title: Minimum mutual information based level set clustering algorithm for fast MRI tissue segmentation.
abstract: Accurate and accelerated MRI tissue recognition is a crucial preprocessing for real-time 3d tissue modeling and medical diagnosis. This paper proposed an information de-correlated clustering algorithm implemented by variational level set method for fast tissue segmentation. The key idea is to design a local correlation term between original image and piecewise constant into the variational framework. The minimized correlation will then lead to de-correlated piecewise regions. Firstly, by introducing a continuous bounded variational domain describing the image, a probabilistic image restoration model is assumed to modify the distortion. Secondly, regional mutual information is introduced to measure the correlation between piecewise regions and original images. As a de-correlated description of the image, piecewise constants are finally solved by numerical approximation and level set evolution. The converged piecewise constants automatically clusters image domain into discriminative regions. The segmentation results show that our algorithm performs well in terms of time consuming, accuracy, convergence and clustering capability.
authors.forename: Shuanglu
authors.initials: S
authors.lastname: Dai
authors.forename: Hong
authors.initials: H
authors.lastname: Man
authors.forename: Shu
authors.initials: S
authors.lastname: Zhan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D016000:Cluster Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D011336:Probability; D012984:Software