Colonoscopy video generation delivers dynamic, information-rich data critical for diagnosing intestinal diseases, particularly in data-scarce scenarios. High-quality video generation demands temporal consistency and precise control over clinical attributes, but faces challenges from irregular intestinal structures, diverse disease representations, and various imaging modalities. To this end, we propose ColoDiff, a diffusion-based framework that generates dynamic-consistent and content-aware colonoscopy videos, aiming to alleviate data shortage and assist clinical analysis. At the inter-frame level, our TimeStream module decouples temporal dependency from video sequences through a cross-frame tokenization mechanism, enabling intricate dynamic modeling despite irregular intestinal structures. At the intra-frame level, our Content-Aware module incorporates noise-injected embeddings and learnable prototypes to realize precise control over clinical attributes, breaking through the coarse guidance of diffusion models. Additionally, ColoDiff employs a non-Markovian sampling strategy that cuts steps by over 90% for real-time generation. ColoDiff is evaluated across three public datasets and one hospital database, based on both generation metrics and downstream tasks including disease diagnosis, modality discrimination, bowel preparation scoring, and lesion segmentation. Extensive experiments show ColoDiff generates videos with smooth transitions and rich dynamics. ColoDiff also produces customized contents tailored for diverse tasks, e.g., colitis, polyps, and adenomas for diagnosis. Incorporating synthetic videos into training promotes discriminative representation learning and improves diagnosis accuracy by 7.1%. ColoDiff presents an effort in controllable colonoscopy video generation, revealing the potential of synthetic videos in complementing authentic representation and mitigating data scarcity in clinical settings.

Congestive heart failure (CHF) is a chronic heart condition with high morbidity and mortality, manifesting as persistent abnormal rhythms and electrophysiological disturbances across multiple cardiac regions. Early and accurate detection of CHF using electrocardiograms (ECGs) is essential for clinical management. However, existing algorithms, typically tailored for short-term single-lead recordings, fail to capture multi-scale and multi-view cardiac abnormalities. Additionally, the pronounced class imbalance, with normal samples predominating, substantially compromises the sensitivity of mediocre models to CHF cases. To address these challenges, this article proposes a novel dual-view class-specific broad aggregation network (DCBA-Net) capable of extracting and integrating multi-scale temporal dynamics from long-term ECGs of limb lead II and chest lead V1. Specifically, an ECGNeXt architecture with multi-kernel depthwise convolutions (DWConvs) and channel attention mechanisms (CAMs) as the backbone is first constructed to extract both local and global disease-related features from the two ECG leads. Subsequently, in the information integration stage, a new objective function is designed to enhance interlead interactions by simultaneously enforcing view discrepancy and target consistency constraints. Furthermore, this function assigns class-specific coefficients to elevate the prominence of CHF cases, thereby alleviating the class imbalance problem. Finally, DCBA-Net aggregates consensus and complementary decisions on both leads for improved CHF detection. Experimental results on two publicly available databases show that DCBA-Net achieves 100% across all metrics under the intrapatient paradigm, with an accuracy of 99.4%, an$F1$-score of 98.98% and a G-mean of 99.1% under the interpatient paradigm, outperforming advanced results and demonstrating its immense potential as an auxiliary CHF diagnostic tool.

Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal disease diagnosis is crucial for assessing lesion evolution and determining treatment plans. However, locality difference and disease progression lead to intra-class disparities and inter-class similarities for colorectal lesion representation. In addition, interpretable algorithms explaining the lesion progression are still lacking, making the prediction process a "black box". In this paper, we propose IPNet, a dual-branch interpretable network with progressive loss for whole-stage colorectal disease diagnosis. The dual-branch architecture captures unbiased features representing diverse localities to suppress intra-class variation. The progressive loss function considers inter-class relationship, using prior knowledge of disease evolution to guide classification. Furthermore, a novel Grain-CAM is designed to interpret IPNet by visualizing pixel-wise attention maps from shallow to deep layers, providing regions semantically related to IPNet's progressive classification. We conducted whole-stage diagnosis on two image modalities, i.e., colorectal lesion classification on 129,893 endoscopic optical images and rectal tumor T-staging on 11,072 endoscopic ultrasound images. IPNet is shown to surpass other state-of-the-art algorithms, accordingly achieving an accuracy of 93.15% and 89.62%. Especially, it establishes effective decision boundaries for challenges like polyp vs. adenoma and T2 vs. T3. The results demonstrate an explainable attempt for colorectal lesion classification at a whole-stage level, and rectal tumor T-staging by endoscopic ultrasound is also unprecedentedly explored. IPNet is expected to be further applied, assisting physicians in whole-stage disease diagnosis and enhancing diagnostic interpretability.

