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A Novel Model-Based Approach for Medical Image Segmentation Using Spatially Constrained Inverted Dirichlet Mixture Models

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Abstract

In this paper, we present a novel statistical approach to medical image segmentation. This approach is based on finite mixture models with spatial smoothness constrains. The main advantages of the proposed approach can be summarized as follows. Firstly, the proposed model is based on inverted Dirichlet mixture models, which have demonstrated better performance in modeling positive data (e.g., images) than Gaussian mixture models. Secondly, we integrate spatial relationships between pixels with the inverted Dirichlet mixture model, which makes it more robust against noise and image contrast levels. Finally, we develop a variational Bayes method to learn the proposed model, such that the model parameters and model complexity (i.e., the number of mixture components) can be estimated simultaneously in a unified framework. The performance of the proposed approach in medical image segmentation is compared with some state-of-the-art segmentation approaches through various numerical experiments on both simulated and real medical images.

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Notes

  1. http://www.bic.mni.mcgill.ca/brainweb/.

  2. http://www.nitrc.org/projects/ibsr/.

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

    Article  Google Scholar 

  2. Alfò M, Nieddu L, Vicari D (2008) A finite mixture model for image segmentation. Stat Comput 18(2):137–150

    Article  MathSciNet  Google Scholar 

  3. Ashburner J, Friston KJ (2005) Unified segmentation. NeuroImage 26(3):839–851

    Article  Google Scholar 

  4. Attias H (1999) A variational Bayes framework for graphical models. In: Proceedings of of Advances in Neural Information Processing Systems (NIPS), pp 209–215

  5. Bdiri T, Bouguila N (2012) Positive vectors clustering using inverted Dirichlet finite mixture models. Expert Syst Appl 39(2):1869–1882

    Article  Google Scholar 

  6. Bdiri T, Bouguila N (2013) Bayesian learning of inverted Dirichlet mixtures for SVM kernels generation. Neural Comput Appl 23(5):1443–1458

    Article  Google Scholar 

  7. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

    MATH  Google Scholar 

  8. Blei DM, Jordan MI (2005) Variational inference for Dirichlet process mixtures. Bayesian Anal 1:121–144

    Article  MathSciNet  MATH  Google Scholar 

  9. Blekas K, Likas A, Galatsanos NP, Lagaris IE (2005) A spatially constrained mixture model for image segmentation. IEEE Trans Neural Netw 16(2):494–498

    Article  Google Scholar 

  10. Celeux G, Forbes F, Peyrard N (2003) EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recogn 36(1):131–144

    Article  MATH  Google Scholar 

  11. Chatzis SP, Varvarigou TA (2008) A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation. IEEE Trans Fuzzy Syst 16(5):1351–1361

    Article  Google Scholar 

  12. Choi HS, Haynor DR, Kim Y (1991) Partial volume tissue classification of multichannel magnetic resonance images—a mixel model. IEEE Trans Med Imaging 10(3):395–407

    Article  Google Scholar 

  13. Cocosco CA, Kollokian V, Kwan RKS, Pike GB, Evans AC (1997) BrainWeb: Online interface to a 3D MRI simulated brain database. NeuroImage 5:425

    Google Scholar 

  14. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17(3):463–468

    Article  Google Scholar 

  15. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  16. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  17. Fan W, Bouguila N, Ziou D (2012) Variational learning for finite Dirichlet mixture models and applications. IEEE Trans Neural Netw Learn Syst 23(5):762–774

    Article  Google Scholar 

  18. Forbes F, Peyrard N (2003) Hidden Markov random field model selection criteria based on mean field-like approximations. IEEE Trans Pattern Anal Mach Intell 25(9):1089–1101

    Article  Google Scholar 

  19. Hong C, Yu J, Tao D, Wang M (2015a) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742–3751

    Google Scholar 

  20. Hong C, Yu J, Wan J, Tao D, Wang M (2015b) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670

    Article  MathSciNet  Google Scholar 

  21. Hong C, Yu J, You J, Chen X, Tao D (2015c) Multi-view ensemble manifold regularization for 3D object recognition. Inf Sci 320:395–405

