医学图像分割研究思路
医学图像分割的主流方法之一是基于水平集(Level Set)的分割方法。目前针对主流的分割方法,我们主体研究思路如下图 在模型凸化以及形状先验两个方面,未开展相关工作。 参考文献(部分演示代码-参数随图像需要调整): [7] Xiaomeng Xin,Lingfeng Wang,Chunhong Pan,Shigang Liu:Adaptive regularization level set evolution for medical image segmentation and bias field correction. International Conference on Image Processing 2015: 1006-1010 [6] Lingfeng Wang,Explicit Order Model for Region-based Level Set Segmentation,International Conference on Acoustics,Speech,and Signal Processing,2015 [5] Lingfeng Wang(Corresponding Author),Robust Level Set Image Segmentation via a Local Correntropy-based K-means Clustering,PatternRecognition,2014 PR_Code.rar [4] Lingfeng Wang(Corresponding Author),Image Guided Regularization Level Set Evolution for MR Image Segmentation and Bias Field Correction,Magnetic Resonance Imaging,2013 [3] Lingfeng Wang(Corresponding Author),Huaiyu Wu,Region-based Image Segmentation with Local Signed Difference Energy,Pattern RecognitionLetters,2013 PRLetters_Code.zip [2] Lingfeng Wang(Corresponding Author),Zeyun Yu,A Unified Level Set Framework Utilizing Parameter Priors for Medical Image Segmentation,Science China (Series F),1-14,2012 (This is the extension version of ACCV 2010) ACCV_CHINA_Science_Segmentation.zip [1] Ying Wang,Shiming Xiang,Level Set Evolution with Locally Linear Classification for Image Segmentation,International Conference on Image Processing,2011 (The entension version is accepted by Pattern Recognition) from:http://www.escience.cn/people/LingfengWang/medical_image_segmentation.html (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |
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