Computer Aided Detection Of Calcifications In Mammograms

Breast cancer is one of the most common malignant tumors in women, which is a serious threat to women's health. But the etiology of breast cancer is not explicit now. Recently there is a trend of increasing incidence. So early diagnosis and treatment of breast cancer is a key ingredient of any strategy designed to reduce breast cancer mortality. Small, granular calcification is an important feature of early breast cancer. According to related statistics, 30%-50% of breast cancer is associated with calcification. Mammography is the first choice for early detection of breast cancer for it's simple and economic. So early detection of calcification in mammography is a key technology for early diagnosis of breast cancer. But calcifications are usually very small and their shapes are irregular. They are also very similar to the shadow of some dense tissues or blood vessels, so only a small part of the calcifications can be identified through human eyes. In most cases, calcifications cannot be easily detected, making it a tough problem for physicians. Thanks to the rapid development of modern medical imaging technology, it's possible to accomplish computer-aided detection of calcification with the use of computer and artificial intelligence technology. On the one hand it provides valuable advice and reference and helps reduce the work of finding lesion area for doctors; On the other hand, it makes mammograms diagnosis become more objective, and helps to reduce the resulting misdiagnosis and missed diagnosis when the doctors are lack of experience. Now, improving the accuracy of detection of calcification through image processing techniques is a research focus.This paper is an investigation for calcification detection in early breast cancer. We'll investigate computer-aided detection of calcification in mammograms. We finished the preparation for mammogram data, extracted the breast region. Then we extracted the calcification regions and labeled them, and show the effectiveness of the algorithm by experimental results. In this paper, the research content and results are the following parts:(1) Achieve extraction of breast region automatically, and reduce the interference which may be caused by background in calcification detection, and can reduce the amount of computation effectively, make a nice prepare for the following calcification detection.(2) For calcifications are always small, and have a low contrast with background, so use the pixel-level feature extraction.Make calcifications segmentation in standard area provided by doctors, and select samples, and do calcifications feature extraction and selection in two kinds of samples, and use forward sequence to find out the most optimal feature subset. (3) Design and train neural network classifier, construct neural network based on the selected feature subset, obtain the optimal classifier, and analyze the performance of the classifier.(4) Use the optimal classifier to test breast X-ray images, after analyzing the shape of pseudo-calcifications, we can remove them better, and improve the accuracy of detection, and achieve the purpose of computer-aided detection.Based on the above ideas, this paper analyze and process the data based on DDSM and MIAS database, follow the steps of breast region extraction, feature extraction, neural network design and image test to conduct the final experiment, verify the feasibility and effectiveness of the algorithm, and achieve the purpose of computer-aided detection.