Image Segmentation with Detail Preserving Based on HMRF Using Neighborhood Selection

doi:10.3969/j.issn.1006-2475.2017.10.004

Abstract

Abstract: Because of the filtering effect of the Markov random fields (MRF) region label model, detail structures may be preserved partially or lost entirely for synthetic aperture radar (SAR) image. The hidden MRF (HMRF) image segmentation approach with adaptive neighborhood systems based on scattering descriptor is proposed to better preserve detail features and border areas and to improve the segmentation effect. In order to improve the reliability and the adaptivity, fuzzy c-means (FCM) clustering algorithm is incorporated into scattering transform, where the choice of neighborhood shapes can be implemented adaptively. From among the different shape alternatives, the one with the highest fuzzy membership is chosen to compute the HMRF region label process. Experiment results demonstrate that the proposed algorithm improves the segmentation effect over the conventional HMRF using fixed shapes of neighborhood systems while detail structures are preserved well.

Key words: image segmentation, hidden Markov random fields model, scattering descriptor, neighbourhood selection, fuzzy c-means

TIAN Qin-yi 1, TIAN Xiao-lin 2. Image Segmentation with Detail Preserving Based on HMRF Using Neighborhood Selection[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.10.004.

References

［1］ Cross G R, Jain A K. Markov random field texture models［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983,5(1):25-39.

［2］ Derin H, Elliott H. Modeling and segmentation of noisy and textured images using Gibbs random fields［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987,9(1):39-55.

［3］ Deng Huawu, Clausi D A. Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005,43(3):528-538.

［4］ Ghamisi P, Benediktsson J A, Ulfarsson M O. Spectral-spatial classification of hyperspectral images based on hidden Markov random fields［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(5):2565-2574.

［5］ Desrosiers C. An unsupervised random walk approach for the segmentation of brain MRI［C］// Proceedings of IEEE International Conference on Biomedical Imaging. 2014:337-340.

［6］ Basak K, Dey G, Sheet D, et al. Probabilistic graphical modeling of speckle statistics in laser speckle contrast imaging for noninvasive and label-free retinal angiography［C］// Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015:6244-6247.

［7］ Smits P C, Dellepiane S G. Synthetic aperture radar image segmentation by a detail preserving Markov random field approach［J］. IEEE Transactions on Geoscience and Remote Sensing, 1997,35(4):844-857.

［8］ Ghamisi P, Benediktsson J A, Ulfarsson M O. Spectral-spatial classification of hyperspectral images based on hidden Markov random fields［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(5):2565-2574.

［9］ Olding W C, Olivier J C, Salmon B P. A Markov random field model for decision level fusion of multi-source image segments［C］// Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. 2015:2385-2388. 

［10］Yang Xi, Gao Xinbo, Tao Dacheng, et al. An efficient MRF embedded level set method for image segmentation［J］. IEEE Transactions on Image Processing, 2015,24(1):9-21.

［11］Lowe D G. Distinctive image features from scale-invariant keypoints［J］. International Journal of Computer Vision, 2004,60(2):91-110.

［12］Bruna J, Mallat S. Classification with scattering operators［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2011:1561-1566.

［13］Bruna J, Mallat S. Invariant scattering convolution networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(8):1872-1886.

［14］Mallat S. Group invariant scattering［J］. Communications on Pure and Applied Mathematics, 2012,65(10):1331-1398.

［15］Bruna J. Scattering representations for pattern and texture recognition［D］. PhD thesis, CMAP, Ecole Polytechnique, 2012.

［16］Alan B. Handbook of Video and Image Processing［M］. 2nd edition, Academic Press, 2005.

［17］ Shen Shan, Sandham W, Granat M, et al. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization［J］. IEEE Transactions on Information Technology in Biomedicine, 2005,9(3):459-467.

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