BREAST CALCIFICATION CLUSTERS PROFESSIONAL

Among them, 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. Common X-ray findings of breast cancer include mass, microcalcification, changes in the nipple and areola, structural distortion, and increased vascular deformation. It is highly sensitive to the intramammary calcification, especially small the calcification. Moreover, mammography can show early lesions that are not easily detectable by visual inspection. It is relatively inexpensive and yields a clear image. Due to mammography non-invasiveness, reproducibility and comprehensive sampling, it has occupied a privileged position in the diagnosis of breast diseases. Moreover, commonly used examination methods for breast include mammography, ultrasound, magnetic resonance imaging, and the computed tomography. Common methods for breast cancer diagnosis include the clinical breast examination, imaging diagnosis, and histopathological biopsy. However, 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. The exact cause of breast cancer is unclear and there is no effective way to prevent breast cancer. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value. 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 remaining 100 are used to test the algorithm.

BREAST CALCIFICATION CLUSTERS PROFESSIONAL

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 Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. Secondly, split the ROI by the improved K-means algorithm. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). The present study proposes an algorithm for detecting microcalcification on mammography 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. Microcalcification is the most important landmark information for early breast cancer.

Breast calcification clusters