期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 33, 期 5, 页码 1137-1147出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2014.2305769
关键词
Bag of features; classification; distancemetrics between models; Gaussian mixture model; global pattern; Markov random field (MRF)
类别
资金
- CICYT, Spain [TEC2010-21619-C04-02]
- Consejeria de Innovacion, Ciencia y Empresas, Junta de Andalucia, Spain [P11-TIC-7727]
- Consejeria de Innovacion, Ciencia y Empresa of Junta de Andalucia, Spain
In this paper different model-based methods of classification of global patterns in dermoscopic images are proposed. Global patterns identification is included in the pattern analysis framework, the melanoma diagnosis method most used among dermatologists. The modeling is performed in two senses: first a dermoscopic image is modeled by a finite symmetric conditional Markov model applied to color space and the estimated parameters of this model are treated as features. In turn, the distribution of these features are supposed that follow different models along a lesion: a Gaussian model, a Gaussian mixture model, and a bag-of-features histogram model. For each case, the classification is carried out by an image retrieval approach with different distance metrics. The main objective is to classify a whole pigmented lesion into three possible patterns: globular, homogeneous, and reticular. An extensive evaluation of the performance of each method has been carried out on an image database extracted from a public Atlas of Dermoscopy. The best classification success rate is achieved by the Gaussian mixture model-based method with a 78.44% success rate in average. In a further evaluation the multicomponent pattern is analyzed obtaining a 72.91% success rate.
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