Texture-based analysis of clustered microcalcifications detected on mammograms
Digital Signal Processing
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A new mixed feature multistage false positive (FP) reduction method has been developed for improving the FP reduction performance. Eleven features were extracted from both spatial and morphology domains in order to describe the micro-calcification clusters (MCCs) from different perspectives. These features are grouped into three categories: gray-level description, shape description and clusters description. Two feature sets that focus on describing MCCs on every single calcification and on clustered calcifications, respectively, were combined with a back-propagation (BP) neural network with Kalman filter (KF) to obtain the best performance of FP reduction. First, 9 of the 11 gray-level description and shape description features were employed with BP neural network to eliminate all the obvious FP calcifications in the image. Second, the remaining MCCs will be classified into several clusters by a widely used criterion in clinical practice, and then the two cluster description features will be added to the first feature set to eliminate the FP clusters from the remaining MCCs. The performance results of this approach were obtained using an image database of 100 real cases of patient's mammogram images in H. Lee Moffitt Cancer Center imaging program.