Detection Method Of Breast Cancer Based On Mammogram

ABSTRACT:Early detection and diagnosis of breast cancer is the most effective way to save lives of patients. Mammogram is the most preferred method for breast cancer census at Present. However, the ambiguous characteristics of the early cancers and the subjective impact of the doctors will all probably induce the error and miss on diagnosing. With the rapid develop of computers, computer-aided detection of breast cancer has become one of the hot research points among medical image processing field. The effective computer-aided detection method can help doctors realize and analyze the mammograms better, and further improve the accuracy of diagnosis. Micro-calcifications(MCs), mass and architectural distortion(AD) are major signs of breast cancer, while the detection of these lesions is a difficult and challenge problem of the computer-aided detection system. The paper is established based on the previous research, and through incorporating the ideas of machine learning and computer vision, to construct the computer-aided detection analysis system. The main contributions and innovation are as follows:(1) Breast region segmentation and image denoising in mammogram preprocessing are explored. A breast region segmentation method based on optimal threshold and an image smoothing method based on adaptive median filtering are realized. The optimal threshold is computed to remove labels and background from mammogram to segment breast region, which can obtain research region for subsequent processing. Adaptive median filtering provides negative feedback for filters, which is capable of smoothing noise as well as retaining the details in the image, providing support to subsequent processing.(2) In view of the problem that micro-calcifications are segmented incompletely in micro-calcification detection, a method of micro-calcification segmentation based on multi-resolution region growth and mage difference is proposed. In the method, an optimal threshold is obtained in each target region, in which the segmentation is effective. The shape and distribution of micro-calcifications can be obtained accurately, providing support to the subsequent micro-calcification processing.(3) The basic theories of relevance vector machine (RVM) and its development---adaptive kernel learning based relevance vector machine (aRVM) are analyzed. Using the capable of aRVM automatically learning the parameters of the kernel, the use of aRVM for detection of mass in mammograms is explored, and a detection method for mass based on aRVM is proposed. The experimental results show that for mass detection, the aRVM method has better detection performance and robustness than method of SVM and RVM.(4) For the problem of high false positive rate in the detection of architectural distortion (AD) in mammograms, a method to detect AD based on speculation similarity convergence index (SCI) is proposed. In the method, the spiculation similarity based on Mahalanobis distance is presented and applied to compute SCI to enhance radiating spiculations. Then local maximum values of SCI are extracted as AD candidates, and lastly these candidates are classified into AD and normal tissues. The experimental results show that the proposed method can effectively weaken the effect of non-spiculations on AD detection, which can reduce false positive rate significantly and be applied to mammograms of various breast types.