Original article: The Lee-Seo model with regularization term for bimodal image segmentation

Authors:
Qiao Xin;Chunlai Mu;Meng Li
Affiliations:
-;-;-
Venue:
Mathematics and Computers in Simulation
Year:
2011

Citing 3
Cited 0

Image Sequence Analysis via Partial Differential Equations

Journal of Mathematical Imaging and Vision
Active contours without edges

IEEE Transactions on Image Processing
Level set-based bimodal segmentation with stationary global minimum

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: In this paper, we improve Lee-Seo' bimodal image segmentation model using a regularization term. This regularization term will maintain the smoothness of the level set function and decrease the level set function' oscillations around the desired steady state when the noise level is lager. Furthermore, we also provide a rigorous study of the modified model. Based on techniques in calculus of variations, the existence of solutions of the modified model in BV space is established. Based on the theory we present (see Lemmas 2 and 3), we constructed a fast convergent algorithm to process images. It turns out our method is twice fast in processing an image than Lee-Seo's algorithm with the same constant value initial level set function.