The three-dimensional (3D) reconstruction of the coronary artery from angiographic images is crucial for diagnosing and treating coronary artery disease. However, accurate reconstruction is challenging due to the non-simultaneous acquisition of angiographic images and the complex motion patterns of coronary arteries. State-of-the-art methods typically involve a two-stage process: manual selection of corresponding point pairs for spatial geometric calibration, followed by centerline reconstruction. However, overlap and foreshortening in 2D images complicate point selection, often requiring repeated adjustments, and the lack of sufficient point pairs can lead to reconstruction failure. In this paper, we propose a one-stage automatic approach that integrates calibration and 3D centerline reconstruction, eliminating the need for manual calibration. For each angiographic image, we constructed a 3D deformable curve corresponding to the 2D vessel centerline, strictly constrained by the projection lines. Unlike traditional methods that minimize 2D reprojection errors, our approach minimizes the 3D spatial distance between two 3D curves, simultaneously optimizing the spatial transformation and the two deformable 3D curves. The transformation is optimized through iterative curves registration, while the curves are evolved based on a cosine representation method. Both processes occur simultaneously and mutually reinforce each other, resulting in high-precision 3D reconstruction without manual calibration. The proposed approach was validated on 45 phantom and 107 clinical data. The mean space error was $0.085~\pm ~0.085$ mm for phantom data; and the mean reprojection error was $0.060~\pm ~0.027$ mm for clinical data. Results demonstrated that our approach achieves state-of-the-art accuracy while eliminating the need for manual intervention.

Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images based on deep learning has made great progress, fully supervised models require a great amount of annotations, making such complex medical image segmentation a difficult problem. In this article, we propose a semi-supervised model for complex medical image segmentation, which innovatively proposes a bidirectional self-training paradigm, through dynamically exchanging the roles of teacher and student by estimating the reliability at the model level. The direction of information and knowledge transfer between the two networks can be controlled, and the probability distribution of the roles of teacher and student in the next stage will be jointly determined by the model's uncertainty and instability in the training process. We also resolve the problem that loosely coupled networks are prone to collapse when training on small-scale annotated data by proposing asymmetric supervision (AS) strategy and hierarchical dual student (HDS) structure. In particular, a bidirectional distillation loss combined with the role exchange (RE) strategy and a global-local-aware consistency loss are introduced to obtain stable mutual promotion and achieve matching of global and local features, respectively. We conduct detailed experiments on two public datasets and one private dataset and lead existing semi-supervised methods by a large margin, while achieving fully supervised performance at a labeling cost of 5%.

Spinal X-ray effectively visualizes the overall spinal situation and vertebral details. However, X-ray is unsuitable for long-term follow-up or frequent monitoring due to its radiation hazard. Motivated by this, we propose a model named dual-decomposition radiograph synthesizer (DDRS) to predict the X-ray image of the present moment, given the previous X-ray image and a pair of three-dimensional spinal ultrasound data, for a practical and radiation-free evaluation of spinal deformity in follow-up or monitoring. The DDRS used a novel dual-decomposition strategy to ensure the quality of synthesized images. First, the DDRS innovatively converted the X-ray image synthesis into a fusion between the spinal pose and anatomical information. A parallel architecture was used to extract and aggregate the two information from ultrasound and X-ray images. Second, an intermodality calibration module and a global-local cooperated feature extractor are further introduced to implement our synthesis strategy effectively. The intermodality calibration module provides an accurate spinal pose description by correcting a potential pose difference during two image acquisition times. The global-local cooperated feature extractor contributes to preserving spinal anatomical information in the previous X-ray image by exploring global dependencies and highlighting local details. Extensive experiments were conducted on a real clinical dataset. Results show that a mean structural similarity (SSIM) of 0.89 was obtained between synthesized X-ray images provided by the DDRS and real ones, and further comparisons with existing outstanding image synthesizers also display a 17.1% improvement in mean SSIM, illustrating the potential of our synthesizer in a radiation-free follow-up of scoliosis.

