激光与光电子学进展, 2017, 54 (2): 021702, 网络出版: 2017-02-10
改进Chan-Vese模型的肝癌消融CT图像肿块分割方法 下载: 556次
A Tumor Segmentation Method of Improved Chan-Vese Model for Liver Cancer Ablation Computed Tomography Image
医用光学 Chan-Vese模型 计算机断层扫描图像 消融 肿瘤分割 medical optics Chan-Vese model computed tomography image ablation tumor segmentation
摘要
针对肝癌消融计算机断层扫描(CT)图像分割中肿块区域存在成分多变和弱边界问题, 为准确提取肝肿块轮廓, 提出了一种改进Chan-Vese模型的水平集算法。利用肝与肿块的高斯均值、标准差有显著差异的特点, 通过高斯混合模型区分目标与背景的像素隶属, 结合边缘梯度信息驱动的长度与形状约束项构造能量泛函, 以肿块先验知识确定目标的初始轮廓, 促使活动轮廓收敛在目标区域边缘。通过肝CT图像实验数据集验证算法, 实现肝上已灭活或部分灭活的癌组织和碘油沉积等构成复杂轮廓提取, 实验结果表明, 算法平均相似度值大于0.87, 其周密性与精确度均优于局部Chan-Vese和局部二值拟合模型。
Abstract
With respect to varied components and weak edge in tumor areas during the segmentation of computed tomography (CT) image of liver cancer ablation, a level set algorithm using the improved Chan-Vese model was proposed to accurately extract the contour of the hepatic tumor. According to significant difference of Gaussian mean and standard deviation between liver and tumor, the Gaussian mixture model was used to distinguish the subjection of pixels between target and background, and the bound terms of length and shape of edge gradient were combined to construct energy functions. The priori knowledge of tumor was applied to determining the initial profile of target, so that the active contour can converge on the edge of target area. By virtue of the verification algorithm of experimental data set of liver CT image, it is feasible to extract irregular contour of components such as inactivated or partially inactivated carcinoma tissues and iodized oil accumulation in liver. Experimental results showed that the average similarity value of our approach was higher than 0.87, the accuracy and precision of the improved algorithm were better than those of local Chan-Vese and local binary fitting models.
谢志南, 郑东, 陈嘉耀, 洪国斌. 改进Chan-Vese模型的肝癌消融CT图像肿块分割方法[J]. 激光与光电子学进展, 2017, 54(2): 021702. Xie Zhinan, Zheng Dong, Chen Jiayao, Hong Guobin. A Tumor Segmentation Method of Improved Chan-Vese Model for Liver Cancer Ablation Computed Tomography Image[J]. Laser & Optoelectronics Progress, 2017, 54(2): 021702.