A Local Type-2 Fuzzy Set Based Technique For He Stain Image Enhancement

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Dibya Jyoti Bora
Rubul Kumar Bania


The proposed approach in the paper comes under “Advanced Soft Computing Based Medical Image Processing Research” and the work has been conducted by Dr. Dibya Jyoti Bora (Assistant Professor), School of Computing Sciences, The Assam Kaziranga University, Jorhat, Assam in the year 2018-2019.

Introduction: HE stain images, although considered as the golden standard for medical image diagnosis, are still found to suffer from poor contrast and degradation in color quality. In this paper, a Type-2 fuzzy set-based enhancement technique is proposed for HE stain image enhancement with special care towards color-based computations and measurements.

Methods: This paper introduces a new approach based on Type-2 fuzzy set for HE stain image enhancement where Bicubic Interpolation plays an important part. Unsharp Masking is also employed as a post enhancement factor.

Results: From the results, it is clearly visible that cell nuclei and other cell bodies are easily distinguishable from each other in the enhanced result produced by our proposed approach. It implies that vagueness in the edges surrounding the objects in the original image is removed to an acceptable level.

Conclusions: The proposed approach is found to be, through both subjective and objective evaluations, an efficient preprocessing technique for a better HE stain image analysis.

Originality: The ideas involved in this paper are original. If work by other researchers are mentioned in any part of the paper, then they are cited properly.

Limitation: The relatively high time complexity is the only limitation associated with the proposed approach.


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How to Cite
Jyoti BoraD., Kumar BaniaR., and NgocC., “A Local Type-2 Fuzzy Set Based Technique For He Stain Image Enhancement”, ing. Solidar, vol. 15, no. 3, pp. 1-22, Sep. 2019.
Research Articles


[1] D. J. Bora, “An Ideal Approach for Medical Color Image Enhancement”, (n.d.)

[2] G. S. Robinson, “Color edge detection,” in Proc. SPIE Symp.Advances Image Transmission Techniques, vol. 87, pp. 126–133, 1976.

[3] M. H. F. Zarandi, and M. Zarinbal, “A new image enhancement method Type-2 Possibilistic C-Mean Approach”, IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 1131-1135, 2013 Joint. doi: http://dx.doi.org/10.1109/IFSA-NAFIPS.2013.6608559

[4] T. Chaira, “Medical image enhancement using intuitionistic fuzzy set,” 1st Inter-national Conference on Recent Advances in Information Technology (RAIT), 2012. doi: http://dx.doi.org/10.1109/rait.2012.6194479.

[5] T. Chaira, “An improved medical image enhancement scheme using Type II fuzzy set,” Applied Soft Computing, 25, pp. 293-308, 2014. doi: http://dx.doi.org/10.1016/j.asoc.2014.09.004

[6] J. Gu, L. Hua, X. Wu, H. Yang, and Z. Zhou, “Color medical image enhancement based on adaptive equalization of intensity numbers matrix histogram,” International Journal of Au-tomation and Computing, vol. 12, pp. 551-558, 2015. doi: http://dx.doi.org/10.1007/s11633-014-0871-9

[7] P. Ensafi, and H. Tizhoosh, “Type-2 Fuzzy Image Enhancement,” Lecture Notes in Computer Science, pp.159-166, 2005.

[8] M. Zarinbal, and M. Fazel Zarandi, “Type-2 fuzzy image enhancement: Fuzzy rule based approach”, 2017. doi: https://doi.org/10.3233/IFS-130902

[9] D. Bora, “An Efficient Innovative Approach Towards Color Image Enhancement,” International Journal of Information Retrieval Research, vol. 8, no. 1, pp.20-37, 2017. [Accessed 1 December 2017]. [Online]. Available from: https://www.igi-global.com/article/an-efficient-innovative-approach-towards-color-image-enhancement/193247

[10] Cytoinformatics.com. (2017). H&E Stain Hematoxylin and Eosin Staining |Digital Pathology Analysis. [Accessed 1 December 2017]. [Online]. Available from: https://cytoinformatics.com/Cyto/he-stain

[11] Wikipedia.org. (2017). H\u0026E stain. [Accessed 1 December 2017]. [Online]. Available from: https://en.wikipedia.org/wiki/H%26E_stain

[12] S. Paxton, M. Adele and Peckham, (2017). The Leeds Histology Guide. Histology.leeds.ac.uk. [Accessed 14 December 2017]. [Online]. Available from: http://histology.leeds.ac.uk/what-is-histology/H_and_E.php

[13] M. Gurcan, L. Boucheron, A. Can, A. Madabhushi, N. Rajpoot, and B. Yener, “Histopathological Image Analysis: A Review,” IEEE Reviews in Biomedical Engineering, vol. 2, pp.147-171, 2009.

[14] Upload.wikimedia.org. (2017). [Accessed 15 December 2017]. [Online]. Available from: https://upload.wikimedia.org/wikipedia/commons/8/86/Emphysema_H_and_E.jpg

[15] D. Bora, “AERSCIEA: An Efficient and Robust Satellite Color Image Enhancement Approach,” Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, 2017.

[16] Stephen Johnson, “Stephen Johnson on Digital Photography,” O'Reilly, 2006.

[17] D. Bora, “Importance of Image Enhancement Techniques In Color Image Segmentation: A Comprehensive And Comparative Study,” Indian J.Sci.Res, vol. 15, no. 1, pp. 115-131, 2017.

[18] R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Transactions On Acoustics, Speech, And Signal Processing, vol. 29, no.6, pp. 1153-1160, 1981. Doi: http://dx.doi.org/10.1109/tassp.1981.1163711

[19] D. Han, “Comparison of Commonly Used Image Interpolation Methods,” Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), China, pp. 1556-1559, 2013.

[20] V. Patel, and K. Mistree, “A Review on Different Image Interpolation Techniques for Image Enhancement,” International Journal of Emerging Technology and Advanced Engineering, vol. 3, no.12, pp. 129-133, 2013.

[21] Z. Zhao, and Y. Zhou, “PLIP based unsharp masking for medical image enhancement” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016. doi: 10.1109/icassp.2016.7471874

[22] Unsharp Mask. (2017). Docs.gimp.org. [Accessed 9 May 2017]. [Online]. Available from: https://docs.gimp.org/en/plug-in-unsharp-mask.html.

[23] Spatial Filters - Gaussian Smoothing. Homepages.inf.ed.ac.uk. [Accessed 9 May 2017]. [Online]. Available from: http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm.

[24] Medicalxpress.com. (2017). Team finds link between backup immune defense, mutation seen in Crohn's disease. [Accessed 16 December 2017]. [Online]. Available from: https://medicalxpress.com/news/2017-07-team-link-backup-immune-defense.html

[25] Pixera.com. (2017). Cite a Website - Cite This For Me. [Accessed 16 December 2017]. [Online]. Available from: http://www.pixera.com/sample-images/users/user-0009.jpg

[26] I. Jafar, and H. Ying, “Multilevel component-based histogram equalization for enhancing the quality of grayscale images” IEEE International Conference on Electro/Information Technology, 2007. doi: http://dx.doi.org/10.1109/eit.2007.4374490.