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Skin lesions characterisation utilising clustering algorithms

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Autore
Tasoulis, S. K.; Doukas, C. N.; Maglogiannis, I.; Plagianakos, V. P.
Data
2010
DOI
10.1007/978-3-642-12842-4_28
Soggetto
Classification
Cluster analysis
Feature extraction
Image analysis
Kernel density estimation
Pigmented skin lesion
Principal Component Analysis
Unsupervised clustering
Pigmented skin lesions
Principal Components
Artificial intelligence
Computer vision
Dermatology
Image segmentation
Processing
Clustering algorithms
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Abstract
In this paper we propose a clustering technique for the recognition of pigmented skin lesions in dermatological images. It is known that computer vision-based diagnosis systems have been used aiming mostly at the early detection of skin cancer and more specifically the recognition of malignant melanoma tumour. The feature extraction is performed utilising digital image processing methods, i.e. segmentation, border detection, colour and texture processing. The proposed method belongs to a class of clustering algorithms which are very successful in dealing with high dimensional data, utilising information driven by the Principal Component Analysis. Experimental results show the high performance of the algorithm against other methods of the same class. © Springer-Verlag Berlin Heidelberg 2010.
URI
http://hdl.handle.net/11615/33569
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