Global pattern analysis and classification of dermoscopic images using textons

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%. 1. DESCRIPTION OF PURPOSE In the last two decades, a rising incidence of malignant melanoma has been observed. Because of the lack of adequate therapies for metastatic melanoma, the best treatment is still early diagnosis and prompt surgical excision of the primary cancer. Dermoscopy (also known as epiluminescence microscopy) is an in-vivo method that has been reported to be a useful tool for the early recognition of malignant melanoma. As diagnostic accuracy with dermoscopy has been shown to depend on the training and the experience of the dermatologist, computer assisted diagnosis (CAD) systems will help less-experienced and un-trained dermatologists. This paper follows the new trend in dermatology: to look for specific patterns in the lesions which will lead dermatologists to an assessment. They take into account the overall general appearance or global patterns of the lesion in order to diagnose the malignancy. The five global patterns of skin lesions are: • Reticular pattern or pigment network pattern This is the most common global feature in melanocytic lesions. It is characterized by a pigment network covering most parts of a given lesion. Basically, the pigment network appears as a grid of thin brown lines over a diffuse light brown background. • Globular pattern The globular pattern is characterized by the presence of numerous, variously sized, round to oval structures with various shades of brown and gray-black coloration. • Cobblestone pattern This is quite similar to the globular one but is composed of closely aggregated, larger, somewhat angulated globules resembling a cobblestone. • Homogeneous pattern this pattern appears as diffuse pigmentation, which might be brown, grey-blue, grey-black ,or reddish black pigmentation in the absence of pigment network or other distinctive local features. • Parallel pattern It is found on the palms and soles due to the particular anatomy of these areas. Medical Imaging 2012: Image Processing, edited by David R. Haynor, Sébastien Ourselin, Proc. of SPIE Vol. 8314, 83144X · © 2011 SPIE · CCC code: 0277-786X/11/$18 · doi: 10.1117/12.911818 Proc. of SPIE Vol. 8314 83144X-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 10/14/2012 Terms of Use: http://spiedl.org/terms