Automatic image segmentation is a fundamental and challenging work in image analysis. We present a stochastic contour approach that draws the contour by multiple agents stochastically, each driven by a simple policy. A contour confidence map is formed, and the image is partitioned hierarchically according to the probability of being surrounded by an average contour. The segmentation is formed by truncating the hierarchical tree based on the dissimilarity increment. The average contour formed in the stochastic contour approach no longer depends on the initial conditions and tolerates less guaranteed convergence. The stochastic contour evolution provides perturbation to jump out of local minima, while the average contour handles model uncertainty naturally. No training process is involved in this approach. The experimental evaluation on a large amount of images with diverse visual properties has shown robustness and good performance of our technique.
Proc. SPIE. 6914, Medical Imaging 2008: Image Processing
KEYWORDS: Image segmentation, Expectation maximization algorithms, Neuroimaging, Brain, Image processing algorithms and systems, Tissues, 3D magnetic resonance imaging, Curium, 3D image processing, Medical imaging
This paper presents a novel algorithm of 3D human brain tissue segmentation and classification in magnetic resonance image (MRI) based on region restricted EM algorithm (RREM).
The RREM is a level set segmentation method while the evolution of the contours was driven by the force field composed by the probability density functions of the Gaussian models.
Each tissue is modeled by one or more Gaussian models restricted by free shaped contour so that the Gaussian models are adaptive to the local intensities.
The RREM is guaranteed to be convergency and achieving the local minimum.
The segmentation avoids to be trapped in the local minimum by the split and merge operation.
A fuzzy rule based classifier finally groups the regions belonging to the same tissue and forms the segmented 3D image of white matter (WM) and gray matter (GM) which are of major interest in numerous applications.
The presented method can be extended to segment brain images with tumor or the images having part of the brain removed with the adjusted classifier.