A novel fuzzy based framework for detection of clustered microcalcification in mammograms
Farhang Sahba, A. Venetsanopoulos · 2010
This paper outlines a new method for automatic detection of microcalcification clusters in mammograms. The presence of microcalcification clusters, which appear as small bright spots in mammographic images, is considered a very important sign in breast cancer diagnosis. However, such clusters can be hard to detect due to their size and low contrast from surrounding normal tissue. This work presents a new fuzzy based method for the detection of microcalcification clusters. The proposed method consists of four major steps. First, the breast area is extracted. Then a powerful fuzzy contrast adaptation is employed to highlight the contrast of the microcalcification spots. Next, a thresholding method based on fuzzy sets type II is used to extract the candidate points. Finally, the features of these points are extracted and a support vector machine classifier distinguishes the location of real microcalcifications. During these steps, the selection of appropriate parameters is performed based on local image characteristics. The results are promising and show that this method can detect microcalcifications effectively, making it useful towards computer-aided breast cancer diagnosis.