Title :
Robust K-means based active contours for fast inhomogeneity image segmentation
Author :
Zhihui Hao;Xiaozhen Xie;Qianying Zhang
Author_Institution :
College of Science, Northwest A&F University, Yangling 712100, PR China
Abstract :
A novel robust K-means based active contours model is proposed to segment medical images with various noise and intensity inhomogeneities. Relying on the correntropy-based image features, the model uses the local adaptive weights to be robust to various noises. Moreover, the combination of information in the global and the local regions ensures that our approach is extremely hard to trap into a local minimum. To avoid the reinitialization and shorten the computational time, we use the signed distance functions to regularize the level set functions, and adopt the iteratively re-weighted method to accelerate our algorithm during the contour evolution. Experimental results show that our algorithm can fast achieve the robust segmentation results in the presence of the intensity inhomogeneities, various noise and blur.
Keywords :
"Image segmentation","Mathematical model","Robustness","Nonhomogeneous media","Biomedical imaging","Computational modeling","Level set"
Conference_Titel :
Image and Signal Processing (CISP), 2015 8th International Congress on
DOI :
10.1109/CISP.2015.7407929