Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution. (30th December 2016)

• Record Type: Journal Article
• Authors: da Rocha, Simara Vieira; Braz Junior, Geraldo; Silva, Aristófanes Corrêa; de Paiva, Anselmo Cardoso; Gattass, Marcelo
• Is Part Of: Expert systems with applications. Volume 66(2016)
• Page Start: 7
• Page End: 19
• Publication Date: 2016-12-30
• Subjects: Breast cancer -- Mammography -- Pattern recognition -- Local binary pattern -- Diversity index; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.08.070
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A methodology to texture analysis of masses in digitized mammography is proposed. Our methodology uses only the texture analysis for recognition. We provide the specialist bigger support to the diagnosis of breast cancer. We cooperate to a more precise diagnosis and support in the medical intervention. Abstract: A World Health Organization (WHO) report estimates that in 2015, at least 561 thousand women will die of breast cancer. Although breast cancer is considered a disease of the developed world, nearly 50% of the cases and 58% of the deaths occur in the less developed countries. A mammogram is a way of discovering not just the palpable tumors that cause cancer but also other lesions that are not perceived during the physical examination performed by the expert physician or during self-exams; however, it is known that this exam is targeted for women after the age of 40 because age is one of the factors that can cause great variations in sensitivity during the exam. Besides the patient's age, the expert's experience and the quality of the images obtained during the exam are decisive factors in the detection of breast cancer. This work presents two novelties. The first is the use of Local Binary Patterns (LBPs) to generate a representation of a Region of Interest (ROI) image. Over this representation, we generate other representations using techniques such as image histograms, gray-level co-occurrence matrices (GLCMs) and gray-level run-length matrices (GLRLMs). These representations allow texture analysis through several perspectives. The second novelty uses these representations as input to the application of indexes adapted from ecology (Shannon, McIntosh, Simpson, Gleason and Menhinick) as texture descriptors. Based on this strategy, we analyze mammographic image textures to classify regions of these images as benign or malignant using a Support Vector Machine (SVM). The best result achieved was of 88.31% accuracy, 85% sensitivity, 91.89% specificity, a positive probability ratio of 10.48, a negative probability ratio of 0.16, and an area under the Receiver Operating Characteristic (ROC) curve of 0.88, obtained through the Shannon index. We believe that the proposed method, with some adaptations, may also be used for image texture analysis of several different lesions such as lung nodules, glaucoma and prostates. This belief is based on the achieved results and the method's simplicity.