Breast density pattern characterization by histogram features and texture descriptors

Article English OPEN
Pedro Cunha Carneiro ; Marcelo Lemos Nunes Franco ; Ricardo de Lima Thomaz ; Ana Claudia Patrocinio (2017)
  • Publisher: Sociedade Brasileira de Engenharia Biomédica
  • Journal: Research on Biomedical Engineering (issn: 2446-4740)
  • Related identifiers: doi: 10.1590/2446-4740.07916
  • Subject: BI-RADS™ | Biotechnology | BI-RADS™ | Breast density | R5-920 | CAD | Artificial neural networks | Medicine (General) | Digital mammography | Feature selection | TP248.13-248.65
    acm: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION | ComputingMethodologies_PATTERNRECOGNITION

Abstract Introduction Breast cancer is the first leading cause of death for women in Brazil as well as in most countries in the world. Due to the relation between the breast density and the risk of breast cancer, in medical practice, the breast density classification is merely visual and dependent on professional experience, making this task very subjective. The purpose of this paper is to investigate image features based on histograms and Haralick texture descriptors so as to separate mammographic images into categories of breast density using an Artificial Neural Network. Methods We used 307 mammographic images from the INbreast digital database, extracting histogram features and texture descriptors of all mammograms and selecting them with the K-means technique. Then, these groups of selected features were used as inputs of an Artificial Neural Network to classify the images automatically into the four categories reported by radiologists. Results An average accuracy of 92.9% was obtained in a few tests using only some of the Haralick texture descriptors. Also, the accuracy rate increased to 98.95% when texture descriptors were mixed with some features based on a histogram. Conclusion Texture descriptors have proven to be better than gray levels features at differentiating the breast densities in mammographic images. From this paper, it was possible to automate the feature selection and the classification with acceptable error rates since the extraction of the features is suitable to the characteristics of the images involving the problem.