Identifying cancer regions in vital-stained magnification endoscopy images using adapted color histograms

Authors:
A. Sousa;M. Dinis-Ribeiro;M. Areia;M. Coimbra
Affiliations:
Instituto de Telecomunicações, Faculdade de Ciências da Universidade do Porto;CINTESIS, Faculdade de Medicina do Porto and Instituto Português de Oncologia, Porto;Instituto Português de Oncologia, Coimbra;Instituto de Telecomunicações, Faculdade de Ciências da Universidade do Porto
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

In-body imaging technologies such as vital-stained magnification endoscopy pose novel image processing challenges to computer-assisted decision systems given their unique visual characteristics such as reduced color spaces and natural textures. In this paper we will show the potential of using adapted color features combined with local binary patterns, a texture descriptor that has exhibited good adaptation to natural images, for classifying gastric regions into three groups: normal, pre-cancer and cancer lesions. Results exhibit 91% accuracy, confirming that specific research for in-body imaging could be the key for future computer assisted decision systems for medicine.