Multimodal analysis of placental ultrasound (US) and microflow imaging (MFI) could greatly aid in the early diagnosis and interventional treatment of placental insufficiency (PI), ensuring a normal pregnancy. Existing multimodal analysis methods have weaknesses in multimodal feature representation and modal knowledge definitions and fail on incomplete datasets with unpaired multimodal samples. To address these challenges and efficiently leverage the incomplete multimodal dataset for accurate PI diagnosis, we propose a novel graph-based manifold regularization learning (MRL) framework named GMRLNet. It takes US and MFI images as input and exploits their modality-shared and modality-specific information for optimal multimodal feature representation. Specifically, a graph convolutional-based shared and specific transfer network (GSSTN) is designed to explore intra-modal feature associations, thus decoupling each modal input into interpretable shared and specific spaces. For unimodal knowledge definitions, graph-based manifold knowledge is introduced to describe the sample-level feature representation, local inter-sample relations, and global data distribution of each modality. Then, an MRL paradigm is designed for inter-modal manifold knowledge transfer to obtain effective cross-modal feature representations. Furthermore, MRL transfers the knowledge between both paired and unpaired data for robust learning on incomplete datasets. Experiments were conducted on two clinical datasets to validate the PI classification performance and generalization of GMRLNet. State-of-the-art comparisons show the higher accuracy of GMRLNet on incomplete datasets. Our method achieves 0.913 AUC and 0.904 balanced accuracy (bACC) for paired US and MFI images, as well as 0.906 AUC and 0.888 bACC for unimodal US images, illustrating its application potential in PI CAD systems.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ultrasound (US), which are two common modalities for clinical breast tumor diagnosis besides Mammograms, can provide different and complementary information for the same tumor regions. Although many machine learning methods have been proposed for breast tumor classification based on either single modality, it remains unclear how to further boost the classification performance by utilizing paired multi-modality information with different dimensions. In this paper, we propose MRI-US multi-modality network (MUM-Net) to classify breast tumor into different subtypes based on 3D MR and 2D US images. The key insight of MUM-Net is that we explicitly distill modality-agnostic features for tumor classification. Specifically, we first adopt a discrimination-adaption module to decompose features into modality-agnostic and modality-specific ones with min-max training strategies. Then, we propose a feature fusion module to increase the compactness of the modality-agnostic features by utilizing an affinity matrix with nearest neighbour selection. We build a paired MRI-US breast tumor classification dataset containing 502 cases with three clinical indicators to validate the proposed method. In three tasks including lymph node metastasis, histological grade and Ki-67 level, MUM-Net achieves AUC scores of 0.8581, 0.8965 and 0.8577, outperforming other counterparts which are based on single task or single modality by a wide margin. In addition, we find that the extracted modality-agnostic features can help the network focus on the tumor regions in both modalities.

3D coronary artery reconstruction (3D-CAR) in intravascular ultrasound (IVUS) sequences allows quantitative analyses of vessel properties. Existing methods treat two main tasks of the 3D-CAR separately, including the cardiac phase retrieval (CPR) and the membrane border extraction (MBE). They ignore the CPR-MBE connection that could achieve mutual promotions to both tasks. In this paper, we pioneer to achieve one-step 3D-CAR via a collaborative constraint generative adversarial network (GAN) named the AwCPM-Net. The AwCPM-Net consists of a dual-task collaborative generator and a dual-task constraint discriminator. The generator combines a self-supervised CPR branch with a semi-supervised MBE branch via a warming-up connection. The discriminator promotes dual-branch predictions simultaneously. The CPR branch requires no annotations and outputs inter-frame deformation fields used for identifying cardiac phases. Deformation fields are additionally constrained by the MBE branch and the discriminator. The MBE branch predicts membrane boundaries for each frame. Two aspects assist the semi-supervised segmentation: annotation augmentation by deformation fields of the CPR branch; information exploitation on unlabeled images enabled by GAN design. Trained and tested on an IVUS dataset acquired from atherosclerosis patients, the AwCPM-Net is effective in both CPR and MBE tasks, superior to state-of-the-art IVUS CPR or MBE methods. Hence, the AwCPM-Net reconstructs reliable 3D artery anatomy in the IVUS modality.

