Application of Markov Random Field in the Retinal Vessel Segmentation

Abstract:

Some diseases, particularly cardiovascular disease, will change the shape and structure of retinal vessels. Observation and detection of retinal vessels play an important role in the diagnosis of diseases. Traditional diagnosis of the retinal vessels that ophthalmologist perform under artificial visual attending. Image segmentation based on Markov random field is a method based on statistical theory, which takes into account the correlation between the local pixels, uses the prior knowledge effectively, has fewer model parameters and is easy to be combined with other methods etc., so this method has been widely researched and applied in the field of image segmentation. This paper which mainly studied the Markov random field is how to specific apply to image segmentation, and the iterated conditional mode and the traditional segmentation (clustering) algorithm segmented and compared in the medical retinal vessel image. The method of MRF can effectively restrain the noise in the vessel segmentation.

• Periodical: Applied Mechanics and Materials (Volume 696)
• Pages: 114-118
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.696.114
• Online since: November 2014
• Authors: Wen Long Yin*, Hong Song Li, Hao Ran Zhang, Shu Ting Zhao
• Keywords: C-Means Segmentation, Iterated Conditional Mode, MAP-MRF Estimation, Markov Random Field, Retinal Vessel Segmentation

References

[1] Wen Hangyu. MRF-based image segmentation algorithm and the application for change detection . nanjing: Nanjing University of Science and Technology. (2013).

[2] Xu Di. Research on image segmentation applications of Markov random field . haerbin: Harbin Institute of Technology. (2013).

[3] Stan Z. Li. Markov Random Field Modeling in Image Analysis. Springer London Ltd, 3rd ed, (2009).

[4] Xu Rong. Application of Markov random field theory in the field of image segmentation. nanjing: Southeast University. (2009).

[5] Li Xuchao. Summary methods of Markov random field in image segmentation. Journal of Image and Graphics, 2007, 12(5).

[6] Zhang Xuegong. Pattern Recognition. Tsinghua University Press, 3rd ed, (2010).

[7] Sun Yifeng. A new fast fuzzy C-means clustering algorithm for image segmentation. Journal of Chinese Computer Systems, 2008, 29(2) : 320-323.

[8] Yannis A. Tolias, Stavros M. Panas. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Transactions on Medical Imaging, 1998, 17(2): 263-273.

DOI: 10.1109/42.700738

[9] GIBI, B. K., SATYA, S. T. Segmentation of Vessels in Fundus Images using Spatially Weighted Fuzzy c-Means Clustering Algorithm. International Journal of Computer Science and Network Security, 2007, 7(12): 102-109.

[10] Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing. Publishing House of Electronics Industry, 3rd ed, (2011).