3d Segmentation and Labeling Using Self-organizing Kohonen Network for Volumetric Measurments on Brain Ct Imaging
نویسنده
چکیده
NETWORK FOR VOLUMETRIC MEASURMENTS ON BRAIN CT IMAGING Mohamed N. Ahmed, and Aly A. Farag Computer Vision and Image Processing Lab University of Louisville, Department of Electrical Engineering Louisville, KY 40292 E-mail:[email protected], Phone:(502)-852-7510, Fax:(502)852-6807 Abstract|In this paper, we present a new system to segment and label CT Brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of di erential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classi cation of the voxels into regions. Keywords|Volume Segmentation, Neural Networks, Kohonen Feature maps.
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3D Segmentation and Labeling Using Unsupervised Clustering For Volumetric Measurments On Brain CT Imaging
In this paper, we present a new system to segment and label CT Brain slices using diierential geometrical invariant features and unsupervised clustering. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses, and to study the correlation between the mor...
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