Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image processing and applications
Color image processing and applications
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Scalable multiresolution color image segmentation
Signal Processing
Intelligent segmentation and classification of pigmented skin lesions in dermatological images
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Melanoma recognition using representative and discriminative kernel classifiers
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Estimation and choice of neighbors in spatial-interaction models of images
IEEE Transactions on Information Theory
Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model
IEEE Transactions on Image Processing
Scale invariant descriptors in pattern analysis of melanocytic lesions
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bayesian multiscale analysis of images modeled as Gaussian Markov random fields
Computational Statistics & Data Analysis
Pattern classification of dermoscopy images: A perceptually uniform model
Pattern Recognition
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
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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.