Pattern Recognition 42 (2009) 1052--1057 Contents lists available at ScienceDirect Pattern Recognition journal homepage: www.elsevier.com/locate/pr Pattern analysis of dermoscopic images based on Markov random fields Carmen Serrano, Begoña Acha ∗ Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Sevilla, Spain ARTICLE INFO ABSTRACT Article history: Received 5 December 2007 Received in revised form 13 June 2008 Accepted 14 July 2008 Keywords: Dermoscopic images Pattern classification Markov random field In this paper a method for detecting different patterns in dermoscopic images is presented. In order to diagnose a possible skin cancer, physicians assess the lesion based on different rules. While the most famous one is the ABCD rule (asymmetry, border, colour, diameter), the new tendency in dermatology is to classify the lesion performing a pattern analysis. Due to the colour textured appearance of these patterns, this paper presents a novel method based on Markov random field (MRF) extended for colour images that classifies images representing different dermatologic patterns. First, each image plane in L ∗ a ∗ b ∗ colour space is modelled as a MRF following a finite symmetric conditional model (FSCM). Coupling of colour components is taken into account by supposing that features of the MRF in the three colour planes follow a multivariate Normal distribution. Performance is analysed in different colour spaces. The best classification rate is 86% on average. © 2008 Elsevier Ltd. All rights reserved. 1. Introduction In the last two decades, a rising incident 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 [1]. 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 ma- lignant melanoma [2]. Its use increases diagnostic accuracy between 5% and 30% over clinical visual inspection [3]. In order to give a diagnosis, physicians follow a two-step algo- rithm: (1) classify the lesion into melanocytic and non-melanocytic type and (2) for the melanocytic ones, classify into benign and ma- lignant lesions. In order to perform the second step, four different approaches are the most commonly used: the ABCD rule of der- moscopy, the 7-point checklist, the Menzies method, and pattern analysis [4]. The currently available digital dermoscopic systems offer the pos- sibility of computer storage and retrieval of dermoscopic images and patient. Some systems even offer the potential of computer assisted diagnosis (CAD) [5,6]. As diagnostic accuracy with dermoscopy has been shown to depend on the experience of the dermatologist, CAD systems will help less-experienced dermatologists. Most of the technical papers developing methods to classify automatically dermatologic images are based on the ABCD rule ∗ Corresponding author. Tel.: +34 954487333; fax: +34 954487341. E-mail addresses: cserrano@us.es (C. Serrano), bacha@us.es (B. Acha). 0031-3203/$ - see front matter © 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.patcog.2008.07.011 (asymmetry, border irregularity, colour variegation, diameter greater than 6 mm or growing). Normally, the papers present one approach to cover one or some of the “letters” of the rule, that is, some are based on detecting asymmetry [7,8], borders [9–12], colour [13–15] or diameter [14]. There are some papers that cover the whole ABCD criterion. Tomatis et al. detect features for the ABCD rule, but they need a telespectrophotometric system [16]. Larabi et al. [17] extract some parameters to cover the ABCD rule, but they do not use it to classify the lesion but for retrieval. Maglogiannis et al. use a sup- port vector machine to classify border features, colour features and texture features [18]. In any case, all the methods present in the literature, to the best of our knowledge, consist always of a feature extraction step (colour, texture and/or shape characteristics), an optional feature selection step and a final feature classification step. In general, the contribution of the papers is the election of new features to classify the lesion. One of the novelties of this paper is that it is not based on de- tecting specific features in the images to cover the four letters of the ABCD rule, but it follows the new tendency in dermatology: to look for specific patterns in the lesions which will lead physicians to an assessment. Looking at the clinical references in this subject, we can see that the procedure can be summarized as a pattern recognition system. Physicians, in order to classify between benign and malign lesions, take into account the overall general appearance of colour, architectural order, symmetry of pattern and homogeneity (CASH). Benign melanocytic lesions tend to have few colours, architectural order, symmetry of pattern or homogeneity. Malignant melanoma often has many colours and much architectural disorder, asymmetry of pattern and heterogeneity [4].