A Genuine GLCM-based Feature Extraction for Breast Tissue Classification on Mammograms

  • Semih Ergin
  • İdil Işıklı Esener
  • Tolga Yüksel
Keywords: Breast tissue, Digital mammography, Feature extraction, Computer-aided detection

Abstract

A breast tissue type detection system is designed, and verified on a publicly available mammogram dataset constructed by the Mammographic Image Analysis Society (MIAS) in this paper. This database consists of three fundamental breast tissue types that are fatty, fatty-glandular, and dense-glandular. At the pre-processing stage of the designed detection system, median filtering and morphological operations are applied for noise reduction and artifact suppression, respectively; then a pectoral muscle removal operation follows by using a region growing algorithm. Then, 88-dimensional texture features are computed from the GLCMs (Gray-Level Co-Occurrence Matrices) of mammogram images. Besides, a formerly introduced 108-dimensional feature ensemble is also computed and cascaded with the 88-dimensional texture features. Finally, a classification process is realized using Fisher’s Linear Discriminant Analysis (FLDA) classifier in four different classification cases: one-stage classification, first fatty – then others, first fatty-glandular – then others, and first dense-glandular – then others. A maximum of 72.93% classification accuracy is achieved using only texture features whereas it is increased to 82.48% when cascade features are utilized. This consequence clearly exposes that the cascade features are more representative than texture features. The maximum classification accuracy is attained when “first fatty-glandular – then others” classification case is implemented, that is consistent with the fact that fatty-glandular tissue type is easily confused with fatty and dense-glandular tissue types.

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Published
2016-12-26
How to Cite
[1]
S. Ergin, İdil Esener, and T. Yüksel, “A Genuine GLCM-based Feature Extraction for Breast Tissue Classification on Mammograms”, IJISAE, pp. 124-129, Dec. 2016.
Section
Research Article