Integration of strategies based on relevance feedback into a tool for the retrieval of mammographic images

Authors:
A. Fornells;E. Golobardes;X. Vilasís;J. Martí
Affiliations:
Research Group in Intelligent Systems, Ramon Llull University;Research Group in Intelligent Systems, Ramon Llull University;Automatic and Control Section, Ramon Llull University, Enginyeria i Arquitectura La Salle, Barcelona, Spain;Computer Vision and Robotics Group, University of Girona, Girona, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

The incidence of breast cancer varies greatly among countries, but statistics show that every year 720,000 new cases will be diagnosed world-wide. However, a high percentage of these cases can be 100% healed if they are detected in early stages. Because symptoms are not visible as far as advanced stages, it makes the treatments more aggressive and also less efficient. Therefore, it is necessary to develop new strategies to detect the formation in early stages. We have developed a tool based on a Case-Based Reasoning kernel for retrieving mammographic images by content analysis. One of the main difficulties is the introduction of knowledge and abstract concepts from domain into the retrieval process. For this reason, the article proposes integrate the human experts perceptions into it by means of an interaction between human and system using a Relevance Feedback strategy. Furthermore, the strategy uses a Self-Organization Map to cluster the memory and improve the time interaction.