title: [Study on multi-level fractal features extraction method of breast mass].
abstract: Breast mass is women's common disease, and it seriously affects the health of women. Accurate detection and location of the mass will greatly improve the precision of the diagnosis of the breast disease. Studies have shown that the organizational structure and surface roughness of the mass constitute the texture features of mass and it is an important basis for distinguishing the mass. In this paper, a multi-level fractal features extraction method is proposed, and, moreover, the fractal feature vector of the suspicious lesions has been established, thus realizing the feature extraction and analysis of the mammary gland. Finally, the result of classification with the support vector machine (SVM) was given. 110 mammograms are extracted and classified with an accuracy of 90%. The results showed that the multi-level fractal features extraction and classification methods proposed in this paper could improve the accuracy of mass detection and could be effective for the early diagnosis of the disease.
authors.affiliation: Institute of Biomedical and Electromagnetic Engineering, Shenyang University of Technology, Shenyang 110870, China. ke.l@live.cn
authors.forename: Li
authors.initials: L
authors.lastname: Ke
authors.forename: Xue
authors.initials: X
authors.lastname: Zhang
authors.forename: Yan
authors.initials: Y
authors.lastname: Kang
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D017709:Fractals; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods; D011856:Radiographic Image Enhancement; D060388:Support Vector Machine