A new method of micro-calcifications detection in digitized mammograms based on improved simplified PCNN

GoalThe micro-calcifications is early symptom of breast cancer, however it is inefficient for radiologists to read mammograms manually. In this study, an automatic detection method for micro-calcification clusters (MCs) in digitized mammograms is proposed. MethodFirstly, the Otsu thresholding method and a minimum enclosing rectangle are used to obtain the breast area. Secondly, we use mathematical morphology and a non-linear transform to enhance contrast, and then bi-orthogonal wavelet is used to extract wavelet high-frequency coefficients. Finally, the MCs are obtained by a modified Simplified Pulse Coupled Neural Network (SPCNN) model. ResultThe system is tested both on the Mammographic Image Analysis Society (MIAS) Database and the database from Japanese Society of Medical Imaging Technology, moreover the clinical database of People's Hospital of Gansu Province is also obtained to verify this proposed method. The detection result shows that this new method is effective both on experiment and clinic. ContributionFirstly, the relationship between the iteration step and the segmentation result is studied to improve the detection rate; secondly, an improved Pulse Coupled Neural Network (PCNN) model without training is proposed to detect MCs, and it is proved to be more practical and effective than the current state-of-the-art models.

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