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods. Code is available at https://github.com/tao-aimi/DeepRecursiveEmbedding.

Thin-section magnetic resonance imaging (MRI) can provide higher resolution anatomical structures and more precise clinical information than thick-section images. However, thin-section MRI is not always available due to the imaging cost issue. In multicenter retrospective studies, a large number of data are often in thick-section manner with different section thickness. The lack of thin-section data and the difference in section thickness bring considerable difficulties in the study based on the image big data. In this article, we introduce DeepVolume, a two-step deep learning architecture to address the challenge of accurate thin-section MR image reconstruction. The first stage is the brain structure-aware network, in which the thick-section MR images in axial and sagittal planes are fused by a multitask 3-D U-net with prior knowledge of brain volume segmentation, which encourages the reconstruction result to have correct brain structure. The second stage is the spatial connection-aware network, in which the preliminary reconstruction results are adjusted slice-by-slice by a recurrent convolutional network embedding convolutional long short-term memory (LSTM) block, which enhances the precision of the reconstruction by utilizing the previously unassessed sagittal information. We used 305 paired brain MRI samples with thickness of 1.0 mm and 6.5 mm in this article. Extensive experiments illustrate that DeepVolume can produce the state-of-the-art reconstruction results by embedding more anatomical knowledge. Furthermore, considering DeepVolume as an intermediate step, the practical and clinical value of our method is validated by applying the brain volume estimation and voxel-based morphometry. The results show that DeepVolume can provide much more reliable brain volume estimation in the normalized space based on the thick-section MR images compared with the traditional solutions.

Recently, the use of portable equipment has attracted much attention in the medical ultrasound field. Handheld ultrasound devices have great potential for improving the convenience of diagnosis, but noise-induced artifacts and low resolution limit their application. To enhance the video quality of handheld ultrasound devices, we propose a low-rank representation multipathway generative adversarial network (LRR MPGAN) with a cascade training strategy. This method can directly generate sequential, high-quality ultrasound video with clear tissue structures and details. In the cascade training process, the network is first trained with plane wave (PW) single-/multiangle video pairs to capture dynamic information and then fine-tuned with handheld/high-end image pairs to extract high-quality single-frame information. In the proposed GAN structure, a multipathway generator is applied to implement the cascade training strategy, which can simultaneously extract dynamic information and synthesize multiframe features. The LRR decomposition channel approach guarantees the fine reconstruction of both global features and local details. In addition, a novel ultrasound loss is added to the conventional mean square error (MSE) loss to acquire ultrasound-specific perceptual features. A comprehensive evaluation is conducted in the experiments, and the results confirm that the proposed method can effectively reconstruct high-quality ultrasound videos for handheld devices. With the aid of the proposed method, handheld ultrasound devices can be used to obtain convincing and convenient diagnoses.

In the medical ultrasound field, ultrafast imaging has recently become a hot topic. However, the diagnostic reliability of ultrafast high-frame rate plane-wave (PW) imaging is reduced by its low-quality images. The medical ultrasound equipment on the market usually adopts the line-scanning mode, which can obtain high-quality images at a very low frame rate. In addition, many proven data-driven ultrasound image processing methods are trained by line-scan images. Since the gray-level distributions of line-scan images and PW images are very different, these gray-level distribution-sensitive methods cannot be generalized to ultrafast ultrasound imaging, which limits further applications. Hence, we propose an ultrasound-transfer generative adversarial network to improve the quality of PW images and extend the existing image processing methods to ultrafast ultrasound imaging by reconstructing PW images into line-scan images. This network adopts a residual dense generator with a self-attention system that fully uses the hierarchical features and generates details from all the relevant physiological information. A projection discriminator and spectral normalization are introduced to increase the discernibility and to maintain a balance between the generator and the discriminator. Moreover, we reorganize the transmit sequence of the transducer array to eliminate the negative influence of human movements and facilitate the convergence of the proposed model. The experimental results are evaluated with five metrics, which confirm the feasibility of the proposed method to obtain a line-scan-quality image with a very high frame rate. This technology could significantly popularize ultrafast medical ultrasound imaging.

