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应用主动轮廓线生长模型的细胞核自动分割 被引量:4

Automatic Segmenting Cell Nucleus with Improved Snake
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摘要 提出了一种改进的主动轮廓线模型应用于细胞核的分割。在利用极限腐蚀检测到每个细胞核的种子点后,以种子点为中心点分别建立一个基于极坐标描述的生长Snake模型,加入了一个基于区域相似度的生长能量,克服了传统模型须将初始轮廓置于真实边界附近的缺点;在应用贪心算法求解时,搜索空间由常规的8邻域减少为径向的两个相邻量化点,提高了计算效率。 This paper presents a novel cell nucleus segmentation method for esophageal cell image. Firstly the ultimate erosion operation is applied to detect the localization of each nucleus and sign it with a seed point. Then the proposed active contour model is built to track the nuclear boundaries. The proposed model describes nuclear contour with sampled points of polar coordinates. A negative constant is added as the growing energy to the energy function at the adjacent point outside when the average intensities difference between the block at this point and the block at the nuclear center point is less than a preset threshold. The growing energy can push the contour points outward. So the initial contour points need not be close to the nucleus boundary. Applying greedy algorithm, the contour points are designed to move from nuclear center to nuclear boundary along the radial directions. So the computation complexity of the model is small.
出处 《计算机工程》 EI CAS CSCD 北大核心 2006年第1期37-39,129,共4页 Computer Engineering
基金 河南省普通科技攻关资助项目"食管贲门癌细胞组织病理学的计算机诊断系统"(0324410092)
关键词 图像分割 细胞图像 主动轮廓线模型 蛇模型 Image segmentation Cell linage Active contour model Snake
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参考文献4

  • 1Lassouaoui N, Hamami L. Genetic Algorithms and Multifractal Segmentation of Cervical Cell Images[C]. France: Proceedings of IEEE-EURASIP Seventh International Symposium on Signal Processing and Its Applications, 2003, 2:1-4.
  • 2Jiang Tianzi, Yang Faguo. An Evolutionary Tabu Search for Cell Image Segmentation[J]. IEEE Transactions on Systems, Man and Cybernetics, 2002, 32(5): 675-678.
  • 3Xu Chenyang, Prince J L. Snakes, Shapes and Gradient Vector Flow[J]. IEEE Transaction on Image Processing, 1998, 7(3): 359-369.
  • 4Williams D, Shab M. A Fast Algorithm for Active Contours and Curvature Estimation[J]. Computer Vision, Graphics and Image Processing: Image Understanding, 1992, 55(1): 14-26.

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