface roughness. Moreover, we propose a method for removing noise of the result by using characteristic value of ultrasonic echo. We applied this method to phantom with three kinds of surface roughness. Then, our system can successfully estimate the roughness of the phantom.

Support implant has been used to reconstruct an artificial hip joint on the acetabulum in total hip arthroplasty (THA). After THA, we should diagnose periodically state of the support implant because the implants may be distorted or broken. This paper proposes an in vivo evaluation method using multidetector-row computed tomography (MDCT) images. The proposed method estimates the distortion degree of the support implant by comparing the 3D geometric model of the support implant with the support implant region segmented from the MDCT images. The support implant region is segmented from the MDCT images using a fuzzy object model which can express knowledge about the shape of objects. The distortion degree is estimated based on a multiscale matching algorithm. The performance of estimating the distortion degree was validated through computer simulation experiments, phantom experiments in vitro, and subject experiments.

In this paper, we propose a real-time position and pose tracking method for pin insertion to a pipe-like hole in almost uniform motion region. In the conventional methods for estimating position or pose of the region, a three-dimensional (3D) model data of the target region is often needed. For reducing the processing time, our approach achieves the position and pose estimation without the model data. In order to estimate and track it using a single camera, we set some landmarks on the region surface. Additionally, Kalman filters are used to estimate 3D position of the landmarks. We apply the proposed method to the insertion system for a pipe-like hole in a solid object. In the experimental results using a manipulation robot, we show that it can insert a stick into the hole in the almost uniform motion region in real-time.

Various methods for generating arbitrary view images from input images of cameras have been proposed. These conventional methods use multiple cameras. In this paper, we propose a method to generate arbitrary view images using a single camera in known room. Especially, we perform basic experiments to apply our method to surveillance. We use one pan tilt camera. Unknown objects in known room are replaced by simple shapes such as rectangular parallelepiped, triangle pole, circular cylinder, etc. Then, by using camera parameters and 3 dimensional (3D) spatial information of the room, the 3D position and model of unknown objects are estimated, and arbitrary view images are generated. Since we use a single camera, we can easily set it in secluded spot. In experimental results, we show the effectiveness of the proposed method.

A conventional ultrasonography system can noninvasively provide human tissue and blood flow velocity information with real-time processing. In general, since the human skull prevents the disclosure of brain anatomy, we usually placed the sensor at the anterior and superior attachment site of the upper ear (the posterior temporal window) in adults. Due to this limitation, the conventional system cannot obtain transcranial information from arbitrary places in the skull. This paper describes a transcranial sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and brain disease such as cerebral atrophy and epidural or subdural hematoma. In this system, we develop anatomical knowledge of the human head, and we employ fuzzy inference to determine the skull and brain surface. To evaluate our method, three models are applied: the phantom model, the animal model with soft tissue, and the animal model with brain tissue. In all models, the shapes of the skull and the brain tissue surface are successfully determined. Next, the method is applied to two adults. As a result, we have determined the skin surface, skull surface, skull bottom, and brain tissue surface for the subjects' foreheads. Consequently, our system can provide the skull and brain surface information using three-dimensional shapes.

This paper shows an automated method for segmenting the lateral ventricles from human brain MR images. It enables automatic volumetry and construction of 3D rendering images. First, we segment the whole brain and the cerebrospinal fluid (CSF) using 3D MR image processing software developed by Y. Hata et al. (see IEEE Trans. Syst., Man, Cybern. C, vol.30, no.3, p.381-95, 2000). Our method for segmenting the lateral ventricles is based on fuzzy inference techniques and is able to represent expert knowledge and to introduce the knowledge to image processing. The employed knowledge on the lateral ventricles consists of the location, the intensity and the shape. The proposed method was applied to MR volumes of 20 normal volunteers, 20 Alzheimer disease and 20 hydrocephalus patients. The experimental results validated that this method was able to segment the lateral ventricles with high accuracy.

This paper shows an application of fuzzy information granulation (fuzzy IG) to medical image segmentation. Fuzzy IG is to derive fuzzy granules from information. In the case of medical image segmentation, information and fuzzy granules correspond to an image taken from a medical scanner, and anatomical parts, namely region of interests (ROIs), respectively. The proposed method to granulate information is composed of volume quantization and fuzzy merging. Volume quantization is to gather similar neighboring voxels. The generated quanta are selectively merged according to degrees for pre-defined fuzzy models that represent anatomical knowledge of medical images. The proposed method was applied to blood vessel extraction from three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) images of the brain. The volume data studied in this work is composed of about 100 contiguous and volumetric MRA images. According to the fuzzy IG concept, information correspond to the volume data, fuzzy granules corresponds to the blood vessels and fat. The qualitative evaluation by a physician was done for two- and three-dimensional images generated from the obtained blood vessels. The evaluation shows that the method can segment MRA volume data, and that fuzzy IG is applicable to, and suitable for medical image segmentation.

This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.

The paper describes useful fuzzy logic techniques for medical image segmentation. Specific methods to be reviewed include fuzzy information granulation, fuzzy inference and fuzzy cluster identification. Fuzzy information granulation is introduced as a powerful scheme to find the thresholds to obtain the whole brain region in MR data. A fuzzy inference technique succeeds in segmenting the brain region into the left cerebral hemisphere, right cerebral hemisphere, cerebellum and brain stem. The fuzzy inference aided segmentation procedure is also useful for human foot CT images. Fuzzy cluster identification is adapted to determine the obtained clusters into blood vessels or other tissues in an MRA image.

This paper presents a robust automatic threshold finding method for the human brain MR image segmentation. The method is based on fuzzy information granulation shown by Zadeh (see Abstract of BISC Seminar, 1996). The human brain MR image consists of several parts; the gray matter, white matter, cerebrospinal fluid and so on. By treating their parts as the fuzzy granules in the gray level histogram of the image and developing a fuzzy matching technique, we can find the required thresholds and can segment the brain region from the MR image. An experiment is done on 50 gray level histograms of the human brain MR volumes. To evaluate our method, we extract the brain region using the obtained thresholds. A comparison of the obtained region with canonical atlas images shows that our method find the thresholds of the gray matter and white matter correctly.