Supervised convolutional neural networks (CNNs) have demonstrated state-of-art performance in medical image segmentation tasks. However, the performance of a well-trained CNN on an independent dataset (e.g., different vendors, sequences) relies strongly on the distribution similarity, and may drop unexpectedly in case of distribution shift. To obtain a large amount of annotation from each new dataset for re-training the CNN is expensive and impractical. Adaptation algorithms to improve the CNN generalizability from source domain to target domain has significant practical value. In this work, we propose a highly efficient end-to-end domain adaptation approach, with left ventricle segmentation from cine MRI sequences as an example. We propose to perform domain adaptation in the output space where different domains share the strongest similarities. The core of this algorithm is a flexible and light output adaption module based on adversarial learning. Moreover, Canny edge detector is introduced to enhance model's attention to edges during adversarial learning. Comparative experiments were carried out using images from three major MR vendors (Philips, Siemens, and GE) as three domains. Our results demonstrated that the proposed method substantially improved the generalization of the trained CNN model from one vendor to other vendors without any additional annotation. Moreover, the ablation study proved that introducing Canny edge detector further refined the edge detection in segmentation. The proposed adaption is generic can be extended to other medical image segmentation problems.

In magnetic resonance imaging (MRI), different imaging settings lead to various intensity distributions for a specific imaging object, which brings huge diversity to data-driven medical applications. To standardize the intensity distribution of magnetic resonance (MR) images from multiple centers and multiple machines using one model, a cycle generative adversarial network (CycleGAN)-based framework is proposed. It utilizes a unified forward generative adversarial network (GAN) path and multiple independent backward GAN paths to transform images in different groups into a single reference one. To preserve image details and prevent resolution loss, two jump connections are applied in the CycleGAN generators. A weak-pair strategy is designed to fully utilize the prior knowledge of the organ structure and promote the performance of the GANs. The experiments were conducted on a T2-FLAIR image database with 8192 slices from 489 patients. The database was obtained from four hospitals and five MRI scanners and was divided into nine groups with different imaging parameters. Compared with the representative algorithms, the peak signal-to-noise ratio, the histogram correlation, and the structural similarity were increased by 3.7%, 5.1%, and 0.1% on average, respectively; the gradient magnitude similarity deviation, the mean square error, and the average disparity were reduced by 19.0%, 15.7%, and 9.9% on average, respectively. Experiments also showed the robustness of the proposed model with a different training set configuration and effectiveness of the proposed framework over the original CycleGAN. Therefore, the MR images with different imaging settings could be efficiently standardized by the proposed method, which would benefit various data-driven applications.

Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors. First, we developed a dictionary learning- and sparse representation-based feature extraction method that exploits the statistical characteristics of the lesion area, leading to fine and more effective feature extraction compared with the traditional explicitly calculation-based methods. Then, we set up an iterative sparse representation method to solve the redundancy problem of the extracted features. Finally, we proposed a novel multi-feature collaborative sparse representation classification framework that introduces a new coefficient of regularization term to combine features from multi-modal images at the sparse representation coefficient level. Two clinical problems were used to validate the performance and usefulness of the proposed SRR system. One was the differential diagnosis between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), and the other was isocitrate dehydrogenase 1 estimation for gliomas. The SRR system had superior PCNSL and GBM differentiation performance compared with some advanced imaging techniques and yielded 11% better performance for estimating IDH1 compared with the traditional radiomics methods.

BACKGROUND: Image registration is an important research topic in the field of image processing. Applying image registration to vascular image allows multiple images to be strengthened and fused, which has practical value in disease detection, clinical assisted therapy, etc. However, it is hard to register vascular structures with high noise and large difference in an efficient and effective method. RESULTS: Different from common image registration methods based on area or features, which were sensitive to distortion and uncertainty in vascular structure, we proposed a novel registration method based on network structure and circuit simulation. Vessel images were transformed to graph networks and segmented to branches to reduce the calculation complexity. Weighted graph networks were then converted to circuits, in which node voltages of the circuit reflecting the vessel structures were used for node registration. The experiments in the two-dimensional and three-dimensional simulation and clinical image sets showed the success of our proposed method in registration. CONCLUSIONS: The proposed vascular image registration method based on network structure and circuit simulation is stable, fault tolerant and efficient, which is a useful complement to the current mainstream image registration methods.

