Breast masses in mammography classification with local contour features

• Published in: Biomedical engineering online Vol. 16; no. 1; p. 44
• Main Authors: Li, Haixia, Meng, Xianjing, Wang, Tingwen, Tang, Yuchun, Yin, Yilong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14.04.2017; BioMed Central; BMC
• Subjects: 1D signature contour subsection; Automation; Benign; Breast cancer; Breast implants; Breast mass; Breast Neoplasms - diagnostic imaging; Care and treatment; Classification; Classifiers; Contours; Feature extraction; Fractals; Humans; Image Processing, Computer-Assisted - methods; Mammography; Methods; Online data bases; Process controls; RMS slope; Shape; Support vector machines; Tumors; 1D signature contour subsection; RMS slope; Breast mass
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-017-0332-0

Mammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well. In this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass. The proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier. The performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.