A Hybrid Method Based on Fuzzy Clustering and Local Region-Based Level Set for Segmentation of Inhomogeneous Medical Images

  • Maryam Rastgarpour
  • Jamshid Shanbehzadeh
  • Hamid Soltanian-Zadeh
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

Keywords

Medical image segmentation Level set Fuzzy clustering Intensity inhomogeneity Automatic segmentation 

Abbreviations

CT

Computed tomography

CV

Chan-Vese

FCM

Fuzzy C-Means

FTC

Fast two cycle

FLICM

Fuzzy local information c-means

KFCM

Kernel-based fuzzy c-means

LBF

Local binary fitting level set evolution

LSEBFE

level set evolution with bias field estimation

MIS

medical image segmentation

MRI

magnetic resonance imaging

MSE

mean square error

MSSD

mean sum of square distance

ROI

region of interest

SCR

segmentation in chest radiographs

SFCM

spatial fuzzy C-means

WM

White matter

Notes

Ethical standards

The authors declare that the experiments comply with the current laws of the country in which we were performed.

Conflict of interest

The authors have no actual or potential conflict of interest including any financial, personal, or other relationships with other people or organizations to disclose.

References

  1. 1.
    Faisal, A., Parveen, S., Badsha, S., Sarwar, H., and Reza, A. W., Computer assisted diagnostic system in tumor radiography. Journal of medical systems 37(3):1–10, 2013.CrossRefGoogle Scholar
  2. 2.
    Jiang, J., Trundle, P., and Ren, J., Medical image analysis with artificial neural networks. computerized medical imaging and graphics 34(8):617–631, 2010.PubMedCrossRefGoogle Scholar
  3. 3.
    Wang, S., and Summers, R. M., Machine learning and radiology. Medical image analysis 16(5):933–951, 2012. doi: 10.1016/j.media.2012.02.005.PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Rastgarpour, M., and Shanbehzadeh, J., The problems, applications and growing interest in automatic segmentation of medical images from the year 2000 till 2011. International Journal of Computer Theory and Engineering (IJCTE) 5(1):1–4, 2013.CrossRefGoogle Scholar
  5. 5.
    Kannan, S., Ramathilagam, S., Devi, P., and Sathya, A., Improved fuzzy clustering algorithms in segmentation of dc-enhanced breast mri. Journal of medical systems 36(1):321–333, 2012.PubMedCrossRefGoogle Scholar
  6. 6.
    Cai, W., Chen, S., and Zhang, D., Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition 40(3):825–838, 2007.CrossRefGoogle Scholar
  7. 7.
    Ziyan U, Tuch D, Westin C-F, Segmentation of thalamic nuclei from DTI using spectral clustering. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI. Springer, pp 807–814, 2006Google Scholar
  8. 8.
    Bijari, P. B., Akhondi-Asl, A., and Soltanian-Zadeh, H., Three-dimensional coupled-object segmentation using symmetry and tissue type information. computerized medical imaging and graphics 34(3):236–249, 2010.PubMedCrossRefGoogle Scholar
  9. 9.
    Paragios, N., A level set approach for shape-driven segmentation and tracking of the left ventricle. Medical Imaging, IEEE Transactions on 22(6):773–776, 2003.CrossRefGoogle Scholar
  10. 10.
    Suri, J. S., Liu, K., Singh, S., Laxminarayan, S. N., Zeng, X., and Reden, L., Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. Information Technology in Biomedicine, IEEE Transactions on 6(1):8–28, 2002.CrossRefGoogle Scholar
  11. 11.
    Li, B. N., Chui, C. K., Chang, S., and Ong, S., Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine 41(1):1–10, 2011.PubMedCrossRefGoogle Scholar
  12. 12.
    Reddy G, Ramudu K, Zaheeruddin S, Rao R Image segmentation using kernel fuzzy c-means clustering on level set method on noisy images. In: Communications and Signal Processing (ICCSP), 2011 International Conference on. IEEE, pp 522–526, 2011Google Scholar
