Study On The New Method Of Computer-aided Diagnosis Based On Mammograms

Breast cancer is one of the most common malignant diseases among women.Clear evidence shows that early discovery, early diagnosis and early treatment ofbreast cancer can significantly increase the chance of survival for patients.Mammography is the most effective method for the early detection of breast cancer.However normally, viewed mammograms display only a very small part of the totalinformation they contain. It is very hard to find the microcalcifications (MCCs) ofearly breast cancer in mammograms even for an experienced radiologist. Therefore,any increase in the detection and classification of MCCs will lead to furtherimprovement in its efficacy in the detection of early breast cancer. With the rapidprogress of computer technology, computer aided detection and identification ofMCCs have been a hot research field since clustered MCCs in mammograms are animportant sign for early detection of breast cancer. It is estimated that about 30% to50% of breast carcinomas detected radiographically demonstrates MCCs inmammograms. So the increase in the detection and classification of MCCs inmammograms has been of interest to many researchers. This paper presents a prototype of a computer-aided diagnostic system (CAD) formammography screening to automatically detect and classify MCCs in mammograms.It comprises four modules. The first module, called the mammogram preprocessingmodule, digitizes and normalizes the original mammogram, and makes it to be fit forcomputer processing. Since the region of interest (ROI) covers only a small part ofthe whole mammograms, the second module, called the ROI finder module, finds andlocates suspicious areas of MCCs. Independent component analysis is implemented toextract the features of ROI, and artificial neural network(ANN) classifier is used tolabel the region as either true or false ROI. Since only MCCs are of interest inproviding a sign of breast cancer, the third module, called the MCCs detectionmodule, is a computer automated MCCs detection system that takes as inputs theROIs provided by the ROI finder module. Two methods are used to detect the MCCs.The first one based on difference-image technique is used to remove thelow-frequency background, while the second one based on wavelet denoising andneural network classifying technique is used to remove the very high-frequency noise.Signals coming out from two methods are combined through a logical AND operationto get the final detected result that contains the position information of MCCs. Finally, IIIthe fourth module, called the MCCs classification module, includes featuresextraction, feature optimization and pattern recognition. A pool of many features (33)with the information about shape, texture and so on of MCCs is computed. Geneticalgorithm is introduced to get the optimal features (17). The ANN classifier is used tolabel the MCC as either malignant or benign. Moreover, support vector machinerelying on the statistical learning theory is applied to the patter recognition in theresearch. One advantage of the designed system is that each module is a separatecomponent that can be individually upgraded to improve the whole system. Moreover,receiver operation curve is used to evaluate the performance of each decision modelin this research. The above methods are adopted in the processing of the test sampleswith a true positive rate (TPR) of 87.5% (ANN) and 90.0% (SVM) in the ROIautomated finder module, a TPR of 96.3% (ANN) and 97.0% (SVM) in the MCCsdetection module, a TPR of 88.7% (ANN) and 93.0% (SVM) in the MCCsclassification module. The SVM classifiers get slightly better results than the ANNclassifiers. The results show that the method has a high performance on the detectionand classification of MCCs, and gives a new method for the research on the diagnosisof early breast cancer. The originalities of this thesis are the followings: 1. One modularization design thought is int...