PURPOSE: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting. The accuracy when using Wi-Fi or BLE may be affected by interference or multipath propagation. In this research, we evaluated the positioning accuracy of geomagnetic indoor positioning in hospitals. MATERIALS AND METHODS: We compared the position measurement accuracy of a geomagnetic method alone, Wi-Fi alone, BLE beacons alone, geomagnetic plus Wi-Fi, and geomagnetic plus BLE in a general inpatient ward, using a geomagnetic positioning algorithm by GiPStech. The existing Wi-Fi infrastructure was used, and 20 additional BLE beacons were installed. Our first experiment compared these methods' accuracy for 8 test routes, while the second experiment verified a combined geomagnetic/BLE beacon method using 3 routes based on actual daily activities. RESULTS: The experimental results demonstrated that the most accurate method was geomagnetic/BLE, followed by geomagnetic/Wi-Fi, and then geomagnetic alone. DISCUSSION: The geomagnetic method's positioning accuracy varied widely, but combining it with BLE beacons reduced the average position error to approximately 1.2 m, and the positioning accuracy could be improved further. We believe this could effectively target humans (patients) where errors of up to 3 m can generally be tolerated. CONCLUSION: In conjunction with BLE beacons, geomagnetic positioning could be sufficiently effective for many in-hospital localization tasks.

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid on brain CT images, multi-organ segmentation on abdominal CT images, and multi-class brain tumor segmentation on MR images.

Renal artery segmentation remained a big challenging due to its low contrast. In this paper, we present a novel graph-cut method using tensor-based distance metric for blood vessel segmentation in scale-valued images. Conventional graph-cut methods only use intensity information, which may result in failing in segmentation of small blood vessels. To overcome this drawback, this paper introduces local geometric structure information represented as tensors to find a better solution than conventional graph-cut. A Riemannian metric is utilized to calculate tensors statistics. These statistics are used in a Gaussian Mixture Model to estimate the probability distribution of the foreground and background regions. The experimental results showed that the proposed graph-cut method can segment about $$80\,\%$$ of renal arteries with 1mm precision in diameter.

An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.

The paper proposes a diversity-enhanced condensation algorithm to address the particle impoverishment problem which stochastic filtering usually suffers from. The particle diversity plays an important role as it affects the performance of filtering. Although the condensation algorithm is widely used in computer vision, it easily gets trapped in local minima due to the particle degeneracy. We introduce a modified evolutionary computing method, adaptive differential evolution, to resolve the particle impoverishment under a proper size of particle population. We apply our proposed method to endoscope tracking for estimating three-dimensional motion of the endoscopic camera. The experimental results demonstrate that our proposed method offers more robust and accurate tracking than previous methods. The current tracking smoothness and error were significantly reduced from (3.7, 4.8) to (2.3 mm, 3.2 mm), which approximates the clinical requirement of 3.0 mm.

Endoscope 3-D motion tracking, which seeks to synchronize pre- and intra-operative images in endoscopic interventions, is usually performed as video-volume registration that optimizes the similarity between endoscopic video and pre-operative images. The tracking performance, in turn, depends significantly on whether a similarity measure can successfully characterize the difference between video sequences and volume rendering images driven by pre-operative images. The paper proposes a discriminative structural similarity measure, which uses the degradation of structural information and takes image correlation or structure, luminance, and contrast into consideration, to boost video-volume registration. By applying the proposed similarity measure to endoscope tracking, it was demonstrated to be more accurate and robust than several available similarity measures, e.g., local normalized cross correlation, normalized mutual information, modified mean square error, or normalized sum squared difference. Based on clinical data evaluation, the tracking error was reduced significantly from at least 14.6 mm to 4.5 mm. The processing time was accelerated more than 30 frames per second using graphics processing unit.

The paper presents a new endoscope motion tracking method that is based on a novel external endoscope tracking device and our modified stochastic optimization method for boosting endoscopy navigation. We designed a novel tracking prototype where a 2-D motion sensor was introduced to directly measure the insertion-retreat linear motion and also the rotation of the endoscope. With our developed stochastic optimization method, which embeds traceable particle swarm optimization in the Condensation algorithm, a full six degrees-of-freedom endoscope pose (position and orientation) can be recovered from 2-D motion sensor measurements. Experiments were performed on a dynamic bronchial phantom with maximal simulated respiratory motion around 24.0 mm. The experimental results demonstrate that our proposed method provides a promising endoscope motion tracking approach with more effective and robust performance than several current available tracking techniques. The average tracking accuracy of the position improved from 6.5 to 3.3 mm, which further approaches the clinical requirement of 2.0 mm in practice.

