Cluster-based classification using self-organising maps for medical image databases

Authors:
L. A. Silva;E. Del-Moral-Hernandez;R. A. Moreno;S. S. Furuie
Affiliations:
Department of Electronic Systems Engineering, University of Sao Paulo, Av. Prof. Luciano Gualberto Trav.3 N.158. CEP:05508-900, Cidade Universitaria, Sao Paulo, SP, Brasil.;Department of Electronic Systems Engineering, University of Sao Paulo, Av. Prof. Luciano Gualberto Trav.3 N.158. CEP:05508-900, Cidade Universitaria, Sao Paulo, SP, Brasil.;Heart Institute (InCor), University of Sao Paulo Medical School, Av. Dr. Eneas de Carvalho Aguiar, 44, 2o. Andar (Informatica), CEP:05403000, Sao Paulo, SP, Brasil.;School of Engineering, University of Sao Paulo, Av. Prof. Luciano Gualberto Trav.3 N.158. CEP:05508-900, Cidade Universitaria, Sao Paulo, SP, Brasil
Venue:
International Journal of Innovative Computing and Applications
Year:
2009

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Abstract

Images are a fundamental source of information in medicine. They can support doctors and students in diagnostic decisions besides providing research and didactic material. The images stored in a database and divided in categories are an important step for data mining and content-based image retrieval (CBIR). This work addresses a methodology which joins the use of discrete wavelet transforms to characterise images and self-organising maps (SOM) neural networks to cluster based classification of medical images. This data mining methodology can be used in categorisation and in computer-aided diagnostic decision.