Detection of Tumour Based on Breast Tissue Categorization

Abstract

Background: Despite the benefits of Computer Aided Detection (CAD), false detection of breast tumour is still a challenging issue with oncologist. A mammography is a non-invasive screening tool that uses low energy X-rays to show the pathology structure of breast tissue. Interpreting mammogram visually is a time consuming process and requires a great deal of skill and experience. Earlier Computer Aided Techniques emphasis detection of tumour in breast tissues rather than categorization of breast into Breast Imaging Report and Data System (BI-RADS) which is the medically understandable method of reporting. Aim: The work centred on developing a CAD system which is capable of not only detecting but also categorizing breast tissue in line with BI-RADS scale. Methodology: The acquired images were pre-processed to remove unwanted contents. Two stage medical procedural approach was designed to categorize the tissue in breast images into low dense (fatty) and high dense. Tumours in the low dense breasts were segmented, and then classified as normal, benign and malignant. The developed system was evaluated using sensitivity, specificity, false positive reduction, false negative reduction and overall performance. Results: The developed CAD achieved 90.65% sensitivity, 73.59% specificity, 0.02 positive reduction, 0.04 false negative reduction and 85.71% overall performance. Conclusion: The false positive reduction result obtained shows that false detection has been minimized as a result of categorization procedure of the breast tissue in mammograms