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Medical image segmentation using rough set and local polynomial regression

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Abstract

Rough-set based multimodal histogram thresholding technique is effective for medical image segmentation. However, it is difficult to obtain the significant peaks and valleys of the roughness measure. Moreover, it is sensitive to the noise for medical image. In this paper, we proposed a new medical image segmentation method using rough set theory and local polynomial regression model to address those disadvantages. Firstly, compute histogram of image intensity information and histon of image intensity and spatial information. Secondly, use the local polynomial regression model to smooth the histogram and histon. The smoothed histogram correlates with lower approximation and the smoothed histon correlates with upper approximation. Lastly, rough measure is calculated with the lower and upper approximations. And then, multimodal thresholding method is applied to medical image segmentation. The local polynomial regression model can obtain a smooth rough measure and has two advantages: first, it is easy to find the real peaks and valleys of the smoothed roughness measure to segment medical image; second, the local polynomial regression reduces the effect of noise and can find the thresholds correctly. The proposed approach is compared with the histogram based approach, histon based approach, and rough set with the histogram and histon based approach. Experimental results demonstrate that our approach can find the real peaks and valleys more easily and yields better segmentation results than those of other three methods.

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References

  1. Aubert-Broche B, Griffin M, Pike GB et al (2006) 20 new digital brain phantoms for creation of validation image data bases. IEEE Trans Med Imaging 25:1410–1416

    Article  Google Scholar 

  2. Beaubouef T, Petry F (2001) Vagueness in spatial data: Rough set and egg-yolk approaches. In: 2001 Industrial and Engineering Applications of Artificial Intelligence and Expert Systems — IEA/AIE, pp 367–373

  3. Bonnet N, Cutrona J, Herbin M (2002) A ‘no-threshold’ histogram-based image segmentation method. Pattern Recogn 35:2319–2322

    Article  MATH  Google Scholar 

  4. Bowman W, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-plus illustrations. Oxford statistical science series. Clarendon, New York

    Google Scholar 

  5. Bricq S, Collet CH, Armspach JP (2008) Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains. Med Image Anal 12:639–652

    Article  Google Scholar 

  6. Brink AD (1995) Minimum spatial entropy threshold selection. IEE Proc Vision Image Sig Process 142:128–132

    Article  Google Scholar 

  7. Cai WL, Chen SC, Zhang DQ (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40:825–838

    Article  MATH  Google Scholar 

  8. Cheng HD, Jiang XH, Wang J (2002) Color image segmentation based on homogram thresholding and region merging. Pattern Recogn 35:373–393

    Article  MATH  Google Scholar 

  9. Clive L (1999) Local regression and likelihood. Springer, New York

    MATH  Google Scholar 

  10. Divyendu S, Phillip L (2004) A rough set-based approach to handling spatial uncertainty in binary images. Eng Appl Artif Intell 17:97–110

    Article  Google Scholar 

  11. Ferreira da Silva AR (2007) A Dirichlet process mixture model for brain MRI tissue classification. Med Image Anal 11:169–182

    Article  Google Scholar 

  12. Ghanizadeh A, Abarghouei AA, Sinaie S (2011) Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata. Appl Opt 50:3191–3200

    Article  Google Scholar 

  13. Gonzalez RC, Woods RE (2002) Digital image processing. Prentice Hall, New Jersey

    Google Scholar 

  14. Hassanien AE (2007) Fuzzy rough sets hybrid scheme for breast cancer detection. Image Vision Comput 25:172–183

    Article  Google Scholar 

  15. Hirano S, Tsumoto S (2005) Rough representation of a region of interest in medical images. Int J Approx Reason 40:23–34

    Article  Google Scholar 

  16. Jiang JF, Yang DQ, Wei HE (2008) Image segmentation based on rough set theory and neural networks. In: 5th International Conference on Visual Information Engineering, pp 361–365

  17. Karoui I, Fablet R, Boucher J, Augustin J (2010) Variational region-based segmentation using multiple texture statistics. IEEE Trans Image Process 19:3146–3156

