Improved local Gaussian distribution fitting energy model for image segmentation

Shengming Fan; Lixiong Liu; Lejian Liao

doi:10.1117/12.2244858

29 August 2016 Improved local Gaussian distribution fitting energy model for image segmentation

Shengming Fan, Lixiong Liu, Lejian Liao

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003316 (2016) https://doi.org/10.1117/12.2244858
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Image segmentation is one of the most important parts of image processing. Several segmentation models have been proposed during study for recent decades. However noise, low contrast, and intensity inhomogeneity on images are still big challenges for image segmentation. Thus this paper presents an improved segmentation method based on well-known local Gaussian distribution fitting (LGDF) model. We first apply automatic initialization based on simple threshold segmentation to dealing with the drawback that LGDF model is sensitive to initialization position. Then we utilize result of effective and efficient Canny edge detector to get noteworthy edge information and after further processing we gain an edge field. The edge field is used to reduce the probability of local minima on regions far from true boundaries and to force evolving curve to snap to target boundaries. The experimental results demonstrate the advantages of our method on not only medical and synthetic images but also some natural images.

Citation Download Citation

Shengming Fan, Lixiong Liu, and Lejian Liao "Improved local Gaussian distribution fitting energy model for image segmentation", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003316 (29 August 2016); https://doi.org/10.1117/12.2244858

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KEYWORDS

Image segmentation

Medical imaging

Motion models

Sensors

Image processing

Performance modeling

Binary data

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