A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

We developed a novel multipoint measurement system capable of acquiring video and sound at more than 100 points in a "synchronized" manner. In this paper, we first describe the specification of the system and how the system works in detail. Then we report some experimental results that confirm the performance of the system. We also describe test data set we provided for MPEG (moving picture experts group) multi-viewpoint video coding activities. Using this system, we are planning to conduct projects to measure humans and their activities, collect a large volume of real-world data of video and sound, and release them to the public

Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and is typically the rate-limiting step in the execution time of intensity-based two-dimensional to three-dimensional (2D-3D) registration algorithms. We address this computational issue by extending the technique of light field rendering from the computer graphics community. The extension of light fields, which we call attenuation fields (AFs), allows most of the DRR computation to be performed in a preprocessing step; after this precomputation step, DRRs can be generated substantially faster than with conventional ray casting. We derive expressions for the physical sizes of the two planes of an AF necessary to generate DRRs for a given X-ray camera geometry and all possible object motion within a specified range. Because an AF is a ray-based data structure, it is substantially more memory efficient than a huge table of precomputed DRRs because it eliminates the redundancy of replicated rays. Nonetheless, an AF can require substantial memory, which we address by compressing it using vector quantization. We compare DRRs generated using AFs (AF-DRRs) to those generated using ray casting (RC-DRRs) for a typical C-arm geometry and computed tomography images of several anatomic regions. They are quantitatively very similar: the median peak signal-to-noise ratio of AF-DRRs versus RC-DRRs is greater than 43 dB in all cases. We perform intensity-based 2D-3D registration using AF-DRRs and RC-DRRs and evaluate registration accuracy using gold-standard clinical spine image data from four patients. The registration accuracy and robustness of the two methods is virtually identical whereas the execution speed using AF-DRRs is an order of magnitude faster.

This paper aims at obtaining diagnosis and prognosis information by searching similar images into a three-dimensional (3-D) CT image database of pulmonary nodules for which diagnosis is known. For this purpose, we propose an automatic method to retrieve nodule candidates with similar characteristics from the database. Each pulmonary nodule image is represented by the distribution pattern of CT density and 3-D curvature index. The nodule representation is then applied to a similarity measure such as a correlation coefficient. Our database is composed of 248 pulmonary nodules with associated clinical information. For each new case, we sort all the nodules of the database from most to less similar ones. By applying the retrieval method to our database, we present its feasibility to search the similar 3-D nodule images.

This paper reports a method for estimating human spine posture using the front and side views of a human body taken by a video camera. We present here a new 3D model to estimate the spine posture using connected vertebra spheres. In this model, each vertebra forming the spine is approximated as a sphere. The spine is approximated as a series of spheres connecting each other. Each sphere has the control points to represent the outer shape of the body. The spine posture is estimated by moving the sphere and the control points. The estimation is performed by calculating the matching ratio between a projected image of the model and an input image. X-ray CT slices are used for the construction of the model. We applied the proposed method to real human images. The experimental results show that our 3D model worked reasonably well for the estimation of human spine posture based on real human images.

This paper presents an expanded system for the online recognition of handwritten mathematical formulas. Our target handwritten mathematical formulas are strokes drawn on a data tablet. This system recognizes such strokes as components of mathematical formulas on the basis of their positions and combinations. Including matrix structures, general mathematical expressions are acceptable for this system. Each recognition result is acquired as a L/sup A/T/sub E/X source code. This system also has a preview function to enable a more highly intuitive recognition result. In recognition experiments, this system proved to be fairly feasible in handling handwritten mathematical formulas in real-time.

We are developing computerized feature extraction and classification methods to analyze malignant and benign pulmonary nodules in three-dimensional (3-D) thoracic CT images. Surrounding structure features were designed to characterize the relationships between nodules and their surrounding structures such as vessel, bronchi, and pleura. Internal structure features were derived from CT density and 3-D curvatures to characterize the inhomogeneous of CT density distribution inside the nodule. The stepwise linear discriminant classifier was used to select the best feature subset from multidimensional feature spaces. The discriminant scores output from the classifier were analyzed by the receiver operating characteristic (ROC) method and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 248 pulmonary nodules in this study. The internal structure features (Az=0.88) were more effective than the surrounding structure features (Az=0.69) in distinguishing malignant and benign nodules. The highest classification accuracy (Az=0.94) was obtained in the combined internal and surrounding structure feature space. The improvement was statistically significant in comparison to classification in either the internal structure or the surrounding structure feature space alone. The results of this study indicate the potential of using combined internal and surrounding structure features for computer-aided classification of pulmonary nodules.

