3D texture-based classification applied on brain white matter lesions on MR images

Mariana Leite; David Gobbi; Marina Salluzi; Richard Frayne; Roberto Lotufo; Letícia Rittner

doi:10.1117/12.2216285

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24 March 2016 3D texture-based classification applied on brain white matter lesions on MR images

Mariana Leite, David Gobbi, Marina Salluzi, Richard Frayne, Roberto Lotufo, Letícia Rittner

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Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97852N (2016) https://doi.org/10.1117/12.2216285
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

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Mariana Leite, David Gobbi, Marina Salluzi, Richard Frayne, Roberto Lotufo, and Letícia Rittner "3D texture-based classification applied on brain white matter lesions on MR images", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852N (24 March 2016); https://doi.org/10.1117/12.2216285

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