Classification of Breast Masses in Mammogram Images Using Ripley's K Function and Support Vector Machine

  • Authors:

  • Leonardo Oliveira Martins;Erick Corrêa Silva;Aristófanes Corrêa Silva;Anselmo Cardoso Paiva;Marcelo Gattass

  • Affiliations:

  • Federal University of Maranhão - UFMA, Department of Electrical Engineering, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil;Federal University of Maranhão - UFMA, Department of Electrical Engineering, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil;Federal University of Maranhão - UFMA, Department of Electrical Engineering, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil;Federal University of Maranhão - UFMA, Department of Computer Science, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580, São Luís, MA, Brazil;Pontifical Catholic University of Rio de Janeiro, Technical Scientific Center, Departament of Informatics, Rua Marquês de São Vicente, 225, Gávea, 22453-900 - Rio de Janeiro, RJ, Br ...

  • Venue:
  • MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
  • Year:
  • 2007

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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 Kfunction that is frequently applied to Spatial Analysis in Ecology, like mapping specimens of plants. This paper proposes a new way in applying Ripley's Kfunction to classify breast masses from mammogram images. The features of each nodule image are obtained through the calculate of that function. Then, the samples gotten are classified through a Support Vector Machine (SVM) as benign or malignant masses. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. The best result achieved was 94.94% of accuracy, 92.86% of sensitvity and 93.33% of specificity.