Abstract
Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.
Highlights
Breast cancer is a common malignant tumor with high morbidity and mortality
Gabriele Valvano [18] proposed a novel based on convolutional neural networks for networks the detection of microcalcification approach based on convolutional neural for and the segmentation detection and segmentation of clusters
Our image enhancement algorithm is based on Contourlet transform and mathematical In order to reduce the misdiagnosis and wrong diagnosis, caused by the fixed threshold, it is intended morphology, which makes full use of the multi-resolution analysis of Contourlet transform and to introduce an adaptive threshold mechanism to determine the threshold of the layering, according the characteristics of image edge information processing and mathematical morphology
Summary
Breast cancer is a common malignant tumor with high morbidity and mortality. It has become one of the main threats of women’s health and life. A large number of studies have shown that breast cancer screening is an effective way to improve the early detection and cure rates of breast cancer. Common methods for breast cancer diagnosis include the clinical breast examination, imaging diagnosis, and histopathological biopsy. The mass and microcalcification are the most important and obvious pathological signs of breast cancer, which are of great significance from cancer detection and diagnosis point of view. Statistics show that with the advent of computer-aided diagnosis systems, the detection rate of breast cancer increased by 20% [6]. Between anatomic pathology and imaging diagnosis, R Bonfiglio [7] offers a significant new way to improve the analysis of microcalcification for breast cancer. The clinic-pathological factors, progression-free survival, and overall survival were evaluated by SPSS
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