  13. 13.
    Saikumar, T., Shashidhar, B., Harshavardhan, V., and Rani, K. S., Mri brain image segmentation algorithm using watershed transform and kernel fuzzy c-means clustering on level set method. International Journal on Computer Science and Engineering 3(4):1591–1598, 2011.Google Scholar
  14. 14.
    Wu, Y., Hou, W., and Wu, S., Brain MRI segmentation using KFCM and Chan-Vese model. Transactions of Tianjin University 17(3):215–219, 2011.CrossRefGoogle Scholar
  15. 15.
    Bhadauria, H., Singh, A., and Dewal, M., An integrated method for hemorrhage segmentation from brain CT Imaging. Computers & Electrical Engineering, Elsevier 39(5):1527–1536, 2013.CrossRefGoogle Scholar
  16. 16.
    Gao S, Yang J, Yan Y., A novel multiphase active contour model for inhomogeneous image segmentation. Multimedia Tools and Applications, Springer:1–17, 2013Google Scholar
  17. 17.
    Vovk, U., Pernus, F., and Likar, B., A review of methods for correction of intensity inhomogeneity in MRI. Medical Imaging, IEEE Transactions on 26(3):405–421, 2007.CrossRefGoogle Scholar
  18. 18.
    Szilágyi, L., Szilágyi, S. M., Benyó, B., and Benyó, Z., Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models. Biomedical Signal Processing and Control 6(1):3–12, 2011.CrossRefGoogle Scholar
  19. 19.
    Zheng Q, Dong EQ (2012) New local segmentation model for images with intensity inhomogeneity. Optical Engineering 51 (3):037006-037001-037006-037010Google Scholar
  20. 20.
    He, C., Wang, Y., and Chen, Q., Active contours driven by weighted region-scalable fitting energy based on local entropy. Signal Processing, Elsevier 92(2):587–600, 2012.CrossRefGoogle Scholar
  21. 21.
    Likar, B., Viergever, M. A., and Pernus, F., Retrospective correction of MR intensity inhomogeneity by information minimization. Medical Imaging, IEEE Transactions on 20(12):1398–1410, 2001.CrossRefGoogle Scholar
  22. 22.
    Foruzan AH, Kalantari Khandani I, Baradaran Shokouhi S., Segmentation of brain tissues using a 3-D multi-layer Hidden Markov Model. Computers in Biology and Medicine 43 (2):121–130. doi: 10.1016/j.compbiomed.2012.11.001, 2013
  23. 23.
    Ghasemi J, Ghaderi R, Karami Mollaei MR, Hojjatoleslami SA., A novel fuzzy Dempster–Shafer inference system for brain MRI segmentation. Information Sciences 223 (0):205–220. doi: 10.1016/j.ins.2012.08.026, 2013
  24. 24.
    Chao W-H, Lai H-Y, Shih Y-YI, Chen Y-Y, Lo Y-C, Lin S-H, Tsang S, Wu R, Jaw F-S., Correction of inhomogeneous magnetic resonance images using multiscale retinex for segmentation accuracy improvement. Biomedical Signal Processing and Control 7 (2):129–140. doi: 10.1016/j.bspc.2011.04.001, 2012
  25. 25.
    Abbas Q, Celebi ME, Garcı́a IF., Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system. Biomedical Signal Processing and Control 8 (2):204–214. doi: 10.1016/j.bspc.2012.08.003, 2013
  26. 26.
    Rastgarpour, M., and Shanbehzadeh, J., Automatic medical image segmentation by integrating kfcm clusteringand level set based ftc model. IAENG Transactions on Electrical Engineering, Special Issue of the International MultiConference of Engineers and Computer Scientists 1:257–270, 2013. doi: 10.1142/9789814439084_0020.Google Scholar
  27. 27.
    Zadeh, L. A., Fuzzy sets. Information and control 8(3):338–353, 1965.CrossRefGoogle Scholar
  28. 28.
    Pham, D. L., and Prince, J. L., An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognition Letters 20(1):57–68, 1999.CrossRefGoogle Scholar
  29. 29.
