Method of differentiation of benign and malignant masses in digital mammograms using texture analysis based on phylogenetic diversity. (April 2018)

• Record Type: Journal Article
• Authors: Carvalho, Edson Damasceno; de Carvalho Filho, Antonio Oseas; de Sousa, Alcilene Dalília; Silva, Aristófanes Corrêa; Gattass, Marcelo
• Is Part Of: Computers & electrical engineering. Volume 67(2018)
• Page Start: 210
• Page End: 222
• Publication Date: 2018-04
• Subjects: Medical images -- Breast cancer -- Texture descriptors -- Phylogenetic diversity; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.03.038
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Breast cancer is a disease resulting from the multiplication of abnormal breast cells, which form masses. Every year, breast cancer kills more than 500, 000 women around the world. In 2015, 570, 000 women died of breast cancer. When detected early, the five-year survival rate for breast cancer exceeds 80% of cases. Early diagnosis of breast cancer is critical for the survival of the patient. Screening by mammography is the most promising means for early diagnosis. This article presents a method of classifying malignant and benign breast tissue using digital mammography exams. This method employs texture descriptors from all image regions, including to the inner regions. This approach enables a more detailed texture description of the analyzed region of interest. The feature extraction is based on phylogenetic indexes. Then, classification is conducted using multiple classifiers. Experiments are performed to verify the performance of the proposed method. Results show that the method achieves 99.73% accuracy, 99.41% sensitivity, 99.84% specificity, and a receiver operating characteristic (ROC) curve with a value of one when using images of the Digital Database for Screening Mammography. An accuracy of 100% is achieved when using the Mammography Imaging Analysis Society image database. The use of phylogenetic indexes to describe patterns in regions of mammography images in both external and internal areas is thus effective in the categorization of malignant and benign tumors, thereby making the proposed method a robust tool for specialists.