基于改进水平集的肝脏CT图像分割方法  

Liver CT image segmentation method based on modified level set

作　　者：陈英[1] 王静 段喜龙 CHEN Ying;WANG Jing;DUAN Xi-long(School of Software,Nanchang Hangkong University,Nanchang 330063,China)

机构地区：[1]南昌航空大学软件学院,江西南昌330063

出　　处：《传感器与微系统》2018年第10期44-46,共3页Transducer and Microsystem Technologies

基　　金：江西省自然科学基金资助项目(20161BAB212034);江西省教育厅项目(GJJ160692);国家自然科学基金资助项目(61501217;61662049)

摘　　要：针对测地线主动轮廓(GAC)法和Shi水平集分割方法存在的缺点,提出适用肝脏CT图像的改进策略。采用图像预处理技术实现算法初始曲线的自动化;利用加权中值和平均梯度改进GAC算法、引入Poisson函数改进Shi算法;采用连通域提取技术、图像填充技术以及部分手动的方式得到分割后肝脏区域的标的图像。基于峰值信噪比、交互信息和分割错误率对分割效果进行评价的实验结果表明:改进后的水平集方法不仅有效地提高了肝脏区域分割的准确率,也降低了算法的时间复杂度。Aiming at shortcomings of the geodesic active contour（ GAC） and Shi level set segmentation methods,an improved strategy for liver CT images is proposed. Image preprocessing technology is used to realize automation of initial curve of algorithm. Use weighted median and average gradient to improve the GAC algorithm,and introduce the Poisson function to improve the Shi algorithm. The ground truth image of the liver region after segmentation is obtained by using the connected domain extraction technique,the image filling technique and partial manual method. Results of the experiment,based on peak signal-to-noise ratio,interactive information and segmentation error rate to evaluate the segmentation effect,show that the improved level set method not only effectively enhances the accuracy of liver region segmentation,but also reduces the time complexity of the algorithm.

关 键 词：肝脏CT图像 改进水平集 图像分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]