基于三维感兴趣区域和模糊聚类的肝脏肿瘤分割

摘要

肝脏CT图像往往存在着较多的噪声，且肝脏肿瘤的灰度与周围肝实质接近，边界模糊，分割困难。在对肝脏肿瘤分割时，传统的水平集方法对初始轮廓敏感，需要手动调整参数，时间复杂度较高。本文结合肿瘤的模糊性，提出基于三维感兴趣区域（三维ROI）和结合空间信息的模糊聚类的肝脏肿瘤分割方法。首先在三维选取肿瘤的初始感兴趣区域，再结合空间信息的模糊聚类方法进行分割，然后进行形态学操作，最后利用B样条水平集对轮廓边缘进行平滑。实验结果表明，本文提出的方法，操作简便，速度快，能较好地分割出肝脏肿瘤。%In Computed Tomography ( CT) scans of liver it exists many noises.Besides, liver tumor’ s gray level is very close to the liver and the tumor has fuzzy boundaries, it is hard to be segmented.During liver tumor segmentation, the traditional level set method is sensitive to initial contours and needs to adjust the parameters manually, and the time complexity is high.According to liver tumor’ s fuzziness, this paper proposed a new segmentation method for liver tumor based on three dimension region of interest (3D ROI) and spatial fuzzy c-means clustering (FCMS).First it picks the ROI in three dimensions, then uses FCMS to segment the tumor, then does morphology operation, in the end uses variational B-spline level sets method to smooth the contour.The re-sult of test turns out that, the method proposed in this paper gets better result and higher efficiency, which is also easy to operate.