Overview of advanced computer vision systems for skin lesions characterization
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Introducing intelligence in electronic healthcare systems: state of the art and future trends
Artificial intelligence
Markov-Gibbs random field modeling of 3D skin surface textures for haptic applications
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
Computers in Biology and Medicine
Classification of dermatological ulcers based on tissue composition and color texture features
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Content-based image retrieval of skin lesions by evolutionary feature synthesis
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Skin lesions characterisation utilising clustering algorithms
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
A query-by-example content-based image retrieval system of non-melanoma skin lesions
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
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This paper describes an integrated prototype computer-based system for the characterization of skin digital images. The first stage includes an image acquisition arrangement designed for capturing skin images, under reproducible conditions. The system processes the captured images and performs unsupervised image segmentation and image registration utilizing an efficient algorithm based on the log-polar transform of the images' Fourier spectrum. Border- and color-based features, extracted from the digital images of skin lesions, were used to construct a classification module for the recognition of malignant melanoma versus dysplastic nevus. Different methods, drawn from the fields of artificial intelligence (neural networks) and statistical modeling (discriminant analysis), were used in order to find the best classification rules and to compare the results of different approaches to the problem.