    Article  MathSciNet  Google Scholar 

  22. Kwan RKS, Evans AC, Pike GB (1996) An extensible MRI simulator for post-processing evaluation. In: Proceedings of the 4th international conference on visualization in biomedical computing (VBC’96), pp 135–140

  23. Kwan RKS, Evans AC, Pike GB (1999) MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans Med Imaging 18(11):1085–1097

    Article  Google Scholar 

  24. Li C, Kao CY, Gore JC, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949

    Article  MathSciNet  MATH  Google Scholar 

  25. Luque-Baena RM, Ortiz-de Lazcano-Lobato JM, López-Rubio E, Palomo EJ (2013) A competitive neural network for multiple object tracking in video sequence analysis. Neural Process Lett 37(1):47–67

    Article  Google Scholar 

  26. McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York

    Book  MATH  Google Scholar 

  27. Mentzelopoulos M, Psarrou A, Angelopoulou A, García-Rodríguez J (2013) Active foreground region extraction and tracking for sports video annotation. Neural Process Lett 37(1):33–46

    Article  Google Scholar 

  28. Nasios N, Bors AG (2006) Variational learning for Gaussian mixture models. IEEE Trans Syst Man Cybern B Cybern 36(4):849–862

    Article  Google Scholar 

  29. Nguyen N, Wu QMJ, Ahuja S (2010) An extension of the standard mixture model for image segmentation. IEEE Trans Neural Netw 21(8):1326–1338

    Article  Google Scholar 

  30. Nguyen TM, Wu QMJ (2012) Robust student’s-t mixture model with spatial constraints and its application in medical image segmentation. IEEE Trans Med Imaging 31(1):103–116

    Article  Google Scholar 

  31. Nikou C, Galatsanos NP, Likas AC (2007) A class-adaptive spatially variant mixture model for image segmentation. IEEE Trans Image Process 16(4):1121–1130

    Article  MathSciNet  Google Scholar 

  32. Nikou C, Likas AC, Galatsanos NP (2010) A bayesian framework for image segmentation with spatially varying mixtures. IEEE Trans Image Process 19(9):2278–2289

    Article  MathSciNet  MATH  Google Scholar 

  33. Pham DL (2001) Spatial models for fuzzy clustering. Comput Vis Image Underst 84(2):285–297

    Article  MATH  Google Scholar 

  34. Robert C, Casella G (1999) Monte Carlo statistical methods. Springer, Berlin

    Book  MATH  Google Scholar 

  35. Salembier P, Garrido L (2000) Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans Image Process 9(4):561–576

    Article  Google Scholar 

  36. Sanjay GS, Hebert TJ (1998) Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm. IEEE Trans Image Process 7(7):1014–1028

    Article  Google Scholar 

  37. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  38. Tiao GG, Cuttman I (1965) The inverted Dirichlet distribution with applications. J Am Stat Assoc 60(311):793–805

    Article  MathSciNet  MATH  Google Scholar 

  39. Titterington DM, Smith AFM, Makov UE (1985) Statistical analysis of finite mixture distributions. Wiley, Hoboken

    MATH  Google Scholar 

  40. Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272

    Article  MathSciNet  MATH  Google Scholar 

  41. Yu J, Tao D, Li J, Cheng J (2014) Semantic preserving distance metric learning and applications. Inf Sci 281:674–686

    Article  MathSciNet  Google Scholar 

  42. Zhang Y, Brady M, Smith S (2001) Segmentation of brain mr images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57

    Article  Google Scholar 

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Acknowledgements

The completion of this work was supported by the National Natural Science Foundation of China (61502183, 61673186), and the Scientific Research Funds of Huaqiao University (600005- Z15Y0016).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Wentao Fan, Can Hu & Jixiang Du

  2. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada

    Nizar Bouguila

Authors
  1. Wentao Fan
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  2. Can Hu
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  3. Jixiang Du
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  4. Nizar Bouguila
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Correspondence to Wentao Fan.

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Fan, W., Hu, C., Du, J. et al. A Novel Model-Based Approach for Medical Image Segmentation Using Spatially Constrained Inverted Dirichlet Mixture Models. Neural Process Lett 47, 619–639 (2018). https://doi.org/10.1007/s11063-017-9672-9

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  • DOI: https://doi.org/10.1007/s11063-017-9672-9

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