Microcalcification Clusters Detection Based on Ensemble Learning
Xinsheng Zhang, Hua Xie, Ya-Ling Niu · 2008
A new microcalcification clusters (MCs) detection method in mammograms is proposed in this paper, which is based on a new ensemble learning method. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimensional image features are extracted by the feature extractor and fed to the ensemble decision model. In image feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained ensemble model is used as a classifier to decide the presence of MCs or not. A large number of experiments are carried out to evaluate the proposed MCs detection algorithms. The experimental results illustrate its effectiveness.