    Article  MathSciNet  Google Scholar 

  18. Khang ST, NorAshidi M (2011) Color image segmentation using histogram thresholding Fuzzy C-means hybrid approach. Pattern Recogn 44:1–15

    Article  MATH  Google Scholar 

  19. Maji P, Kundu MK, Chanda B (2006) Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix. In: The Proceedings of the IET International Conference on Visual Information Engineering, pp 327–332

  20. Maji P, Pal SK (2007) Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans Syst Man Cybern B Cybern 37:1529–1540

    Article  Google Scholar 

  21. Milind MM, Ajoy KR (2008) Color image segmentation: rough-set theoretic approach. Pattern Recogn Lett 29:483–493

    Article  Google Scholar 

  22. Mohabey A, Ray AK (2000a) Rough set theory based segmentation of color images. In: 19th Internat. Conf. North Amer. Fuzzy Inform. Process. Soc. (NAFIPS), pp 338–342

  23. Mohabey A, Ray AK (2000b) Fusion of rough set theoretic approximations and FCM for color image segmentation.In: 2000 I.E. Internat. Conf. Systems Man Cybernet, pp 1529–1534

  24. Orlando J, Tobias RS (2002) Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans Image Process 11:1457–1465

    Article  Google Scholar 

  25. Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11:341–356

    Article  MATH  MathSciNet  Google Scholar 

  26. Ridler TW, Calvard S (1978) Picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern 8:630–632

    Article  Google Scholar 

  27. Sen D, Pal SK (2006) Image segmentation using global and local fuzzy statistics. In: Proceedings of the 2006 Annual IEEE India Conference, pp 1–6

  28. Sen D, Pal SK (2009) Histogram thresholding based on fuzzy and rough measures of association error. IEEE Trans Image Process 18:879–888

    Article  MathSciNet  Google Scholar 

  29. Sen D, Pal SK (2009) Generalized rough sets, entropy, and image ambiguity measures. IEEE Trans Syst Man Cybern B Cybern 39:117–128

    Article  Google Scholar 

  30. Shan ZY, Yue GH, Liu JZ (2002) Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. NeuroImage 17:1587–1598

    Article  Google Scholar 

  31. Souplet JC, Lebrun C, Ayache N et al (2008) An automatic segmentation of T2-FLAIR multiple sclerosis lesions. In: Grand Challenge Work: Mult. Scler. Lesion Segm. Challenge, pp 1–11

  32. Tan KS, Isa NAM (2011) Color image segmentation using histogram thresholding—Fuzzy C-means hybrid approach. Pattern Recogn 44:1–15

    Article  MATH  Google Scholar 

  33. Wendy LM, Angel RM (2002) Computational statistics handbook with matlab. Chapman& Hall/Crc, NewYork

    Google Scholar 

  34. Wojcik ZM (1994) Application of rough sets for edge enhancing image filters. In: 1994 I.E. International Conference on Image Processing, pp 525–529

  35. Wojcik ZM, Wojcik BE (1996) Structural modeling using rough sets. In: 5th IEEE International Conference on Fuzzy Systems, pp 761–766

  36. Wu Z (2001) Research on remote sensing image classification using neural network based on rough sets. In: 2001 Proceedings of the Info-tech and Info-net International Conferences, pp 279–284

  37. Zijdenbos AP, Dawant BM, Margolin RA et al (1994) Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging 13:716–724

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to express their sincere thanks to the editor and all reviewers who made great contributions to the improvement of the final paper. This work is supported by the Natural Science Foundation of Jiangsu Province in China (No. BK2012209 and No. BK20130529).

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Correspondence to Cong-Hua Xie.

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Xie, CH., Liu, YJ. & Chang, JY. Medical image segmentation using rough set and local polynomial regression. Multimed Tools Appl 74, 1885–1914 (2015). https://doi.org/10.1007/s11042-013-1723-2

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