Automatic MR image segmentation using maximization of mutual information

Abstract

Magnetic resonance (MR) brain image segmentation is an important task for the early detection of any deformation followed by the quantitative analysis for the prediction and stage defection of brain diseases. But segmentation of the MR brain image suffers from limited accuracy as captured images have non-uniform homogeneity over an organ, presence of noise, uneven and broken boundary etc. Due to the complex structure of the brain and varieties of the captured MR images, only a single feature based MR image segmentation cannot give sufficient accurate result. In the proposed method thresholds for segmenting the MR image are computed by maximizing the mutual information for the two features, compactness and homogeneity. The proposed algorithm is tested against the real T1 MR image to asses the accuracy. Further the output is validated and compared with the ground truth and other recently reported works.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Aubert-Broche B, Griffin M, Pike G, Evans A, Collins D (2006) Twenty new digital brain phantoms for creation of validation image data bases. IEEE Trans Med Imaging 25(11):1410–1415

    Article  Google Scholar 

  2. Bouhrara M, Bonny JM, Ashinsky BG, Maring MC, Spencer RG (2017) Noise estimation and reduction in magnetic resonance imaging using a new multispectral nonlocal maximum-likelihood filter. IEEE Trans Med Imaging 36(1):181–193

    Article  Google Scholar 

  3. Bourouis S, Hamrouni K (2010) 3d segmentation of mri brain using level set and unsupervised classification. Int J Image Gr World Scientif 10(1):135–154

    MathSciNet  Article  Google Scholar 

  4. Cheng HD, Sun Y (2000) A hierarchical approach to color image segmentation using homogeneity. IEEE Trans Image Process 9(12):2071–2082

    Article  Google Scholar 

  5. Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan NM, Tao D, Cheng CY, Aung T, Wong TY (2013) Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging 32(6):1019–1032

    Article  Google Scholar 

  6. Dataset: Brainweb dataset. http://www.bic.mni.mcgill.ca/brainweb/. Accessed 29 May 2017

  7. de Moura Frana L, Amigo JM, Cairs C, Bautista M, Pimentel MF (2017) Evaluation and assessment of homogeneity in images. part 1: Unique homogeneity percentage for binary images. Chemom Intell Lab Syst 171:26–39

    Article  Google Scholar 

  8. Fattah SA, Lin CC, Kung SY (2011) A mutual information based approach for evaluating the quality of clustering. In: IEEE International conference on acoustics, speech and signal processing (ICASSP), Prague, pp 601–604

  9. Garcia-Gomez VJ, A-Bayarri A, S-Requena R, Garca AM, M-Bonmat L, Naranjo V (2014) Design, development and implementation of semi-automated cine MR images segmentation pipeline using feature extraction and active contours. In: IEEE-EMBS International conference on biomedical and health informatics (BHI), Valencia, pp 161–164

  10. Ismail M, Mostapha M, Soliman A, Nitzken M, Khalifa F, Elnakib A, Gimel G, Casanova M, El-Baz (2015) A Segmentation of infant brain MR images based on adaptive shape prior and higher-order MGRF. In: IEEE international conference on image processing (ICIP), Quebec City, pp 4327–4331

  11. Jing F, Li M, Zhang HJ, Zhang B (2003) Unsupervised image segmentation using local homogeneity analysis. In: Circuits and systems, 2003. ISCAS ’03. Proceedings of the 2003 international symposium (ISCAS), Bangkok, vol 2, pp II–456–II–459

  12. Kayalvizhi M, Kavitha G, Sujatha CM, Ramakrishnan S (2014) Analysis of alzheimer MR brain images using entropy based segmentation and minkowski functional. In: International conference on informatics, Electronics and Vision (ICIEV), Dhaka, pp 1–6

  13. Lucchi A, Smith K, Achanta R, Knott G, Fua P (2012) Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features. IEEE Trans Med Imaging 31(2):474–486

    Article  Google Scholar 

  14. Mathew J, Mekkayil L, Ramasangu H, Karthikeyan BR, Manjunath AG (2016) Robust algorithm for early detection of alzheimer’s disease using multiple feature extractions. In: IEEE Annual India Conference (INDICON), Bangalore, pp 1–6

  15. Monteiro FC, Campilho AC (2006) Performance evaluation of image segmentation. In: Campilho A, Kamel MS (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg

  16. Montero RS, Bribiesca E (2009) State of the art of compactness and circularity measures. Int Math Forum 4(27):1305–1335

