Title :
Discriminant snakes for 3D reconstruction in medical images
Author :
Pardo, X.M. ; Radeva, P.
Author_Institution :
Dept. Electron. e Comput., Santiago de Compostela Univ., Spain
Abstract :
We propose a new statistic deformable model that we call discriminant snake for 3D reconstruction in volumetric images. Our discriminant snake generalises the classical snake attracted by edge points; it deforms due to a generalised contour representation. The snake selects and classifies image features by a parametric classifier and each snaxel deforms to minimise the dissimilarity between the learned and found image features inside the feature space. We apply our statistic snake to segment anatomical organs and the results are very encouraging
Keywords :
edge detection; feature extraction; image classification; image reconstruction; image segmentation; learning (artificial intelligence); medical image processing; principal component analysis; stereo image processing; 3D reconstruction; contour representation; discriminant snake; feature extraction; image classification; image segmentation; medical images; principal component analysis; statistic deformable model; supervised learning; volumetric images; Biomedical imaging; Deformable models; Image edge detection; Image reconstruction; Image segmentation; Image sequences; Parametric statistics; Shape; Statistical analysis; Supervised learning;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.902927