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Proceedings Paper

3D texture-based classification applied on brain white matter lesions on MR images
Author(s): Mariana Leite; David Gobbi; Marina Salluzi; Richard Frayne; Roberto Lotufo; Letícia Rittner
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Paper Abstract

Lesions in the brain white matter are among the most frequently observed incidental findings on MR images. This paper presents a 3D texture-based classification to distinguish normal appearing white matter from white matter containing lesions, and compares it with the 2D approach. Texture analysis were based on 55 texture attributes extracted from gray-level histogram, gray-level co-occurrence matrix, run-length matrix and gradient. The results show that the 3D approach achieves an accuracy rate of 99.28%, against 97.41% of the 2D approach by using a support vector machine classifier. Furthermore, the most discriminating texture attributes on both 2D and 3D cases were obtained from the image histogram and co-occurrence matrix.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852N (24 March 2016); doi: 10.1117/12.2216285
Show Author Affiliations
Mariana Leite, Univ. Estadual de Campinas (Brazil)
Univ. of Calgary (Canada)
David Gobbi, Univ. of Calgary (Canada)
Marina Salluzi, Univ. of Calgary (Canada)
Richard Frayne, Univ. of Calgary (Canada)
Roberto Lotufo, Univ. Estadual de Campinas (Brazil)
Letícia Rittner, Univ. Estadual de Campinas (Brazil)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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