A Genuine GLCM-based Feature Extraction for Breast Tissue Classification on Mammograms - Ergin, Semih; Esener, Ä°dil IÅŸÄ±klÄ±; YÃ¼ksel, Tolga (Prof. Dr. Ismail SARITAS, 2016)
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 FisherÃ¢Â€Â™s Linear Discriminant Analysis (FLDA) classifier in four different classification cases: one-stage classification, first fatty Ã¢Â€Â“ then others, first fatty-glandular Ã¢Â€Â“ then others, and first dense-glandular Ã¢Â€Â“ 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 Ã¢Â€Âœfirst fatty-glandular Ã¢Â€Â“ then othersÃ¢Â€Â 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.