원문정보
초록
영어
Level set methods have been widely used in image processing specially in image segmentation. This paper presents a new region based active contour model in a variational level set formulation for segmentation of real world images in the presence of intensity in-homogeneity and noise. In this paper, we derive a local intensity clustering property in the image domain with better distance regularization function. The level set methods sometimes develop irregularities during its evolution state, which may cause numerical complexity and destroy the stability of evolution. This distance regularization function is able to maintain the desired shape of level set function smoothly and eliminates the need of re-initialization of LSF. The local clustering criterion function is defined for image intensities in neighborhood of each point. Now, this local clustering criterion of point is then integrated with respect to the neighborhood of entire points for global clustering criterion of image segmentation. In which bias function is also evaluated to intensity inhomogeneity correction. Implementation of our method shows that, it is more robust to initialization, and more accurate than conventional model.
목차
1. Introduction
2. Literature Survey
2.1. Mumford–Shah Functional Model
2.2. Chan-Vese’s Model
2.3. Local Intensity Clustering Method
3. Variational Level Set Framework with Reformed Potential Function
3.1 Energy Formulation
3.2. Reformed Potential Function for Regularization Term
3.3. Minimization of Energy using Gradient Descent Flow
4. Implementation and Experiment
4.1 Numerical Implementation
4.2. Performance Evaluation:
4.3. Experiment Result
5. Conclusion
References