    MathSciNet  MATH  Google Scholar 

  17. Ortiz A, Grriz JM, Ramirez J, Salas-Gonzalez D (2011) MR brain image segmentation by growing hierarchical som and probability clustering. Electron Lett 47(10):585–586

    Article  Google Scholar 

  18. Pal SK, Pal NR (1987) Segmentation using contrast and homogeneity measures. Pattern Recognit Lett 5(4):26–39

    Article  Google Scholar 

  19. Parameshwari DS, Aparna P (2014) An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications. In: 19th International conference on digital signal processing (DSP), Hong Kong, pp 339–344

  20. Qiu C, Xiao J, Yu L, Han L, Iqbal MN (2013) A modified interval type-2 fuzzy c-means algorithm with application in MR image segmentation. Pattern Recognit Lett 34(12):1329–1338

    Article  Google Scholar 

  21. Qiu W, Yuan J, Ukwatta E, Tessier D, Fenster A (2013) Three-dimensional prostate segmentation using level set with shape constraint based on rotational slices for 3d end-firing trus guided biopsy. J Med Phys 40:12–7 (072,903)

    Google Scholar 

  22. Roy A, Maity SP, Maity HK (2014) On maximization of fuzzy entropy for MR image segmentation at compressed sensing. In: First international conference on image processing, Applications and systems conference (IPAS), Sfax, vol. 35, pp 1–6

  23. Siddique I, Bajwa IS, Naveed MS, Choudhary MA (2006) Automatic functional brain MR image segmentation using region growing and seed pixel. In: 2006 ITI 4th International conference on information communications technology, Cairo, pp 1–2

  24. Song H, Bogdan IIM, Wang S, Dong W, Quan W, Dang W, Yu X (2016) Automatic schizophrenia discrimination on fNIRS by using PCA and SVM. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, pp 389–394

  25. Tian Z, Liu L, Zhang Z, Fei B (2016) Superpixel-based segmentation for 3d prostate MR images. IEEE Trans Med Imaging 35(3):791–801

    Article  Google Scholar 

  26. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A (2003) A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging 22(2):137–154

    Article  Google Scholar 

  27. Wang C, Goatman KA, MacGillivray T, Beveridge E, Koutraki Y, Boardman J et al (2015) Automatic multi-parametric MR registration method using mutual information based on adaptive asymmetric K-means binning. In: IEEE 12th international symposium on biomedical imaging (ISBI), New York, pp 1089–1092

  28. Wang H, Xu H, Ahmed SN, Mandai M (2016) Computer aided detection of cavernous malformation in t2-weighted brain MR images. In: IEEE Healthcare innovation point-of-care technologies conference (HI-POCT), Cancun, pp 101–104

  29. Wang L, Chen Y, Pan X, Hong X, Xia D (2010) Level set segmentation of brain magnetic resonance images based on local gaussian distribution fitting energy. J Neurosci Methods Elsevier 188(2):316–325

    Article  Google Scholar 

  30. Wang ZZ, Xiong JJ, Yang YM, Li HX (2017) A flexible and robust threshold selection method. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2017.2719122

    Article  Google Scholar 

  31. Wangn H, Zhang H, Ray N (2013) Adaptive shape prior in graph cut image segmentation. Pattern Recognit 46:1409–1414

    Article  Google Scholar 

  32. Yang SC, He YJ, Wun YJ (2016) Designated target enhancement and segmentation in multi-spectral MR images. In: International symposium on computer, consumer and control (IS3C), pp 1059–1062

  33. Yuan S, Ying W (2015) MR image segmentation algorithm based on non-local fuzzy C-means clustering. In: The 27th Chinese control and decision conference (CCDC), Qingdao, pp 1117–1122

  34. Zhang Y, Li X, Gao X, Zhang C (2016) A simple algorithm of superpixel segmentation with boundary constraint. IEEE Trans Circuits Syst Video Technol 27(7):1502–1514

    Article  Google Scholar 

  35. Zhang Z, Zhang X, Zhang J (2009) Sar image processing based on fast discrete curvelet transform. Inf Technol Appl 3:28–31

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Apurba Roy.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Roy, A., Maity, S.P. Automatic MR image segmentation using maximization of mutual information. Microsyst Technol 27, 341–351 (2021). https://doi.org/10.1007/s00542-018-4031-y

Download citation