Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification

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 Bhatta...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2007-01, Vol.26 (1), p.1-14
Main Authors: Aldasoro, C.C.R., Bhalerao, A.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Butterflies
Classification tree analysis
Cluster Analysis
Data mining
Discriminant Analysis
Extraterrestrial measurements
Filtering
Filters
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Knee
Knee Joint - anatomy & histology
Knees
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Magnetic separation
multiresolution
Pattern Recognition, Automated - methods
Reproducibility of Results
Segmentation
Sensitivity and Specificity
Spatial resolution
Studies
Surface layer
Testing
Texture
texture segmentation
Trees
volumetric texture
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Summary: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
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2006.884637