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Volumetric texture segmentation by discriminant feature selection and multiresolution classification

Reyes-Aldasoro, C. C. and Bhalerao, A. (2007). Volumetric texture segmentation by discriminant feature selection and multiresolution classification. IEEE Transactions on Medical Imaging, 26(1), pp. 1-14. doi: 10.1109/TMI.2006.884637

Abstract

In this paper, a multiresolution volumetric texture segmentation (M-VTS) algorithm is presented. The method extracts textural measurements from the Fourier domain of the data via subband filtering using an orientation pyramid (Wilson and Spann, 1988). A novel Bhattacharyya space, based on the Bhattacharyya distance, is proposed for selecting the most discriminant measurements and producing a compact feature space. An oct tree is built of the multivariate features space and a chosen level at a lower spatial resolution is first classified. The classified voxel labels are then projected to lower levels of the tree where a boundary refinement procedure is performed with a three-dimensional (3-D) equivalent of butterfly filters. The algorithm was tested with 3-D artificial data and three magnetic resonance imaging sets of human knees with encouraging results. The regions segmented from the knees correspond to anatomical structures that can be used as a starting point for other measurements such as cartilage extraction.

Publication Type: Article
Additional Information: © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Algorithms, Artificial Intelligence, Cluster Analysis, Discriminant Analysis, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Information Storage and Retrieval, Knee Joint, Magnetic Resonance Imaging, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity
Subjects: R Medicine > RC Internal medicine
Departments: School of Mathematics, Computer Science & Engineering > Engineering
School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
Related URLs:
URI: http://openaccess.city.ac.uk/id/eprint/4320
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