Research On Computer-Aided Detection Methods Of Breast Mass Based On Mammography

Breast cancer is one of the leading causes of death in women over the age of 50. For the early detection and diagnosis of breast cancer, mammography is currently considered the most reliable and cost-effective method. Computer-aided detection (CAD) based on mammography could eventually provide a valuable "second opinion" for improving accuracy, efficiency and consistency of detecting breast cancer in the clinical evironment. However, the performance of current CAD in mass detection remains relatively low, and the radiologists could not have confidence in and accept this type of schemes.To improve the performance in the computer-aided detection of mass based on single mammography, as well as to increase radiologists' confidence in and reliance on CAD-prompted mass detection results, computer-aided detection methods of breast mass based on mammography are studied in detailed in this dissertation. The main contents are as follows: accurate mass segmentation and related characteristic quantification in CAD based on single mammography, matched regions identification and related feature extraction in CAD based on multiple mammographies (ipsilateral views), accurate and efficient search of similar reference images in CAD based on content-based image retrieval (CBIR).First, due to the two challenging problems, mass segmentation and related feature extraction, an automated segmentation method based on maximum entropy principle and active contour model is proposed in this dissertation. With the segmented mass contour, spiculated tissues surrounding the mass are detected, and a quantitative spiculation index is computed to assess the degree of spiculation.Second, based on the projected distance to the nipple along the centerline, matched regions identification and related feature extraction are carried out on both ipsilateral views (cranio-caudal view and mediolateral oblique view). With the 39 features extracted from single mammography and the 23 features extracted from multiple mammographies, stepwise feature selection method and linear discriminant analysis (Fisher) are sequentially used to obtain the detection scores of the suspicious regions. The performances in mass detection of CAD based on single mammography and CAD based on multiple mammographies are evaluated and compared. The experimental results show that CAD based on multiple mammographies could improve the sensitivities, while reducing false-positive detection rates in the CAD based on single mammography. CAD based on multiple mammographies could improve the performance of CAD based on single mammography.Third, with the mass segmentation based on maximum entropy principle and active contour model, 60 related feature extraction, GA-based (Genetic Algorithm) feature selection, PSO-based (Particle Swarm Optimization) feature weights study, similarity measure based on weighted Euclidean distance and KNN-based (K Nearest Neighbor) decision index computation, CAD based on CBIR is implemented in this dissertation.Based on the mass segmentation, the reference databased is divided. Feature selection and weight study are respectively applied to each dataset. With the combination of weighted multi-image feature-based similary measure and pixel value-based Pearson's correlation, the effect of the segmentation qualify on the similariy measure is adjusted. This method improves the performance of ICAD (Interactive CAD) based on CBIR, and obtains CAD-prompted mass detection results which will increase the radiologists' confidence and reliance.The researches on CAD method based on single mammography, CAD method based on multiple mammographies and CAD method based on content-based image retrieval in this dissertation establish theoretical basis for widely use of CAD systems in clinics.