Tumor Detection on Preprocessed Mammograms

Breast cancer has become one of the leading mortality causes among women [1]. The chance of survival significantly grows if it is detected early; so a screening program was started in 2002. The high number of mammograms requires computer-aided diagnostics. The main goal is to help the work of radiologists by finding true negative cases, thus they only have to examine suspected positive ones. So the detection ratio of true positive cases must be as high as possible, while the ratio of false positive detections must be kept at a low level to avoid unnecessary examinations. Tumors are difficult to describe by mathematical models, and conventional image processing methods also fail to detect them, so a combined technique is being developed. First global methods are used to obtain a segmentation of breast tissues. They can extract some suspicious areas in the breast, but they generally find at least 3-4 such places in every image, so the ratio of false positive detections (FPR) becomes impermissibly high. Thus local methods are used to reduce this FPR. In this work these local methods are discussed, and the achieved results are presented, respectively.