Segmentation of pheochromocytomas in contrast-enhanced computed tomography (CECT) images is an ill-posed problem due to the presence of weak boundaries, intratumoral degeneration, and nearby structures and clutter. Additional information from different phases of CECT images needs to be imposed for better mass segmentations. In this paper, a novel adaptive cosegmentation method is proposed by incorporating a localized region-based level set model (LRLSM). The energy function is formulated with consideration of adaptive tradeoff between the complementary local information from image pairs. Gradient direction and shape dissimilarity measure are integrated to guide the level set evolution. Automatic localization radius selection is added to further facilitate the segmentation. Then, two level set functions from each image pair are evolved and refined alternately to minimize the energy function. Experimental results in 50 CECT image pairs show that the adaptive LRLSM-based method is effective in segmentation of pheochromocytoma at two phases and produces better results, especially in the cases with weak boundaries, and complex foreground and background.

Identification of intracardiac masses in echocardiograms is one important task in cardiac disease diagnosis. To improve diagnosis accuracy, a novel fully automatic classification method based on the sparse representation is proposed to distinguish intracardiac tumor and thrombi in echocardiography. First, a region of interest is cropped to define the mass area. Then, a unique globally denoising method is employed to remove the speckle and preserve the anatomical structure. Subsequently, the contour of the mass and its connected atrial wall are described by the K-singular value decomposition and a modified active contour model. Finally, the motion, the boundary as well as the texture features are processed by a sparse representation classifier to distinguish two masses. Ninety-seven clinical echocardiogram sequences are collected to assess the effectiveness. Compared with other state-of-the-art classifiers, our proposed method demonstrates the best performance by achieving an accuracy of 96.91%, a sensitivity of 100%, and a specificity of 93.02%. It explicates that our method is capable of classifying intracardiac tumors and thrombi in echocardiography, potentially to assist the cardiologists in the clinical practice.

Ultrasonography has been widely used to evaluate duodenogastric reflux (DGR). But to the best of our knowledge, no automatic analysis system was developed to realize the quantitative computer-aided analysis. In this paper, we propose a system to perform the automatic detection of DGR in the ultrasonic image sequences by applying the automatic motion analysis. The motion field is estimated based on image velocimetry. Then, an intelligent motion analysis is applied. For the DGR detection, the motion and structural information is combined to analyze the transploric motion of the fluid. In order to test the performance of the proposed system, we designed the experiment with the real and synthetic ultrasonic data. The proposed system achieved a good performance in the DGR detection. The automatic results were accordant with the gold standard in analyzing the fluid motion. The proposed system is supposed to be a promising tool for the study and evaluation of DGR.

Tracking pylorus in ultrasonic image sequences is an important step in the analysis of duodenogastric reflux (DGR). We propose a joint prediction and segmentation method (JPS) which combines optical flow with active contour to track pylorus. The goal of the proposed method is to improve the pyloric tracking accuracy by taking account of not only the connection information among edge points but also the spatio-temporal information among consecutive frames. The proposed method is compared with other four tracking methods by using both synthetic and real ultrasonic image sequences. Several numerical indexes: Hausdorff distance (HD), average distance (AD), mean edge distance (MED), and edge curvature (EC) have been calculated to evaluate the performance of each method. JPS achieves the minimum distance metrics (HD, AD, and MED) and a smaller EC. The experimental results indicate that JPS gives a better tracking performance than others by the best agreement with the gold curves while keeping the smoothness of the result.

Topological properties have important function in human beings visual attention. TPQFT (Topological properties based Phase spectrum of Quaternion Fourier Transform) model is an attention selection model using topological properties expression we have introduced. A new quantification criterion to evaluate every channel's contribution and model's performance is proposed in this paper and used in TPQFT. This paper improves TPQFT model in several aspects and WTPQFT (Weight Adjusted Topological properties based Phase spectrum of Quaternion Fourier Transform) model is introduced. The experimental results show that WTPQFT model reflects the real attention selection more accurately than TPQFT and PQFT (Phase spectrum of Quaternion Fourier Transform) method.

