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The automatic analysis of subtle changes between longitudinal MR images is animportant task as it is still a challenging issue in scope of the breastmedical image processing. In this paper we propose an effective automaticchange detection framework composed of two phases since previously used methodshave features with low distinctive power. First, in the preprocessing phase anintensity normalization method is suggested based on Hierarchical HistogramMatching (HHM) that is more robust to noise than previous methods. To eliminateundesirable changes and extract the regions containing significant changes theproposed Extraction Region of Changes (EROC) method is applied based onintensity distribution and Hill-Climbing algorithm. Second, in the detectionphase a region growing-based approach is suggested to differentiate significantchanges from unreal ones. Due to using proposed Weighted Local MutualInformation (WLMI) method to extract high level features and also utilizing theprinciple of the local consistency of changes, the proposed approach enjoysreasonable performance. The experimental results on both simulated and reallongitudinal Breast MR Images confirm the effectiveness of the proposedframework. Also, this framework outperforms the human expert in some caseswhich can detect many lesion evolutions that are missed by expert.

A New Automatic Change Detection Frame-work Based on Region Growing and  Weighted Local Mutual Information: Analysis of Breast Tumor Response to  Chemotherapy in Serial MR Images