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In this paper, a texture-based segmentation method of the Malignant Pleural Mesothelioma from thoracic CT scans is presented. For the texture analysis part, we have used an automatic sampling and a manual sampling to extract statistical features from the MPM texture. For the segmentation stage, the method iterates the whole CT volume and selects pixels satisfying the extracted statistical criteria...
In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the...
Identification of lobar fissures in human lungs is a non-trivial task due to their variable shape and appearance, along with the low contrast and high noise in computed tomographic (CT) images. Pathologies in the lungs can further complicate this identification by deforming and/or disrupting the lobar fissures. Current algorithms rely on the general anatomy of the lungs to find fissures affected by...
This paper introduces an automatic liver parenchyma segmentation algorithm that can delineate liver in abdominal CT images. The proposed approach consists of three main steps. Firstly, a texture analysis is applied onto input abdominal CT images to extract pixel level features. Here, two main categories of features, namely wavelet coefficients and Haralick texture descriptors are investigated. Secondly,...
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