The paper describes the extraction of pulmonary major and minor fissures from three-dimensional (3D) chest multidetector-row computed tomography (MDCT) images. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. We have proposed (see Kubo, M. et al, IEEE Trans. Nucl. Sci, vol.46, p.2128-33, 1999) an automatic fissures extraction method using thin-section CT images with much noise. The present study proposes a simpler algorithm to extract fissures using MDCT images with little noise. The new proposed algorithm consists of the highlight method using the VanderBrug operator and the extraction method using morphology filters. We applied the proposed algorithm to one patient. Our method could accurately extract fissures.

This paper presents an approach for characterizing the internal structure which is one of important clues for differentiating between malignant and benign nodules in three-dimensional (3-D) thoracic images. In this approach, each voxel was described in terms of shape index derived from curvatures on the voxel. The voxels inside the nodule were aggregated via a shape histogram to quantify how much shape category was present in the nodule. Topological features were introduced to characterize the morphology of the cluster constructed from a set of voxels with the same shape category. In the classification step, a hybrid unsupervised/supervised structure was performed to improve the classifier performance. It combined the k-means clustering procedure and the linear discriminate classifier. Receiver operating characteristics analysis was used to evaluate the accuracy of the classifiers. Our results demonstrate the feasibility of the hybrid classifier based on the topological and histogram features to assist physicians in making diagnostic decisions.

Presents a scheme to analyze nodule surrounding using differential geometry based vector fields in three-dimensional (3-D) thoracic images. In this scheme the differential characteristics such as the principal curvatures and directions are computed from the differential values of the isointensity surfaces. Each voxel in the nodule surrounding is described in terms of shape index and curvedness derived from the principal curvatures. Two vector fields are formed from the directions of the maximum principal curvatures of nodule surrounding and gradient vectors of nodule surface, respectively. The gradient vector field is computed by diffusing the gradient vector on the nodule surface. The regions corresponding to the cylindrical or conic figures which are similar to vessel and plural images are segmented by the shape index and curvedness values. Then, the relationship between the segmented regions and the nodule is evaluated by the inner product of the direction of the maximum principal curvature and the gradient vector. The authors demonstrate the effectiveness of their scheme by using real pulmonary nodule images.

This paper present an automatic extraction algorithm of the pulmonary major and minor fissures from three-dimensional (3-D) chest thin-section computed tomography (CT) images of helical CT. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. The proposed algorithm improves on the previous extraction method using the surface-curvatures calculation for density profile and morphological filters. The proposed method can extract the major and minor fissures in contact with the nodule and the chest walls. We apply the proposed algorithm to 12 patients. The results of our method are more accuracy to extract fissures around pulmonary lesions than by the previous method. The warped fissures extracted by our method show that lesions near fissures are malignant. Extracted fissures will aid in the diagnosis of lung cancer and in the analysis of automatic pulmonary conformation by using a computer.

This paper presents a computerized approach to characterize pulmonary nodules as benign or malignant based on contrast enhancement patterns extracted from serial three-dimensional (3-D) thoracic CT images. In this approach the registration procedure of sequential 3-D pulmonary images consisted of the rigid transformation between two sequential region-of-interest (ROI) images including the pulmonary nodule. The normalized mutual information was used as a voxel-based similarity measure in the registration. After motion correction between successive ROI images, the enhancement rate within a core of the segmented 3-D nodule image was estimated from the difference between the preand post-contrast images. We analyzed a data set of twelve 3-D thoracic CT images with pulmonary nodules in this study. Based on the Wilcoxon rank sum test, the median enhancement of the malignant lesions was significantly higher than that of the benign lesions (p<0.01). The preliminary results of the approach are very promising in characterizing pulmonary nodules based on quantitative measures of the contrast enhancement.

This paper focuses on an approach for characterizing the internal structure which is one of important clues for differentiating between malignant and benign nodules in 3D thoracic images. In this approach, each voxel was described in terms of shape index derived from curvatures on the voxel. The voxels inside the nodule were aggregated via shape histogram to quantify how much shape category was present in the nodule. Topological features were introduced to characterize the morphology of the cluster constructed from a set of voxels with the same shape category. In the classification step, a hybrid unsupervised/supervised structure was performed to improve the classifier performance. It combined the k-means clustering procedure and the linear discriminate classifier. The receiver operating characteristics analysis was used to evaluate the accuracy of the classifiers. Our results demonstrate the feasibility of the hybrid classifier based on the topological and histogram features to assist physicians in making diagnostic decisions.

The objective of the present paper is to extract the pulmonary major and minor fissures from 3D chest thin-section computed tomography (CT) images obtained by helical scan. These fissures are used for the diagnosis of lung cancer and the analysis of pulmonary conformation. We have proposed fissures extraction method without reference to streak artifacts and motion artifacts on the CT images. The new proposed algorithm improves on the previous extraction method using the surface-curvatures analysis for density profile and the morphological filters. The proposed method can also extract pulmonary fissures in contact with the module and the chest walls. We applied the proposed algorithm to 12 patients. The results of our method were more accuracy to extract fissures around pulmonary lesion than by the previous method. The warped fissures extracted by our method show that lesion near fissures is malignancy. Extracted fissures will be aided to diagnose lung cancer and to analyze automatically pulmonary conformation by using computer.