The nuchal translucency (NT) thickness is an important parameter in the diagnosis of fetuses. The previous computerized methods often require manual operations to select the NT region, which leads to the time-consuming problem and the detection variability. In the paper, a hierarchical structural model is proposed for the automated detection of the NT region. Three discriminative classifiers are first trained with Gaussian pyramids to represent the NT, head and body of fetuses respectively. Then a spatial model is proposed to denote the spatial constrains among them. Finally the dynamic programming and generalized distance transform are applied for the inference from the proposed model, which ensures the optimal solution can be obtained for the NT detection. The performance of the proposed model is verified by the experimental results of 345 clinical NT ultrasound images.

A gradient vector flow (GVF) snake based method was proposed for pedicle segmentation in vertebral radiographs. Since pedicles were oval-shaped, the elliptical shape prior was used to constrain the evolution of the GVF snake. From segmented pedicles, some landmarks were automatically identified for 3D stereoradiographic reconstruction of vertebrae to reduce the observer variability. Ten radiographs including 260 pedicles were used to evaluate the segmentations. Results demonstrated that the distance between contours manually delineated by the user and those segmented by the proposed algorithm was far less than the distance resulted from the traditional GVF snake. The 3D reconstruction variance was reduced by using the landmarks obtained from the segmented pedicles. These results indicated that utilizing the elliptical shape prior improved the GVF snake for segmenting pedicles in radiographs, and the proposed method might be a useful preprocessing tool for 3D stereoradiographic reconstruction.

Endothelial permeability is associated with the genesis and development of atherosclerosis. Computerized image analysis is utilized to investigate the relationship between endothelial permeability and endothelial morphology. First, microscopic images are segmented to detect endothelial cells using the speckle reduction anisotropic diffusion and marker-controlled watershed, whose optimal parameter settings are obtained from the cell detection receiver operating characteristic. Two categories of morphological features are then extracted, including cell shape features and intercellular features. Finally, Student's t-test is conducted to explore the relation of the morphology to permeability. The method correctly detected 82.3% cells in two test images, while the over-segmented and fused cells were 8.4% and 9.3%, respectively. T-tests using images from two porcine coronary arteries demonstrated that four features had significant difference (P<0.05) between regions with highest (top 25%) and lowest (bottom 25%) albumin permeability. This finding is helpful in exploring the mechanisms responsible for high permeability.

The goal of feature selection is to search the optimal feature subset with respect to the evaluation function. Exhaustively searching all possible feature subsets requires high computational cost. The alternative suboptimal methods are more efficient and practical but they cannot promise globally optimal results. We propose a new feature selection algorithm based on distance discriminant and distribution overlapping (HFSDD) for continuous features, which overcomes the drawbacks of the exhaustive search approaches and those of the suboptimal methods. The proposed method is able to find the optimal feature subset without exhaustive search or Branch and Bound algorithm. The most difficult problem for optimal feature selection, the search problem, is converted into a feature ranking problem following rigorous theoretical proof such that the computational complexity can be greatly reduced. Since the distribution of overlapping degrees between every two classes can provide useful information for feature selection, HFSDD also takes them into account by using a new approach to estimate the overlapping degrees. In this sense, HFSDD is a distance discriminant and distribution overlapping based solution. HFSDD was compared with ReliefF and mrmrMID on ten data sets. The experimental results show that HFSDD outperforms the other methods.

Accurate estimation of fetal weight before delivery is of great benefit to limit the potential complication associated with the low-birth-weight infants. Although the regression analysis has been used as a daily clinical means to estimate the fetal weight on the basis of ultrasound measurements, it still lacks enough accuracy for low-birth-weight fetuses. The ineffectiveness is mainly due to the large inter- or intraobserver variability in measurements and the inappropriateness of the regression analysis. A novel method based on the support vector regression (SVR) is proposed to improve the weight estimation accuracy for fetuses of less than 2500 g. Here, fuzzy logic is introduced into SVR (termed FSVR) to limit the contribution of inaccurate training data to the model establishment, and thus, to enhance the robustness of FSVR to noisy data. To guarantee the generalization performance of the FSVR model, the nondominated sorting genetic algorithm (NSGA) is utilized to obtain the optimal parameters for the FSVR, which is referred to as the evolutionary fuzzy support vector regression (EFSVR) model. Compared with regression formulas, back-propagation neural network, and SVR, EFSVR achieves the lowest mean absolute percent error (6.6%) and the highest correlation coefficient (0.902) between the estimated fetal weight and the actual birth weight. The EFSVR model produces significant improvement (1.9%-4.2%) on the accuracy of fetal weight estimation over several widely used formulas. Experiments show the potential of EFSVR in clinical prenatal care.

