Adaptive Edge Detection Technique Towards Features Extraction from Mammogram Images

Abstract

Cancer is one of the most dreaded diseases of modern world. Breast cancer is the second most type of cancer & the fifth most common cause of cancer related death so it’s a significant public health problem in the world especially for elderly females. Computer technology specifically computer aided diagnosis (CAD), relatively young interdisciplinary technology, has had a tremendous impact on medical diagnosis of cancer detection due to its accuracy and cost effectiveness. The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm. To achieve accurate segmentation, an efficient edge-detection algorithm is essential. The mammogram is a comparatively efficient and low cost diagnostic imaging technique for breast cancer detection. In this paper a robust mammogram enhancement and edge detection algorithm is proposed, using tree-based adaptive thresholding technique. The proposed technique has been compared with different classical edge-detection techniques yielding acceptable out come. The proposed edge-detection algorithm showing 0.07 p-values and 2.411 t-stat in one sample two tail t-test (I± = 0.025). The edge is single pixeled and connected which is very significant for medical edge-detection.