摘要
结合CV模型和RSF模型,建立CV-RSF模型,实现甲状腺结节超声图像半自动分割算法。基于CV模型的全局信息对图像粗分割,并以粗分割结果作为RSF模型的初始轮廓,然后再利用RSF模型的局部信息对病灶实现最终的分割。根据设定的两组不同初始轮廓,分别利用RSF模型和CV-RSF模型对病灶分割。结果表明,CV-RSF模型解决了RSF模型对初始轮廓敏感的问题,而且通过重叠率的对比,CV-RSF模型分割更准确。对比RSF模型,CV-RSF模型实现的甲状腺结节超声图像半自动分割算法,更加有效、准确。
To establish a CV-RSF model based on CV model and RSF model,and to realize semi-automatic segmentation algorithm of thyroid nodules.Based on the global information of the CV model,the image was roughly segmented,and the rough segmentation result was used as the initial contour of the RSF model.Then the local information of the RSF model was used to achieve the final segmentation of the lesion.According to the set of different initial contours,the lesions were segmented by RSF model and CV-RSF model respectively.The results showed that the CV-RSF model solved the problem that the RSF model was sensitive to the initial contour,by comparing the overlap ratio,the CV-RSF model segmentation was more accurate.Compared with the RSF model,the semi-automatic segmentation algorithm of thyroid nodules with CV-RSF model is more effective and accurate.
作者
邵蒙恩
严加勇
崔崤峣
于振坤
SHAO Mengen;YAN Jiayong;CUI Xiaoyao;YU Zhenkun(University of Shanghai for Science and Technology,Shanghai 200093,China;Zhoupu Hospital affiliated to Shanghai University of Medical&Health Science,Shanghai 201318;Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou 215163,China;Tongren Hospital of Nanjing,Nanjing 211102,China)
出处
《生物医学工程研究》
2019年第3期336-340,共5页
Journal Of Biomedical Engineering Research
基金
江苏省省级重点研发专项资金资助项目(BE2017601)
上海市浦东新区科技发展基金民生科研专项医疗卫生项目(PKJ2017-Y41)
