Global pattern analysis and classification of dermoscopic images using textons

Maryam Sadeghi; Tim K. Lee; David McLean; Harvey Lui; M. Stella Atkins

doi:10.1117/12.911818

24 February 2012 Global pattern analysis and classification of dermoscopic images using textons

Maryam Sadeghi, Tim K. Lee, David McLean, Harvey Lui, M. Stella Atkins

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83144X (2012) https://doi.org/10.1117/12.911818
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Detecting and classifying global dermoscopic patterns are crucial steps for detecting melanocytic lesions from non-melanocytic ones. An important stage of melanoma diagnosis uses pattern analysis methods such as 7-point check list, Menzies method etc. In this paper, we present a novel approach to investigate texture analysis and classification of 5 classes of global lesion patterns (reticular, globular, cobblestone, homogeneous, and parallel pattern) in dermoscopic images. Our statistical approach models the texture by the joint probability distribution of filter responses using a comprehensive set of the state of the art filter banks. This distribution is represented by the frequency histogram of filter response cluster centers called textons. We have also examined other two methods: Joint Distribution of Intensities (JDI) and Convolutional Restricted Boltzmann Machine (CRBM) to learn the pattern specific features to be used for textons. The classification performance is compared over the Leung and Malik filters (LM), Root Filter Set (RFS), Maximum Response Filters (MR8), Schmid, Laws and our proposed filter set as well as CRBM and JDI. We analyzed 375 images of the 5 classes of the patterns. Our experiments show that the joint distribution of color (JDC) in the L*a*b* color space outperforms the other color spaces with a correct classification rate of 86.8%.

Citation Download Citation

Maryam Sadeghi, Tim K. Lee, David McLean, Harvey Lui, and M. Stella Atkins "Global pattern analysis and classification of dermoscopic images using textons", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83144X (24 February 2012); https://doi.org/10.1117/12.911818

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