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
[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
[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
