Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contr...Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contrast or ambiguous margins. Besides, the shapes and densities of masses in mammograms are various. In this paper, a hybrid method combining a random walks algorithm and Chan-Vese (CV) active contour is proposed for automatic mass segmentation on mammograms. The data set used in this study consists of 1095 mass regions of interest (ROIs). First, the original ROI is preprocessed to suppress noise and surrounding tissues. Based on the preprocessed ROI, a set of seed points is generated for initial random walks segmentation. Afterward, an initial contour of mass and two probability matrices are produced by the initial random walks segmentation. These two probability matrices are used to modify the energy function of the CV model for prevention of contour leaking. Lastly, the final segmentation result is derived by the modified CV model, during which the probability matrices are updated by inserting several rounds of random walks. The proposed method is tested and compared with other four methods. The segmentation results are evaluated based on four evaluation metrics. Experimental results indicate that the proposed method produces more accurate mass segmentation results than the other four methods.展开更多
This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors.An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood ana...This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors.An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper.In order to accommodate different situations of masses,the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy.106 benign and 110 malignant tumors were included in this study.We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns.Then the segmented results were evaluated by the classification accuracy.42 features including age,intensity,shape and texture were extracted from each segmented mass and support vector machine(SVM)was used as a classifier.The classification accuracy was evaluated using the area(A_(z))under the receiver operating characteristic(ROC)curve.It was found that the maximum likelihood analysis achieved an A_(z)value of 0.835,the maximum gradient analysis got an A_(z)value of 0.932 and the hybrid assessment function performed the best classification result where the value of A_(z)was 0.948.In addition,compared with traditional region growing algorithm,our proposed algorithm is more adaptive and provides a better performance for future works.展开更多
基金Project (Nos. 60772092 and 81101903) supported by the National Natural Science Foundation of China
文摘Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contrast or ambiguous margins. Besides, the shapes and densities of masses in mammograms are various. In this paper, a hybrid method combining a random walks algorithm and Chan-Vese (CV) active contour is proposed for automatic mass segmentation on mammograms. The data set used in this study consists of 1095 mass regions of interest (ROIs). First, the original ROI is preprocessed to suppress noise and surrounding tissues. Based on the preprocessed ROI, a set of seed points is generated for initial random walks segmentation. Afterward, an initial contour of mass and two probability matrices are produced by the initial random walks segmentation. These two probability matrices are used to modify the energy function of the CV model for prevention of contour leaking. Lastly, the final segmentation result is derived by the modified CV model, during which the probability matrices are updated by inserting several rounds of random walks. The proposed method is tested and compared with other four methods. The segmentation results are evaluated based on four evaluation metrics. Experimental results indicate that the proposed method produces more accurate mass segmentation results than the other four methods.
基金This work was supported by the National Natural Science Foundation of China(Grant No.60772092).
文摘This study attempted to accurately segment the mammographic masses and distinguish malignant from benign tumors.An adaptive region growing algorithm with hybrid assessment function combined with maximum likelihood analysis and maximum gradient analysis was developed in this paper.In order to accommodate different situations of masses,the likelihood and the edge gradients of segmented masses were weighted adaptively by the use of information entropy.106 benign and 110 malignant tumors were included in this study.We found that the proposed algorithm obtained segmentation contour more accurately and delineated the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns.Then the segmented results were evaluated by the classification accuracy.42 features including age,intensity,shape and texture were extracted from each segmented mass and support vector machine(SVM)was used as a classifier.The classification accuracy was evaluated using the area(A_(z))under the receiver operating characteristic(ROC)curve.It was found that the maximum likelihood analysis achieved an A_(z)value of 0.835,the maximum gradient analysis got an A_(z)value of 0.932 and the hybrid assessment function performed the best classification result where the value of A_(z)was 0.948.In addition,compared with traditional region growing algorithm,our proposed algorithm is more adaptive and provides a better performance for future works.
