Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy.
This paper presents a variational level set approach in a multi-phase formulation to segmentation of brain magnetic resonance (MR) images with intensity inhomogeneity. In our model, the local image intensities are characterized by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with level set functions and local means and variances as variables. The means and variances of local intensities are considered as spatially varying functions. Therefore, our method is able to deal with intensity inhomogeneity without inhomogeneity correction. Our method has been applied to 3T and 7T MR images with promising results.
Wang
Li
L
School of Computer Science & Technology, Nanjing University of Science and Technology, Nanjing 210094, China. li.wang8401@gmail.com
Chen
Yunjie
Y
Pan
Xiaohua
X
Hong
Xunning
X
Xia
Deshen
D
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation / Q000191:economics; D003365:Costs and Cost Analysis; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D011336:Probability; D012984:Software