A Novel Application of Multifractal Features for Detection of Microcalcifications in Digital Mammograms

This paper presents a novel image processing algorithm for automated microcalcifications (MCs) detection in digital mammograms. In order to improve the detection accuracy and reduce false positive (FP) numbers, two scales of sub-images are considered for detecting varied sized MCs, and different processing algorithms are used on them. The main contributions of this research work include: use of multifractal analysis based methods to analyze mammograms and describe MCs texture features; development of adaptive α values selection rules for better highlighting MCs patterns in mammograms; application of an effective SVM classifier to predict the existence of tiny MC spots. A full-field digital mammography (FFDM) dataset INbreast is used to test our proposed method, and experimental results demonstrate that our detection algorithm outperforms other reported methods, reaching a higher sensitivity (80.6%) and reducing FP numbers to lower levels.