Title :
A variational level set method of multiphase segmentation for piecewise smooth images
Author :
Yao, Jun-sheng ; Pan, Zhen-Kuan ; Wei, Wei-Bo ; Li, Hua ; Huang, Jian-guo
Author_Institution :
Sch. of Marine Eng., NorthWestern Polytech. Univ., Xi´´an, China
Abstract :
A variational level set method of multiphase segmentation for piecewise smooth images is presented in this paper. n-1 level set functions are used to partition n regions using n new characteristic functions via Heaviside functions without overlapping and vacuum problems. The energy functional includes three parts. The first part is a parametric region-based model via generic image noise distributions, but the parameters are not constant in different homogeneous regions to describe the smooth properties based on TV model, the second part is classic edge-based model, the third part is terms used to enforce the constraints of level set functions as signed distance functions. The estimation of parameters is realized through solving a set of PDEs using some concepts of image inpainting, which is different from the method in literatures. The evolution equations of level set functions are solved by a semi-implicit scheme. Some numerical examples for segmentation of synthetic and real images are presented to validate the method suggested in this paper.
Keywords :
image segmentation; partial differential equations; piecewise linear techniques; set theory; variational techniques; Heaviside functions; TV model; classic edge-based model; generic image noise distribution; image inpainting; multiphase segmentation; n-1 level set function; parametric region-based model; piecewise smooth images; real image segmentation; semiimplicit scheme; signed distance function; synthetic image segmentation; variational level set method; Cybernetics; Difference equations; Educational institutions; Gaussian distribution; Gaussian noise; Image segmentation; Level set; Machine learning; Parameter estimation; TV; Level Set Method; Multiphase segmentation; Piecewise smooth images; Variational Method;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212316