Improved Snake for Breast Tumor Image Segmentation Using Prior Shape Constraint

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The accuracy of breast tumor image segmentation plays a crucial role in the radiotherapy treatment planning, which directly affects the effectiveness of patients treatment. Snake can dynamically track the desired object boundaries from an initial contour, however, its sensitivity and narrow capture range of initial contour has limited its utility. When Snake is directly applied to segment breast tumor image, it often produces improper result due to the closeness of intensity variations within both tumor area and non-tumor areas. Therefore, an improved Snake segmentation method based on the prior shape constraint is proposed to solve the problems caused by fuzzy boundaries and similar gray levels of tumor and non-tumor areas. Firstly, tumor lesion via partitioned images is extracted according to the gray and shape characteristics. Then morphological filter is utilized to enhance the lesion. Simultaneously, the average shape is obtained by establishing a prior shape model with Point Distribution Model (PDM). Finally, superposition of the deformable shape is derived by adjusting parameters with the mean shape upon real tumor boundaries in a new image. Snake is then employed to merge the real target contours. Experiments show that this method has higher accuracy and stronger robustness to noise. Moreover, perfect segmentation results are also attained in special cases where breast tumor and chest wall image intensity values are relatively close.