    EtehadTavakol, M., Sadri, S., and Ng, E. Y. K., Application of k- and fuzzy c-means for color segmentation of thermal infrared breast images. Journal of medical systems 34(1):35–42, 2010. doi: 10.1007/s10916-008-9213-1.PubMedCrossRefGoogle Scholar
  30. 30.
    Kande, G., Subbaiah, P. V., and Savithri, T. S., Unsupervised fuzzy based vessel segmentation in pathological digital fundus images. Journal of medical systems 34(5):849–858, 2010. doi: 10.1007/s10916-009-9299-0.PubMedCrossRefGoogle Scholar
  31. 31.
    Moon, W. K., Lo, C.-M., Goo, J. M., Bae, M. S., Chang, J. M., Huang, C.-S., Chen, J.-H., Ivanova, V., and Chang, R.-F., Quantitative analysis for breast density estimation in low dose chest ct scans. Journal of medical systems 38(3):1–9, 2014.CrossRefGoogle Scholar
  32. 32.
    Krinidis, S., and Chatzis, V., A robust fuzzy local information c-means clustering algorithm. IEEE Transactions on Image Processing 19(5):1328–1337, 2010.PubMedCrossRefGoogle Scholar
  33. 33.
    Bezdek, J. C., Pattern recognition with fuzzy objective function algorithms. Publishers, Kluwer Academic, 1981.CrossRefGoogle Scholar
  34. 34.
    Tolias, Y. A., and Panas, S. M., Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 28(3):359–369, 1998.CrossRefGoogle Scholar
  35. 35.
    Noordam J, Van den Broek W, Buydens LM Geometrically guided fuzzy c-means clustering for multivariate image segmentation. In: 15th International Conference on Pattern Recognition. IEEE, pp 462–465, 2000Google Scholar
  36. 36.
    Pham DL Fuzzy clustering with spatial constraints. In: International Conference on Image Processing. IEEE, pp 65–68, 2002Google Scholar
  37. 37.
    Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., and Moriarty, T., A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. Medical Imaging, IEEE Transactions on 21(3):193–199, 2002.CrossRefGoogle Scholar
  38. 38.
    Chen, S., and Zhang, D., Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(4):1907–1916, 2004.CrossRefGoogle Scholar
  39. 39.
    Szilagyi L, Benyo Z, Szilágyi SM, Adam H MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: Engineering in Medicine and Biology Society. Proceedings of the 25th Annual International Conference of the IEEE, 2003. IEEE, pp 724–726, 2003Google Scholar
  40. 40.
    Ji, Z.-X., Sun, Q.-S., and Xia, D.-S., A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image. computerized medical imaging and graphics 35(5):383–397, 2011.PubMedCrossRefGoogle Scholar
  41. 41.
    Krinidis, S., and Chatzis, V., A robust fuzzy local information c-means clustering algorithm. Image Processing, IEEE Transactions on 19(5):1328–1337, 2010.CrossRefGoogle Scholar
  42. 42.
    Bandyopadhyay S, Saha S., Clustering Algorithms. In: Unsupervised Classification. Springer Berlin Heidelberg, pp 75–92. doi: 10.1007/978-3-642-32451-2, 2013
  43. 43.
    Li, C., Huang, R., Ding, Z., Gatenby, J., Metaxas, D., and Gore, J., A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri. Image Processing, IEEE Transactions on 99:1–1, 2011.CrossRefGoogle Scholar
  44. 44.
    Malladi, R., Sethian, J. A., and Vemuri, B. C., Shape modeling with front propagation: a level set approach. Pattern Analysis and Machine Intelligence, IEEE Transactions on 17(2):158–175, 1995.CrossRefGoogle Scholar
  45. 45.
    Caselles, V., Kimmel, R., and Sapiro, G., Geodesic active contours. International Journal Of Computer Vision 22(1):61–79, 1997.CrossRefGoogle Scholar
  46. 46.
    Chan, T. F., and Vese, L. A., Active contours without edges. Image Processing, IEEE Transactions on 10(2):266–277, 2001.CrossRefGoogle Scholar
  47. 47.
    Li, C., Xu, C., Gui, C., and Fox, M. D., Distance regularized level set evolution and its application to image segmentation. Image Processing, IEEE Transactions on 19(12):3243–3254, 2010.CrossRefGoogle Scholar
