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作 者:全美霖 刘奇[1] 陈曦[2] 邓小波 何柯辰 刘艳丽[3] Quan Meilin;Liu Qi;Chen Xi;Deng Xiaobo;He Kechen;Liu Yanli(College of Biomedical Engineering,Sichuan University,Chengdu 610065,Sichuan Province,China;College of Electrical Engineering,Sichuan University,Chengdu 610065,Sichuan Province,China;Department of Biomedical Engineering,Chengde Medical College,Chengde 067000,Hebei Province,China)
机构地区:[1]四川大学生物医学工程学院,四川省成都市610065 [2]四川大学电气工程学院,四川省成都市610065 [3]承德医学院生物医学工程系,河北省承德市067000
出 处:《中国组织工程研究》2023年第2期171-176,共6页Chinese Journal of Tissue Engineering Research
摘 要:背景:肾脏CT图像质量较差且腹腔CT图像中肾脏与周围组织灰度相似,用传统的图像分割方法难以准确分割出肾脏。目的:提出一种改进的测地线活动轮廓模型,辅助肾脏疾病的诊断,提高CT图像中肾脏分割的精度。方法:在对比分析多种传统医学图像分割算法的基础上,设计了基于改进测地线活动轮廓模型的肾脏分割算法,根据先验知识勾画出感兴趣区域,在预处理阶段中获得肾脏的初始轮廓;再以水平集方法中的测地线活动轮廓模型为基础,增强肾脏区域的边界响应并采用改进边缘指示函数,使轮廓曲线的演化结果更接近真实目标边界。结果与结论:在328张二维肾脏CT图像上的平均Dice系数为0.9749,平均重叠度系数为0.9071,相较于其他水平集方法有所提高。实验结果表明,改进的测地线活动轮廓模型可以提高腹腔CT图像中肾脏区域的分割精度及分割效率。BACKGROUND:Kidney CT image with poor quality shows similar gray scale to that of surrounding tissues on abdominal CT images.Therefore,it is difficult to segment the kidney accurately by traditional image segmentation method.OBJECTIVE:To assist the diagnosis of renal diseases and improve the accuracy of renal segmentation in CT images based on an improved geodesic active contour model.METHODS:Based on the comparative analysis of various traditional medical image segmentation algorithms,a kidney segmentation algorithm based on the improved geodesic active contour model was designed.The region of interest was delineated according to prior knowledge,and the initial contour of the kidney was obtained during the pretreatment stage.Based on the geodesic active contour model of the level set method,the boundary response of the kidney region was enhanced and the improved edge indicator function was used to make the contour curve evolution result closer to the real target boundary.RESULTS AND CONCLUSION:The mean Dice coefficient and mean overlap degree of 328 two-dimensional CT images of the kidney were 0.9749 and 0.9071,respectively,which were improved compared with other level set methods.Experimental results show that this model can improve the segmentation accuracy and efficiency of the kidney region in abdominal CT images.
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