A level-set method based on global and local regions for image segmentation

Document Type

Article

Publication Date

2-1-2012

Abstract

This paper presents a new level-set method based on global and local regions for image segmentation. First, the image fitting term of Chan and Vese (CV) model is adapted to detect the image's local information by convolving a Gaussian kernel function. Then, a global term is proposed to detect large gradient amplitude at the outer region. The new energy function consists of both local and global terms, and is minimized by the gradient descent method. Experimental results on both synthetic and real images show that the proposed method can detect objects in inhomogeneous, low-contrast, and noisy images more accurately than the CV model, the local binary fitting model, and the Lankton and Tannenbaum model. © 2012 World Scientific Publishing Company.

Identifier

84862902437 (Scopus)

Publication Title

International Journal of Pattern Recognition and Artificial Intelligence

External Full Text Location

https://doi.org/10.1142/S021800141255004X

ISSN

02180014

Issue

1

Volume

26

Grant

61172184

Fund Ref

National Science Foundation

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