  48. 48.
    Li, C., Kao, C. Y., Gore, J. C., and Ding, Z., Minimization of region-scalable fitting energy for image segmentation. Image Processing, IEEE Transactions on 17(10):1940–1949, 2008.CrossRefGoogle Scholar
  49. 49.
    Bernard, O., Friboulet, D., Thévenaz, P., and Unser, M., Variational B-spline level-set: a linear filtering approach for fast deformable model evolution. Image Processing, IEEE Transactions on 18(6):1179–1191, 2009.CrossRefGoogle Scholar
  50. 50.
    Hou Z., A review on MR image intensity inhomogeneity correction. International Journal of Biomedical Imaging 2006Google Scholar
  51. 51.
    Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., K-i, K., Matsui, M., Fujita, H., Kodera, Y., and Doi, K., Development of a digital image database for chest radiographs with and without a lung nodule receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. American Journal of Roentgenology 174(1):71–74, 2000.PubMedCrossRefGoogle Scholar
  52. 52.
    Van Ginneken, B., Stegmann, M. B., and Loog, M., Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical image analysis 10(1):19–40, 2006.PubMedCrossRefGoogle Scholar
  53. 53.
    Dietenbeck T, Alessandrini M, Friboulet D, Bernard O CREASEG: a free software for the evaluation of image segmentation algorithms based on level-set. In: Image Processing (ICIP), 17th IEEE Int. Conf. on Hong Kong. IEEE, pp 665–668. doi: 10.1109/ICIP.2010.5652991, 2010
  54. 54.
    Lankton, S., and Tannenbaum, A., Localizing region-based active contours. Image Processing, IEEE Transactions on 17(11):2029–2039, 2008.CrossRefGoogle Scholar
  55. 55.
    Shi, Y., and Karl, W. C., A real-time algorithm for the approximation of level-set-based curve evolution. Image Processing, IEEE Transactions on 17(5):645–656, 2008.CrossRefGoogle Scholar
  56. 56.
    Zhang, D. Q., and Chen, S. C., A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. artificial intelligence in medicine 32(1):37–50, 2004.PubMedCrossRefGoogle Scholar
  57. 57.
    Chuang, K. S., Tzeng, H. L., Chen, S., Wu, J., and Chen, T. J., Fuzzy c-means clustering with spatial information for image segmentation. computerized medical imaging and graphics 30(1):9–15, 2006.PubMedCrossRefGoogle Scholar
  58. 58.
    Dice, L. R., Measures of the amount of ecologic association between species. Ecology 26(3):297–302, 1945.CrossRefGoogle Scholar
  59. 59.
    Rote, G., Computing the minimum Hausdorff distance between two point sets on a line under translation. Information Processing Letters 38(3):123–127, 1991.CrossRefGoogle Scholar
  60. 60.
    Duquette, A. A., Jodoin, P.-M., Bouchot, O., and Lalande, A., 3D segmentation of abdominal aorta from CT-scan and MR images. computerized medical imaging and graphics 36(4):294–303, 2012.PubMedCrossRefGoogle Scholar
  61. 61.
    Li C, Xu C, Konwar KM, Fox MD Fast distance preserving level set evolution for medical image segmentation. In: 9th International Conference on Control, Automation, Robotics and Vision (ICARCV’06). IEEE, pp 1–7, 2006Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Maryam Rastgarpour
    • 1
  • Jamshid Shanbehzadeh
    • 2
  • Hamid Soltanian-Zadeh
    • 3
    • 4
  1. 1.Department of Computer Engineering, Faculty of Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer Engineering, Faculty of EngineeringKharazmi University (TarbiatMoallem University)TehranIran
  3. 3.Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
  4. 4.Image Analysis LabRadiology Department, Henry Ford Health SystemDetroitUSA

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