Discriminant snakes for 3D reconstruction of anatomical organs

X. M. Pardo, P. Radeva, D. Cabello

    Research output: Contribution to journalArticleResearchpeer-review

    14 Citations (Scopus)


    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. © 2003 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)293-310
    JournalMedical Image Analysis
    Issue number3
    Publication statusPublished - 1 Jan 2003


    • 3D medical images
    • Fisher linear discriminant analysis
    • Segmentation
    • Snakes
    • Supervised learning


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