Classification of breast tissues in mammogram images using ripley's K function and support vector machine

Authors:
Leonardo de Oliveira Martins;Geraldo Braz Junior;Erick Corrêa da Silva;Aristófanes Corrêa Silva;Anselmo Cardoso de Paiva
Affiliations:
Federal University of Maranhão, Department of Electrical Engineering, São Luís, MA, Brazil;Federal University of Maranhão, Department of Electrical Engineering, São Luís, MA, Brazil;Federal University of Maranhão, Department of Electrical Engineering, São Luís, MA, Brazil;Federal University of Maranhão, Department of Electrical Engineering, São Luís, MA, Brazil;Federal University of Maranhão, Department of Computer Science, São Luís, MA, Brazil
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

Citing 3
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Abstract

Female breast cancer is a major cause of death in western countries. Several computer techniques have been developed to aid radiologists to improve their performance in the detection and diagnosis of breast abnormalities. In Point Pattern Analysis, there is a statistic known as Ripley's K function that is frequently applied to Spatial Analysis in Ecology, like mapping specimens of plants. This paper proposes a new way in applying Ripley's K function in order to distinguish Mass and Non-Mass tissues from mammogram images. The features of each image are obtained through the calculate of that function. Then, the samples gotten are classified through a Support Vector Machine (SVM) as Mass or Non-Mass tissues. SVM is a machinelearning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. Another way of computing Ripley's K function, using concentric rings instead of a circle, is also examined. The best result achieved was 94.25% of accuracy, 94.59% of sensitvity and 94.00% of specificity.