计算机与现代化

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基于三维感兴趣区域和模糊聚类的肝脏肿瘤分割

  

  1. 重庆大学计算机学院,重庆400044
  • 收稿日期:2015-03-04 出版日期:2015-08-08 发布日期:2015-08-19
  • 作者简介:金凯成(1991-),男,浙江义乌人,重庆大学计算机学院硕士研究生,研究方向:医学图像处理; 王翊(1977-),男,博士,研究方向:图像处理; 郑申海(1988-),男,博士研究生,研究方向:图像处理; 欧阳自鹏(1992-),男,硕士研究生,研究方向:医学图像处理。
  • 基金资助:
    国家自然科学重点基金资助项目(61190122); 国家科技支撑计划项目(2012BAI06B01)

Segmentation Method for Liver Tumor Based on 3D ROI and FCM

  1. College of Computer Science, Chongqing University, Chongqing 400044, China
  • Received:2015-03-04 Online:2015-08-08 Published:2015-08-19

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。

关键词: 肝脏肿瘤分割, 三维ROI, 结合空间信息的模糊聚类, 形态学, 水平集

Abstract:  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 result of test turns out that, the method proposed in this paper gets better result and higher efficiency, which is also easy to operate.

Key words: liver tumor segmentation, 3D ROI, FCMS, morphology, level set

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