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The paper presents the automated computation of hepatic tumor burden from abdominal computed tomography (CT) images of diseased populations with images with inconsistent enhancement. The automated segmentation of livers is addressed first. A novel 3-D affine invariant shape parameterization is employed to compare local shape across organs. By generating a(More)
PURPOSE To test the feasibility of and improve a computer algorithm to automatically detect colonic polyps in real human computed tomographic (CT) colonographic data sets. MATERIALS AND METHODS Twenty patients with known polyps underwent CT colonography in the supine position. CT colonographic data were processed by using a shape-based algorithm that(More)
The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four(More)
An automatic method to segment colonic polyps in computed tomography (CT) colonography is presented in this paper. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy c-mean clustering, and deformable models. The computer segmentations were compared with manual segmentations to validate the accuracy of our method. An average(More)
An abdominal computed tomographic scan was modified by inserting 10 simulated colonic polyps with use of methods that closely mimic the attenuation, noise, and polyp-colon wall interface of naturally occurring polyps. A shape-based polyp detector successfully located six of the 10 polyps. When settings that enhanced the edge profile of polyps were chosen,(More)
PURPOSE To apply a computer-aided detection (CAD) algorithm to supine and prone multisection helical computed tomographic (CT) colonographic images to confirm if there is any added benefit provided by CAD over that of standard clinical interpretation. MATERIALS AND METHODS CT colonography (with patients in both supine and prone positions) was performed(More)
RATIONALE AND OBJECTIVES A new classification system for colonic polyp detection, designed to increase sensitivity and reduce the number of false-positive findings with computed tomographic colonography, was developed and tested in this study. MATERIALS AND METHODS The system involves classification by a committee of neural networks (NNs), each using(More)
In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image(More)
PURPOSE To investigate the potential of the normalized probabilistic atlases and computer-aided medical image analysis to automatically segment and quantify livers and spleens for extracting imaging biomarkers (volume and height). METHODS A clinical tool was developed to segment livers and spleen from 257 abdominal contrast-enhanced CT studies. There were(More)
Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per(More)