Exploring Cascade Classifiers for Detecting Clusters of Microcalcifications
The conventional approach to the detection of microcalcifications on mammographies is to employ a sliding window technique. This consists in applying a classifier function to all the subwindows contained in an image and taking each local maximum of the classifier as a possible position of a microcalcification. Although effective such an approach suffers from the high computational burden due to the huge number of subwindows contained in an image. The aim of this paper is to experimentally verify if such problem can be alleviated by a detection system which employs a cascade-based localization coupled with a clustering algorithm which exploits both the spatial coordinates of the localized regions and a confidence degree estimated on them by the final stage of the cascade. The first results obtained on a publicly available set of mammograms show that the method is promising and has large possibility of improvement.
KeywordsMicrocalcifications mammography computer aided detection Adaboost cascade of classifiers clustering
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