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This work presents a computer-aided detection (CAD) system to aid radiologists in finding sclerotic bone metastases in the spine on CT images. The spine is first segmented using thresholding, region growing and a vertebra template. A watershed algorithm and a merging routine segment potential lesion candidates in each two-dimensional (2-D) axial CT image. Next, overlapping 2-D detections on sequential...
Automatic hepatic tumor segmentation is a crucial step for diagnosis and surgery planning. This paper presents a new fully automatic technique to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some pepper noise and tumors as dark gray spots. After applying...
In this paper, we propose a new method to detect liver tumors in CT images automatically. The proposed method is composed of two steps. In the first step, tumor candidates are extracted by EM/MPM algorithm; which is used to cluster liver tissue. To cluster a dataset, EM/MPM algorithm exploits both intensity of voxels and labels of the neighboring voxels. It increases the accuracy of detection, with...
This article presents advanced algorithms for segmenting lung nodules, liver metastases, and enlarged lymph nodes in CT scans. Segmentation and volumetry are essential tasks of a software assistant for oncological therapy monitoring. Our methods are based on a hybrid algorithm originally developed for lung nodules that combines a threshold-based approach with model-based morphological processing....
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