Breast Microcalcifications Detection And Recognition Algorithm Of Benign And Malignant Based On X-ray Mammography

Breast cancer is one of the most common malignant cancers among women,which has been one of the fatal factors for women death.Clear evidence shows that the current examination of women breast is the key measure for early discovery,early diagnosis,and early treatment,increasing the cure rate of breast cancer and reducing mortality,since there are no effective measures for a timely prevention of breast cancer.In the current examination procedure of breast,the number of mammograms to be analyzed by the radiologists is enormous in the screening programs,manual reading is labor intensive and time consuming,demanding great concentration of radiologists, for there is a risk that they may miss some subtle abnormalities.The problems associated with the traditional diagnosis procedure prompted medical practitioners and scientists in related fields to consider alternative approaches.A computer-aided diagnosis (CAD) system for breast cancer is proved to be important in the screening programs.Research activity in computer-based processing and analysis of mammograms has been one of the most important and difficult mammogram processing problems in recent years,the research in this area has great theoretical value as well as important social significance.The novel research methods of computer-aided diagnosis (CAD) system for breast cancer is presented in this paper,the main contents are described as follows:Firstly,the image signs and diagnosis of breast cancers is introduced, secondly, we describe the signs of mammogram,CT,MRI and special imaging.The breast occupies a small part in mammogram,and most regions are low density with noises.In order to increasing the speed of post-processing,the breast is firstly extracted.The novel research some methods of the above-mentioned problem.In mammography processing,theextraction of microcalfication in mammograms is one of the most difficult problems,which is one of the most important steps in computer-aided diagnosis on mammograms.A lot of algorithms have been supposed, but these algorithms are unsatisfying due to low true positive rate and high false positive rate.In this paper,an automatic extracting method of microcalfication in mammography is given to obtain high true positive rate and low false positive rate. This paper presents a new mathematical model of clone algorithm based on an analogy to animal clone;in this model,the clone is regarded as the genetic information gradually expressed under the control of optimize exciting factors which are obtained using iterative method,The regions of interest are classified based on expressed genetic information.The model has been used in the microcalfication detection in mammography and has achieved high true positive rate.The experimental results are given to show the effectiveness of the method.In this novel,a micro-calcifications lesion type identification algorithm, through the calcification point feature extraction and optimization,and lesion type identification, give a preliminary diagnosis.In the finality,the problems requiring further studies are discussed.