Abstract
BackgroundBreast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer. In order to detect the breast cancer, computer aided technology has been introduced. In computer aided cancer detection, the detection and segmentation of mass are very important. The shape of mass can be used as one of the factors to determine whether the mass is malignant or benign. However, many of the current methods are semi-automatic. In this paper, we investigate fully automatic segmentation method.ResultsIn this paper, a new mass segmentation algorithm is proposed. In the proposed algorithm, a fully automatic marker-controlled watershed transform is proposed to segment the mass region roughly, and then a level set is used to refine the segmentation. For over-segmentation caused by watershed, we also investigated different noise reduction technologies. Images from DDSM were used in the experiments and the results show that the new algorithm can improve the accuracy of mass segmentation.ConclusionsThe new algorithm combines the advantages of both methods. The combination of the watershed based segmentation and level set method can improve the efficiency of the segmentation. Besides, the introduction of noise reduction technologies can reduce over-segmentation.
Highlights
Breast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer
The mass location was identified by an experienced radiologist and a region of interest (ROI) containing the mass was extracted
The results show that all cases of segmentation were accurate in comparison with the radiologist-marked on the mammograms
Summary
Breast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer. Mammography is thought of as one of the most effective methods to detect early breast cancer. In computer aided cancer diagnosis, the detection and segmentation of mass are very important. Many methods for mass segmentation algorithms have been proposed These algorithms include manual segmentation [7], semi-automatic segmentation [8], and fully automatic segmentation [9]. Manual segmentation is considered to be the best mass boundary extraction method [10,11], it is time-consuming. It subjects to intra-observer and inter-observer variation [11]. In [14], Lou et al
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