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This study presents a shape and margin characterization method of breast mass lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The overlap between a mass lesion and its minimum volume enclosing ellipsoid (MVEE) is used to capture the overall shape of a lesion. Various statistical measurements on distance from the lesion surface to its MVEE surface are computed to characterize...
Detecting cancerous lesion is a major clinical application in emission tomography. In a previous work, we have shown that penalized maximum likelihood image reconstruction can improve lesion detection at a fixed location by designing a shift-invariant quadratic penalty function. Here we extend this work to detection of tumors at unknown positions. We present a method to design a shift-variant quadratic...
We present a new automatic method for segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Images. The algorithm performs tissue classification combining a within subject global tissue intensity model and a local tissue intensity model derived from an aligned set of healthy reference subjects. MS lesions are detected as outliers towards the proposed coupled global/local intensity...
This article presents a segmentation approach based on random walk (RW) method to delineate tumors having inhomogeneous activity distributions of 18FDG on Positron Emission Tomography (PET) images. Based on the original algorithm of RW [1], we propose an improved approach using an adaptive parameter instead of a fixed one and integrating probability densities of label into the system of linear equations...
Early therapy response prediction, employing biomarkers such as 18F-fluorodeoxyglucose (FDG) followed with positron emission tomography (PET), is an actively researched topic. Traditionally, only the first order intensity based feature estimates are used for the response evaluations. In this work, we focus on the predictive power of lesion texture along with traditional features in follow up studies...
We propose a novel affinity matrix for image segmentation in this paper. The affinity matrix is constructed by using the Gaussian weighted Chi-square distance with neighborhood information, in which the vital spatial structure of the image is considered. An adaptive local scaling parameter is used to refine the segmentation rather than selecting a single scaling parameter. We demonstrate that graph-based...
The present work presents a new approach, based on the elaboration of a whole video clip of the ecoghraphic acquisition describing the lesion from different viewpoints. The focus of the paper is in comparing performances which can be obtained, using a simple classification algorithm, using the full video clip against the more common single frame approach. The system consists of five modules: preprocessing,...
This work presents a computer-aided detection (CAD) system to aid radiologists in finding sclerotic bone metastases in the ribs on routine body imaging CT protocols with 5mm chest/abdomen CT images. First, the spine is segmented to locate the ribs using thresholding, region growing and a vertebra template. The centerlines of ribs are then traced by a progressive algorithm based on maximizing the cross-section...
Pigment network is considered a key differential structure by dermatologists. Recently, different approaches have been proposed for the detection and characterization of this structure. This paper proposes an improved system for automatic detection of pigment network regions. The system starts by detecting the presence of pigment network using a bank of directional filters and a connected component...
Traumatic brain injury (TBI) due to falls, car accidents, and warfare affects millions of people annually. Determining personalized therapy and assessment of treatment efficacy can substantially benefit from longitudinal (4D) magnetic resonance imaging (MRI). In this paper, we propose a method for segmenting longitudinal brain MR images with TBI using personalized atlas construction. Longitudinal...
Brain abnormalities such as white matter lesions (WMLs) are not only linked to cerebrovascular disease, but also with normal aging, diabetes and other conditions increasing the risk for cerebrovascular pathologies. Obtaining quantitative measures which assesses the degree or probability of WML in patients is important for evaluating disease burden, and for evaluating its progression and response to...
Dilated capillaries are an important characteristic of basal cell carcinoma (BCC). Detecting capillaries in images can improve a computer-aided skin cancer diagnosis system. In this study, we investigate the feasibility to extract capillaries from clinical images of skin lesions recorded by a regular digital camera. First, we used a compact set of 1 curvilinear and 2 color parameters to train a support...
In clinical practice, detection and quantification of calcified lesions in the coronary arteries and aorta is normally performed on non contrast enhanced Computed Tomography (CT) scans. This CT scan is generally followed by a contrast enhanced angiography (CTA) scan for better plaque visualization. We propose and evaluate a method for the detection of calcified lesions on CTA scans. Calcium lesion...
This paper presents an algorithm for classification of non-melanoma skin lesions based on a novel hierarchical K-Nearest Neighbors (K-NN) classifier. The K-NN classifier is simple, quick and effective. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that...
We investigate the potential of using dual-time-point PET data to perform Patlak modeling. If successful this approach could be used for whole-body dynamic PET in which we compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position. Our approach directly uses list-mode arrival time for each event to compute the Patlak image. We evaluate performance of the method...
In this paper we propose a pipeline to integrate breast diffusion and perfusion MRI for diagnosis, surgical planning and follow-up. Dynamic contrast enhanced (DCE) and diffusion weighted (DWI) MRI provide complementary information on the tissue structure and properties: while DCE-MRI allows the characterization of the lesion angiogenesis, DWI techniques can probe the apparent diffusion coefficient...
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