Rule-based morphological feature extraction of microcalcifications in mammograms
Dongming Zhao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
In this paper, we discuss a method using combined morphological filtering and rule-based feature extraction for detecting microcalcifications in mammograms. After preprocessing of digitized mammographic images where salt-pepper noise is removed, morphological operations are applied to extract local background of the images. The extracted background is then used for an adaptive thresholding on the images. The objective of the adaptive thresholding is to separate local gray-scale variations from the image, since microcalcification features are most likely embedded in local variations. A threshold is set to binarize the local variations. After thresholding, morphological size filters are applied to extract the features related to mirocalcifications. To facilitate the feature extraction process, a rule-based selection procedure is developed based on local density distribution of the binarized feature image, local variation deviation, and connectivity of suspicious spots. The limited experimental results show that the approach, while combined with other image analysis and pattern classification techniques, can provide a useful tool for assisting mammographic diagnosis processes.