title: Breast mass classification on mammograms using radial local ternary patterns.
abstract: Textural features can be useful in differentiating between benign and malignant breast lesions on mammograms. Unlike previous computerized schemes, which relied largely on shape and margin features based on manual contours of masses, textural features can be determined from regions of interest (ROIs) without precise lesion segmentation. In this study, therefore, we investigated an ROI-based feature, namely, radial local ternary patterns (RLTP), which takes into account the direction of edge patterns with respect to the center of masses for classification of ROIs for benign and malignant masses. Using an artificial neural network (ANN), support vector machine (SVM) and random forest (RF) classifiers, the classification abilities of RLTP were compared with those of the regular local ternary patterns (LTP), rotation invariant uniform (RIU2) LTP, texture features based on the gray level co-occurrence matrix (GLCM), and wavelet features. The performance was evaluated with 376 ROIs including 181 malignant and 195 benign masses. The highest areas under the receiver operating characteristic curves among three classifiers were 0.90, 0.77, 0.78, 0.86, and 0.83 for RLTP, LTP, RIU2-LTP, GLCM, and wavelet features, respectively. The results indicate the usefulness of the proposed texture features for distinguishing between benign and malignant lesions and the superiority of the radial patterns compared with the conventional rotation invariant patterns.
authors.affiliation: Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan. Electronic address: chisa@fjt.info.gifu-u.ac.jp.
authors.forename: Chisako
authors.initials: C
authors.lastname: Muramatsu
authors.affiliation: Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Hara
authors.affiliation: Department of Advanced Diagnosis, Nagoya Medical Center, 4-1-1 Nakaku Sannomaru, Nagoya, Aichi 460-0001, Japan; Department of Breast Surgery, Higashi Nagoya National Hospital, 5-101 Umemorizaka, Meitoku, Nagoya, Aichi 465-8620, Japan.
authors.forename: Tokiko
authors.initials: T
authors.lastname: Endo
authors.affiliation: Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Fujita
mesh_terms: D001940:Breast / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D060388:Support Vector Machine
keywords: Breast masses; Classification; Local binary patterns; Local ternary patterns; Mammograms; Texture feature