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Prostate segmentation is an essential step in developing any noninvasive 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 Dynamic Contrast Enhancement MRI (DCE-MRI) is proposed. The framework is based on Maximum A Posteriori (MAP) estimate of a...
In this paper, we present a novel methodology for computing statistical shape models (SSM's) by leveraging the medial axis model to determine shape variations between objects. Landmark based SSM's (LSSM's) are a popular approach to describing valid shape variation in an object of interest by applying principal component analysis to a set of landmarks on the surface of the object. However, defining...
A framework for fast multiview fusion of Single Plane Illumination Microscopy (SPIM) images based on a spatially-variant point spread function (PSF) model is presented. For the multiview fusion a new algorithm based on the regularized Lucy-Richardson deconvolution and the Overlap-Save method is developed and tested on SPIM images. In the algorithm the image is decomposed into small blocks which are...
This paper presents a novel colon fold contour estimation technique, a crucial step toward fully-automated reconstruction of a 3D colon segment from an image captured from colonoscopy for automated labeling of unseen mucosa areas (where polyps or abnormal lesions may reside) during colonoscopy. The endoscopist can then manipulate the endoscope to inspect these unseen areas. The proposed technique...
Due to the aging of our society, osteoporosis and the subsequent fractures is becoming an increasingly important problem. Currently, Dual-energy X-ray Absortiometry (DXA) derived areal Bone Mineral Density (BMD) is the gold standard for diagnosing osteoporosis, but is limited to a two-dimensional analysis. In this work, we propose a method to discriminate between healthy and fracture patients from...
We present a method for prediction of atherosclerotic growth based on a training set of 229 2D manually annotated baseline and corresponding follow-up calcifications from lateral X-ray images over an 8 year period. The prediction uses affine shape analysis based on singular value decomposition where non-rigid shapes are modeled as projections of rigid high-dimensional shapes. The SVD prediction was...
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
Image segmentation is one of the key problems in medical image analysis. This paper presents a new statistical shape model for automatic image segmentation. In contrast to the previous model based segmentation methods, where shape priors are estimated from a general population-based shape model, our proposed method aims to estimate patient-specific shape priors to achieve more accurate segmentation...
In vivo parcellation of the cerebral cortex via non-invasive neuroimaging techniques has been in active research for over a decade. A variety of model-driven or data-driven computational approaches have been proposed to parcellate the cortex. A fundamental issue in these parcellation methodologies is the features or attributes used to define boundaries between cortical regions. This paper proposes...
Autosomal dominant polycystic kidney disease (ADPKD) is characterized by the growth of cysts in the kidneys that increase their volume, disrupt renal function, and lead to kidney failure. So far no efficacious therapy for this condition exists and thus clinical treatment trials are performed. In this work patients participating in such a study were monitored with MRI. The manual data analysis is costly...
Segmentation and tracking of tagged MR images is a critical component of in vivo understanding for the heart dynamics. In this paper, we propose a novel approach which uses multi-dimensional features and casts the left ventricle (LV) extraction problem as a maximum posteriori estimation process in both the feature and the shape spaces. Exact integration of multi-dimensional boundary and regional statistics...
In this paper, a novel tree-based registration framework is proposed for achieving fast and accurate registration by providing a more appropriate initial deformation field for the image under registration. Specifically, in the training stage, all training real images and a selected portion of simulated images are organized into a combinative tree with the template as the root, and then each training...
Object boundary extraction is an important task in brain image analysis. Acquiring detailed 3D representations of the brain structures could improve the detection rate of diseases at earlier stages. Deformable model based segmentation methods have been widely used with considerable success. Recently, 3D Active Volume Model (AVM) was proposed, which incorporates both gradient and region information...
Quantitative assessment of facial asymmetry is crucial for successful planning of corrective surgery. We propose a tensor-based morphometry (TBM) framework to locate and quantify asymmetry using 3D CBCT images. To this end, we compute a rigid transformation between the mandible segmentation and its mirror image, which yields global rotation and translation with respect to the cranial base to guide...
We represent a shape representation technique using the eigenfunctions of Laplace-Beltrami (LB) operator and compare the performance with the conventional spherical harmonic (SPHARM) representation. Cortical manifolds are represented as a linear combination of the LB-eigenfunctions, which form orthonormal basis. Since the LB-eigenfunctions reflect the intrinsic geometry of the manifolds, the new representation...
Autism severely impairs personal behavior and communication skills, so that improved diagnostic methods are called for. Neuropathological studies have revealed abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We explore a possibility of distinguishing between autistic and normal (control) brains by quantitative CC shape analysis in the 3D magnetic resonance images (MRI). Our approach...
Computed Tomography Angiography (CTA) of the heart is a non-invasive procedure to rule out coronary artery disease or measure its extent and plan treatments and interventions. The need for coronary tree tracking methods that require minimum human interaction and produce accurate and robust measurements is therefore of great clinical importance. In this work we present a probabilistic coronary artery...
Pancreas segmentation in 3-D computed tomography (CT) data is of high clinical relevance, but extremely difficult since the pancreas is often not visibly distinguishable from the small bowel. So far no automated approach using only single phase contrast enhancement exist. In this work, a novel fully automated algorithm to extract the pancreas from such CT images is proposed. Discriminative learning...
Searching for vertebrae in a large collection of spine X-ray images that are relevant to pathology is potentially important for providing assistance to radiologists and bone morphometrists. Developing appropriate methods for such searching tasks is very challenging due to the high similarities among vertebral shapes in contrast to the subtle dissimilarities that characterize the pathology. In this...
Preterm birth is associated with abnormal brain development and long-term neurodevelopmental impairment. Quantitative magnetic resonance (MR) studies of preterm brain injury have focused on morphological features such as shape and volume and on measures of tissue microstructure obtained from diffusion tensor imaging. In this work, we focus on longitudinal changes in signal intensity, which can offer...
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