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Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum the Posteriori (MAP)...
While many computer-aided detection (CADe) systems for CT colonography can detect polypoid lesions at a high sensitivity level, very few are targeted toward detecting flat lesions. Research has shown that flat lesions are more likely to contain carcinoma than polypoid lesions; therefore, it is imperative that they be adequately detected in screenings and examinations. However, current CADe systems...
Image-based morphometry of cells, tissues, and organs is an important topic in biomedical image analysis. We propose a novel method to characterize the morphological information that discriminates between two populations of morphological exemplars (cells, organs). We first demonstrate that the application of standard techniques such as Fisher linear discriminant analysis (FDA) can lead to undesirable...
We introduce a class of spectral shape signatures constructed from symmetric functions on the eigenfunctions of the Laplacian exponentially weighted by their eigenvalues. Such a construction is motivated by problems that arise in the use of the eigenfunctions for shape comparison, such as indeterminacies in the choice of signs and the particular ordering in which the eigenfunctions are presented....
The design and use of statistical pattern recognition models can be regarded as one of the core research topics in the segmentation of the left ventricle of the heart from ultrasound data. These models trade a strong prior model of the shape and appearance of the left ventricle for a statistical model whose parameters can be learned using a manually segmented data set (this set is commonly known as...
An automatically extracted 2d contour of a lymph node in a single CT slice is required for size assessment as well as for the initialization of model based 3d lymph node segmentation algorithms. This paper presents a novel single slice lymph node segmentation approach. It finds a closed path around a seed point thereby minimizing an energy function which depends on gradients, intensities and shape...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
This paper investigates the effectiveness of compact, task-specific transformations for aligning the breast in the prone position to the compressed breast in the supine position. It was employed for registering 3DMRto 3D ultrasound images of the breast. This task has previously not been attempted and is very difficult due to the huge differences in image appearance and the very large breast deformation...
Nakagami imaging is an ultrasonic tissue characterization technique which has shown promise for differentiating different breast masses. Two parameters describing the shape and the scale of the distribution of the RF-data envelope are computed on sliding windows. The choice of the size of the windows is critical as both oversized and undersized windows can lead to an unstable estimation of the parameters...
In this work, we present a novel content-based 3D shape retrieval system for Abdominal Aortic Aneurysm (AAA) rupture risk prediction. The algorithms incorporate shape context, RANdom SAmple Consensus (RANSAC) and thin plate spline (TPS) to achieve a reliable AAA rupture risk assessment system. Pre-labeled unruptured and ruptured cases (`-1' for unruptured and `1' for ruptured cases) are built and...
We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration...
Coronary angiography is routinely used to screen patients both prior to and during angioplasty. Each angiography study results in a collection of video sequences or “runs” that depict coronary arteries from different viewpoints. A key problem to be addressed in the automatic interpretation of coronary angiography videos is the identification of images depicting coronary arteries in these sequences...
Having ground truth is critical for evaluating segmentation algorithms and estimating the ground truth from a collection of manual segmentations remains a hard problem. A proper estimation approach should take into account and compensate for the inter-rater variation. In this paper, we conduct an analysis of manual segmentations in order to have a better understanding of the pattern of the variation...
In this paper we use humans and chimpanzees brain MRI databases to develop methods for evaluating global brain asymmetries. We perform brain segmentation and hemispheric surface extraction on both populations. The human brain segmentation pipeline is adapted to chimpanzees in order to obtain results of good quality. To alleviate the problems due to cortical variability we propose a mesh processing...
The expanding role of complex object detection algorithms introduces a need for flexible architectures that simplify interfacing with machine learning techniques and offer easy-to-use training and detection procedures. To address this need, the Integrated Detection Network (IDN) proposes a conceptual design for rapid prototyping of object and boundary detection systems. The IDN uses a strong spatial...
Atlas based methods and active contours are two families of techniques widely used for the task of 3D medical image segmentation. In this work we present a coupled framework where the two methods are combined together, in order to exploit each's advantage while avoid their respective drawbacks. Indeed, the atlas based methods lacks the flexibility in locally tuning the segmentation boundary; whereas...
Statistical shape and intensity modelling have been subject to an increasing interest within the past decade. However, construction of such models requires large number of segmented examples. Accurate and automatic segmentation techniques that do not require any explicit prior model are therefore of high interest. We propose a fully-automatic method for segmenting the femur in 3D Computed Tomography...
Prostate segmentation based on magnetic resonance images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Recently, graph based interactive (semi-automatic) segmentation...
The quantification of left ventricular motion can provide valuable information about cardiac function. Echocardiography provides a non-invasive, readily available method for generating real time images of the left ventricle. Two methods for myocardial deformation tracking that have been used are shape tracking and speckle tracking. These two methods provide complementary information. Shape tracking...
Registration of 3-D aortic model onto X-ray images provides anatomical details for optimal valve deployment in Trans-catheter aortic valve implantation (TAVI) procedures. Fast and automatic contrast detection in the aortic root in fluoroscopic sequences facilitates a seamless workflow by triggering 2-D/3-D registration automatically when necessary. In this paper, we propose an integrated spatial and...
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