Abstract：
:In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Pardo XM,Radeva P,Cabello Ddoi
10.1016/s1361-8415(03)00014-8subject
Has Abstractpub_date
2003-09-01 00:00:00pages
293-310issue
3eissn
1361-8415issn
1361-8423pii
S1361841503000148journal_volume
7pub_type
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