Multiresolution Texture Segmentation

Volumetric Texture Segmentation by Discriminant Feature Selection and Multiresolution Classification


Updated 8 Feb 2021

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Texture analysis in 2D has been well studied, but many 3D applications in Medical Imaging, Stratigraphy or Crystallography, would beneit from 3D analysis instead of the traditional, slice-by-slice approach. 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 [1]. A novel Bhattacharyya space, based on the Bhattacharyya distance is proposed for selecting the most discriminant measurements and producing a compact feature space. Each dimension of the feature space is used to form the lowest level of a Quad Tree. At the highest level of the tree, new positional features are added to improve the contiguity of the classification. The classified space is then projected to lower levels of the tree where a boundary refinement procedure is performed with a 3D equivalent of butterfly filters. The performance of M-VTS is tested in 2D by classifying a set of standard texture images. M-VTS yields lower misclassification rates than reported elsewhere. The algorithm was tested in 3D with artificial isotropic data and three Magnetic Resonance Imaging sets of human knees with encouraging results. The regions segmented from the knees correspond to anatomical structures that could be used as a starting point for other measurements. By way of example, we demonstrate successful cartilage extraction.

Cite As

Constantino Carlos Reyes-Aldasoro (2023). Multiresolution Texture Segmentation (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2017a
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