This paper presents a new system for the online and real-time recognition of handwritten mathematical formulas. Mathematical formulas are inputted into the system as hand drawings on a computer screen using a data tablet. The system analyzes the inputted data stroke by stroke in order to recognize the structure of the formula based on the relationship of bonding boxes that include components of the formula. A recognized result is obtained as a source code from the "LATEX" type serving system. Our system can recognize arbitrary combinations of superscripts, subscripts, square roots, overlines, underlines, and fractions. The input of the mathematical formula is independent of stroke order. In several experiments, the system proved to be very effective for inputting various mathematical formulas in real-time.

This paper describes a method for the automated anatomical labeling of the bronchial branch extracted from a three-dimensional (3-D) chest X-ray CT image and its application to a virtual bronchoscopy system (VBS). Automated anatomical labeling is necessary for implementing an advanced computer-aided diagnosis system of 3-D medical images. This method performs the anatomical labeling of the bronchial branch using the knowledge base of the bronchial branch name. The knowledge base holds information on the bronchial branch as a set of rules for its anatomical labeling. A bronchus region is automatically extracted from a given 3-D CT image. A tree structure representing the essential structure of the extracted bronchus is recognized from the bronchus region. Anatomical labeling is performed by comparing this tree structure of the bronchus with the knowledge base. As an application, we implemented the function to automatically present the anatomical names of the branches that are shown in the currently rendered image in real time on the VBS. The result showed that the method could segment about 57% of the branches from CT images and extracted a tree structure of about 91% in branches in the segmented bronchus. The anatomical labeling method could assign the correct branch name to about 93% of the branches in the extracted tree structure. Anatomical names were appropriately displayed in the endoscopic view.

Multi-scale curvature indexes are introduced to characterize the internal intensity structure of pulmonary nodules in thin-section CT images. This approach makes use of shape index, curvedness, and CT density to represent locally each voxel constructing the three-dimensional (3D) pulmonary nodule image. Using features extracted from the histogram of the multi-scale curvature indexes and CT density, the pulmonary nodules are discriminated between benign and malignant cases by the linear discriminant classifier. In this study a data set of 128 pulmonary nodules is analyzed to investigate which scale provides high classification accuracy between malignant and benign nodules. Additionally, the extracted features are evaluated for four different regions: (i) entire 3D pulmonary nodule; (ii) core region in the 3D pulmonary nodule; (iii) complement of the-core region in the 3D pulmonary nodule; (iv) neighborhood region surrounding the 3D pulmonary nodule. The effectiveness of the multi-scale curvature indexes in a computer-aided differential diagnosis is demonstrated by receiver operating characteristic (ROC) analysis.

In this paper we propose three strategies for analysis of solitary pulmonary nodules in contrast enhanced dynamic CT images. First we collect contrasted images with regale time interval, and we analyze their CT values with respect to the time variation. In the second, we compare the shape spectra characteristic of each contrasted image. The third strategy consists of subtracting the noncontrasted images from contrasted images. The results show that each starkly promise good differentiation between benign and malignant nodule. So we try differential diagnosis.

Presents a method to characterize the internal structure of small pulmonary nodules through a curvature-based descriptor using thin-section CT images. The work is a first step toward the segmentation of the three-dimensional (3D) nodule images by using a 3D deformable surfaces approach. Secondly, a curvature-based representation of the pulmonary nodule is derived. Based on this representation, the pulmonary nodules are globally characterized through the shape spectra. This quantification emphasizes the difference between benign and malignant pulmonary nodules surroundings. Experiments on true 3D nodule images demonstrate good performance of our curvature based analysis technique.

In this paper we present a procedure to extract bronchus area from 3D chest X-ray CT images. Extraction of bronchus from chest X-ray CT images is of critical importance for both the computer aided detection of lung cancer and the virtual bronchoscopy system. This procedure consists of three major steps: 1) calculation of a start point for region growing; 2) determination of the optimum threshold value; and 3) final extraction of bronchus area with the optimum threshold value. The proposed procedure extracts the bronchus area by following the inside points of bronchus. A 3D region growing method, called the 3D painting algorithm, is developed to perform this. The position of a starting point for the 3D painting algorithm is selected in the trachea automatically by using the shape features of trachea. Also this procedure can search the optimum threshold value by using the information of bronchus shape. We applied this method to real three dimensional X-ray CT images and confirmed that it worked satisfactorily.