A computerized classification based on morphologic and texture features is proposed to increase the accuracy of the ultrasonic diagnosis of breast tumors. Firstly, tumor boundaries are obtained with the gray-level threshold segmentation algorithm and the dynamic programming method. Then five morphologic features and two texture features are extracted. Finally, an artificial neural network with the error back propagation algorithm is applied to classify breast tumors as benign or malignant. Experiments on 168 cases show that the proposed system yields the high accuracy, sensitivity and specificity. Therefore, it is concluded that this system performs well in the ultrasonic classification of breast tumors.

Measurement of visual quality is of fundamental importance to some image processing applications. And the perceived image distortion of any image strongly depends on the local features, such as edges, flats and textures. Since edges often convey much information of an image, we propose a novel algorithm for image quality assessment based on the edge and contrast similarity between the distorted image and the reference(perfect) image. We demonstrate its promise through a set of intuitive examples, as well as validate its performance with subjective ratings. We also compare our method with two other state-of-the-art objective ones, which uses 550 images with different distortion types and BP neural network.

In this paper, we construct a novel HNN energy function, and use the HNN with this energy function to solve anti-aircraft missile deployment, which is a constrained layout problem. A near-optimum solution is obtained when HNN reaches a stable state, i.e, the minimum of the energy function is reached. We have studied the convergence of the network and the relationship between parameters of the network and stability. A group of suitable parameters are obtained by lots of experiences. The simulation results in different scales show that our approach can obtain the near-optimum solution. In addition, our approach can get better solutions in larger scales than other methods such as the divide-and-conquer algorithm which is often used to solve constrained layout problems, and it can also be extended to other constrained layout problems, such as mobile base station planning and integrated circuit layout design.

Hemodynamic parameters of the pulmonary circulation are important reference factors in the diagnoses and treatments of children 's cardiovascular diseases. These parameters are usually measured by the cardiac catheterization. Recently several noninvasive methods have been proposed to estimate these parameters using the ultrasound technique. In order to execute the noninvasive estimation automatically, several signal and image processing algorithms are applied to obtain the feature points of the Doppler spectrogram and ECG signal, from which hemodynamic parameters can be calculated based on empirical formulas. To combine all the processes and simplify the manipulation, a computer-based system is designed and tested for sixty children with congenital heart diseases. Experimental results show that parameters estimated using this system highly correlate to the clinic ones measured by the cardiac catheterization. For its noninvasive and automatic advantage, this system may be used as an assistant tool in the diagnoses and treatments of children's cardiovascular diseases

The low frequency and high amplitude clutter signal constitutes a great disturbance for the discrimination of low velocity blood flow signals in a Doppler ultrasound measurement system. In this letter, a novel method for clutter rejection is proposed, incorporating irregular sampling and iterative reconstruction by cubic spline. Experimental results indicate this approach outperforms the traditional way of high-pass filtering with its effective elimination of clutter and preservation of low velocity blood component in the overlapped frequency region.

A modified optimization-based contour detection method was presented to compute the lumen area of the coronary artery from intravascular ultrasound (IVUS) video images. First, the search range for the artery inner wall was determined based on the continuity of IVUS video frames. Next, the internal and external energy were calculated to describe the smoothness of the arterial wall and the grayscale variation of ultrasound images, respectively. Here, a novel form of the external energy which combines the gradient and variance of the intensity of image in the radial direction was used. Finally, the minimal energy path based on the optimum contour of the artery wall was obtained using circular dynamic programming (DP). By the comparison with the typical DP procedure using the traditional external energy form, based only on the image gradient, the reliability of this modified method is considerably improved in the measurement of coronary artery lumen area.