title: A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation.
abstract: This paper presents a method for breast cancer diagnosis in digital mammogram images. Multi-resolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer.
authors: Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt. tokhy2478@yahoo.com
authors: Mohamed
authors: M
authors: Meselhy Eltoukhy
authors: Ibrahima
authors: I
authors: Faye
authors: Brahim
authors: B
authors: Belhaouari Samir
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D060388:Support Vector Machine; D058067:Wavelet Analysis