TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
A Level Set Method to Image Segmentation Based on Local Direction Gradient |
¿µ¹®Á¦¸ñ(English Title) |
A Level Set Method to Image Segmentation Based on Local Direction Gradient |
ÀúÀÚ(Author) |
Jucheng Yang
Lingchao Zhang
Yuan Wang
Tingting Zhao
Wenhui Sun
Dong Sun Park
Yanjun Peng
Yingran Ma
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¿ø¹®¼ö·Ïó(Citation) |
VOL 12 NO. 04 PP. 1760 ~ 1778 (2018. 04) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
For image segmentation with intensity inhomogeneity, many region-based level set methods have been proposed. Some of them however can¡¯t get the relatively ideal segmentation results under the severe intensity inhomogeneity and weak edges, and without use of the image gradient information. To improve that, we propose a new level set method combined with local direction gradient in this paper. Firstly, based on two assumptions on intensity inhomogeneity to images, the relationships between segmentation objects and image gradients to local minimum and maximum around a pixel are presented, from which a new pixel classification method based on weight of Euclidian distance is introduced. Secondly, to implement the model, variational level set method combined with image spatial neighborhood information is used, which enhances the anti-noise capacity of the proposed gradient information based model. Thirdly, a new diffusion process with an edge indicator function is incorporated into the level set function to classify the pixels in homogeneous regions of the same segmentation object, and also to make the proposed method more insensitive to initial contours and stable numerical implementation. To verify our proposed method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.
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Å°¿öµå(Keyword) |
Face Recognition
Weber Symmetric Local Graph Structure
Local Graph Structure
Weber Local Descriptor
Local Binary Pattern
image segmentation
image gradient
level set method
intensity inhomogeneity
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