A level set method based on local direction gradient for image segmentation with intensity inhomogeneity
Authors: 
Yingran Ma, Yanjun PengAuthors Info & Claims
Pages 30703 - 30727
Abstract
Many medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods have been proposed. Some of them however cannot take advantage of image gradient information. And severe intensity inhomogeneity and weak edges are not disposed properly. To address these problems, a new level set method integrated with local direction gradient information is presented in this paper. Firstly, according to the two assumptions on image intensity inhomogeneity adopted by many existing methods, a new pixel classification model based on image gradient is introduced. Secondly, we employ variational level set method combined with image spatial information, which improves the anti-noise capability of the proposed method. Finally, considering the gray gradients in homogeneous regions are close to constants, an improved diffusion process is incorporated into the level set function to make the evolving curves stay around true image edges. To verify our 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.
References
[1]
Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193---199
[2]
Bresson X, Esedoglu S, Vandergheynst P, Thiran J-P, Osher S (2005) Fast global minimization of the active contours/snake model. Journal of Mathematical Imaging and Vision 28(2):151---167
[3]
Bresson X, Esedoglu S, Vandergheynst P, Thiran J-P, Osher S (2005) Global minimizers of the active contour/snake model. UCLA CAM Report 05---04
[4]
Brox T, Cremers D (2009) On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional. Int J Comput Vis 84(2):184---193
[5]
Caselles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66(1):1---31
[6]
Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61---79
[7]
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266---277
[8]
Chang H, Huang W, Wu C, Huang S, Guan C, Sekar S, Bhakoo KK, Duan Y (2017) A new variational method for bias correction and its applications to rodent brain extraction. IEEE Trans Med Imaging 36(3):721---733
[9]
Chen SC, Zhang DQ (2004) Robust image segmentation using FCM with spatial constrains based on new kernel-induced distance measure. IEEE Transactions on Systems Man Cybernet 34:1907---1916
[10]
Chopp D (1993) Computing minimal surface via level set curvature flow. J Comput Phys 106(1):77---91
[11]
Chuang KS, Tzeng H-L, Chen S, Wu J, Chen T-J (2006) Fuzzy C-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9---15
[12]
Duan Y, Chang H, Huang W, Zhou J, Lu Z, Wu C (2015) The L0 regularized Mumford-Shah model for bias correction and segmentation of medical images. IEEE Trans Image Process. 24(11):3927---3938
[13]
Huang J, You XG, Tang YY et al (2009) A novel iris segmentation using radial-suppression edge detection. Signal Process 89(12):2630---2643
[14]
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321---331
[15]
Konyushkova K, Sznitman R, Fua P (2015) Introducing geometry in active learning for image segmentation. IEEE International Conference on Computer Vision, pp 2974---2982
[16]
Lankton S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Trans Image Process 17(11):2029---2039
[17]
Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: A new variational formulation. IEEE Conf Comput Vis Pattern Recognit 1:430---436
[18]
Li C, Kao C-Y, Gore JC, Ding Z (2007) Implicit active contours driven by local binary fitting energy. IEEE Conference on Computer Vision & Pattern Recognition, pp 1---7
[19]
Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243---3254
[20]
Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007---2016
[21]
Li C, Gore JC, Davatzikos C (2014) Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn Reson Imaging 32(7):913---923
[22]
Likar B, Viergever MA, Pernus F (2001) Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans Med Imaging 20(12):1398---1410
[23]
Liu Y, Nie LQ, Han L et al (2016) Action2activity: recognizing complex activities from sensor data. Proceedings of the twenty-fourth International Joint Conference on Artificial Intelligence (IJCAI), pp 1617---1623
[24]
Liu Y, Nie LQ, Liu L et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108---115
[25]
Liu Y, Zheng Y, Liang YX et al (2016) Urban water quality prediction based on multi-task multi-view learning. Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 2576---2582
[26]
Liu Y, Zhang LM, Nie LQ et al (2016) Fortune teller: predicting your career path. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAAI), pp 201---207
[27]
Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(5):577---685
[28]
Peng D, Merriman B, Osher S, Zhao H, Kang M, PDE-based A (1999) fast local level set method. J Comput Phys 155(2):410---438
[29]
Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM (2001) Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13(5):856---876
[30]
Sussman M, Smereka P, Osher S (1994) A level set approach for computing solutions to incompressible two-phase flow. J Comput Phys 114(1):146---159
[31]
Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vis 50(3):371---293
[32]
Wang LF, Huang J, Lav B et al (2017) MR images segmentation and bias correction via LIC model. IEEE International Conference on Image Processing (ICIP), pp 4412---4416
[33]
Xie X (2010) Active contouring based on gradient vector interaction and constrained level set diffusion. IEEE Trans Image Process 19(1):154---164
[34]
Xu YD, Cui JS, Zhao HJ et al (2013) Tracking generic human motion via fusion of low- and high-dimensional approaches. IEEE Trans Syst Man Cybern B 43(4):996---1002
[35]
Yang Y, Xu D, Nie FP et al (2010) Image clustering using local discriminant models and global integration. IEEE Trans Image Process 19(10):2761---2773
[36]
Yang Y, Ma ZG, Nie FP et al (2015) Multi-class active learning by uncertainty sampling with diversity maximization. Int J Comput Vis 113(2):113---127
[37]
You XG, Peng QM, Yuan Y et al (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn 44(10---11):2314---2324
[38]
Yu SJ, Mou Y, Xu DQ et al (2013) A new algorithm for shoreline extraction from satellite imagery with non-separable wavelet and level set method. International Journal of Machine Learning and Computing 3(1):158---163
[39]
Zhang K, Zhang L, Song H, Zhang D (2013) Reinitialization-free level set evolution via reaction diffusion. IEEE Trans Image Process 22(1):258---270
[40]
Zhang K, Zhang L, Lam K-M, Zhang D (2016) A level set approach to image segmentation with intensity inhomogeneity. IEEE Transactions on Cybernetics 46(2):546---557
[41]
Zhao HK, Chan T, Merriman B et al (1996) A variational level-set approach to multiphase motion. J Comput Phys 127:179---195
Index Terms
- A level set method based on local direction gradient for image segmentation with intensity inhomogeneity
Index terms have been assigned to the content through auto-classification.
Recommendations
A level set method based on local direction gradient for image segmentation with intensity inhomogeneity
AbstractMany medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods have been proposed. Some of them however cannot take advantage of image gradient ...
A Region-Bias Fitting Model based Level Set for Segmenting Images with Intensity Inhomogeneity
ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital MedicineIntensity inhomogeneity is a common phenomenon in real world images, and inevitably leads to many difficulties for accurate image segmentation. This paper proposes a novel region-based model, named Region-Bias Fitting (RBF) model, for segmenting images ...
A survey of level set method for image segmentation with intensity inhomogeneity
AbstractImage segmentation is a fundamental task in computer vision and image processing. Due to the presence of high noise, low resolution and intensity inhomogeneity, it is still a difficult problem in the practical applications. Level set methods have ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 01 December 2018
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 20 Dec 2024