Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

(1) Muhammad Arhami Mail (Politeknik Negeri Lhokseumawe, Indonesia)
(2) * Anita Desiani Mail (Universitas Sriwijaya, Indonesia)
(3) Sugandi Yahdin Mail (Universitas Sriwijaya, Indonesia)
(4) Ajeng Islamia Putri Mail (Universitas Sriwijaya)
(5) Rifkie Primartha Mail (Universitas Sriwijaya, Indonesia)
(6) Husaini Husaini Mail (Politeknik Negeri Lhokseumawe, Indonesia)
*corresponding author

Abstract


Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels.

Keywords


Segmentation; Image Enchancement; Otsu Thresholding; Top-Hat Transformation; Blood Vessels; Retina;

   

DOI

https://doi.org/10.26555/ijain.v8i2.779
      

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References


[1] T. A. Soomro et al., “Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation,” Electronics, vol. 10, no. 18, pp. 1–19, Sep. 2021, doi: 10.3390/electronics10182297.

[2] S. Gayathri, V. P. Gopi, and P. Palanisamy, “Automated classification of diabetic retinopathy through reliable feature selection,” Phys. Eng. Sci. Med., vol. 43, no. 3, pp. 927–945, Sep. 2020, doi: 10.1007/s13246-020-00890-3.

[3] R. Rajalakshmi, V. Prathiba, S. Arulmalar, and M. Usha, “Review of retinal cameras for global coverage of diabetic retinopathy screening,” Eye, vol. 35, no. 1, pp. 162–172, Jan. 2021, doi: 10.1038/s41433-020-01262-7.

[4] M. Shahid and I. A. Taj, “Retracted: Robust Retinal Vessel Segmentation using Vessel’s Location Map and Frangi Enhancement Filter,” IET Image Process., vol. 12, no. 4, pp. 494–501, Apr. 2018, doi: 10.1049/iet-ipr.2017.0457.

[5] K. BahadarKhan, A. A Khaliq, and M. Shahid, “A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding,” PLoS One, vol. 11, no. 7, pp. 1–19, Jul. 2016, doi: 10.1371/journal.pone.0158996.

[6] Z. Shen, H. Fu, J. Shen, and L. Shao, “Modeling and Enhancing Low-Quality Retinal Fundus Images,” IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 996–1006, Mar. 2021, doi: 10.1109/TMI.2020.3043495.

[7] F. Tian, Y. Li, J. Wang, and W. Chen, “Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology,” Comput. Math. Methods Med., vol. 2021, pp. 1–11, May 2021, doi: 10.1155/2021/4761517.

[8] J. Ma, X. Fan, S. X. Yang, X. Zhang, and X. Zhu, “Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement,” Int. J. Pattern Recognit. Artif. Intell., vol. 32, no. 07, p. 1854018, Jul. 2018, doi: 10.1142/S0218001418540186.

[9] C. G. Ravichandran and J. B. Raja, “A Fast Enhancement/Thresholding Based Blood Vessel Segmentation for Retinal Image Using Contrast Limited Adaptive Histogram Equalization,” J. Med. Imaging Heal. Informatics, vol. 4, no. 4, pp. 567–575, Aug. 2014, doi: 10.1166/jmihi.2014.1289.

[10] O. Ramos-Soto et al., “An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering,” Comput. Methods Programs Biomed., vol. 201, pp. 1–13, Apr. 2021, doi: 10.1016/j.cmpb.2021.105949.

[11] R. Kushol, M. H. Kabir, M. S. Salekin, and A. B. M. A. Rahman, “Contrast Enhancement by Top-Hat and Bottom-Hat Transform with Optimal Structuring Element: Application to Retinal Vessel Segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10317, 2017, pp. 533–540. doi: 10.1007/978-3-319-59876-5_59

[12] F. Siddique, T. Iqbal, S. M. Awan, Z. Mahmood, and G. Z. Khan, “A Robust Segmentation of Blood Vessels in Retinal Images,” in 2019 International Conference on Frontiers of Information Technology (FIT), 2019, pp. 83–835, doi: 10.1109/FIT47737.2019.00025.

[13] S. Bharkad, “Automatic segmentation of blood vessels in retinal image using morphological filters,” in Proceedings of the 6th International Conference on Software and Computer Applications - ICSCA ’17, 2017, pp. 132–136, doi: 10.1145/3056662.3056710.

[14] E. Erwin, S. Saparudin, and W. Saputri, “Hybrid multilevel thresholding and improved harmony search algorithm for segmentation,” Int. J. Electr. Comput. Eng., vol. 8, no. 6, pp. 4593–4602, 2018, doi: 10.11591/ijece.v8i6.pp4593-4602.

[15] F. Bukenya, L. Bai, and A. Kiweewa, “A Review of Blood Vessel Segmentation Techniques,” in 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), 2018, pp. 1–10, doi: 10.1109/CAIS.2018.8441989.

[16] O. Ali, N. Muhammad, Z. Jadoon, B. M. Kazmi, N. Muzamil, and Z. Mahmood, “A Comparative Study of Automatic Vessel Segmentation Algorithms,” in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2020, pp. 1–6, doi: 10.1109/iCoMET48670.2020.9074073.

[17] H. Wang, Y. Jiang, X. Jiang, J. Wu, and X. Yang, “Automatic vessel segmentation on fundus images using vessel filtering and fuzzy entropy,” Soft Comput., vol. 22, no. 5, pp. 1501–1509, Mar. 2018, doi: 10.1007/s00500-017-2872-4.

[18] K. B. Shaik, P. Ganesan, V. Kalist, B. S. Sathish, and J. M. M. Jenitha, “Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space,” Procedia Comput. Sci., vol. 57, pp. 41–48, 2015, doi: 10.1016/j.procs.2015.07.362.

[19] H. A. Nugroho, T. Lestari, R. A. Aras, and I. Ardiyanto, “Segmentation of retinal blood vessels using Gabor wavelet and morphological reconstruction,” in 2017 3rd International Conference on Science in Information Technology (ICSITech), 2017, pp. 513–516, doi: 10.1109/ICSITech.2017.8257166.

[20] A. Desiani, B. Suprihatin, S. Yahdin, A. I. Putri, and F. R. Husein, “Bi-path architecture of CNN segmentation and classification method for cervical cancer fisorders based on pap-smear images,” IAENG Int. J. Comput. Sci., vol. 48, no. 3, pp. 782–791, 2021. Available at: Google Scholar.

[21] Sonali, S. Sahu, A. K. Singh, S. P. Ghrera, and M. Elhoseny, “An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE,” Opt. Laser Technol., vol. 110, pp. 87–98, Feb. 2019, doi: 10.1016/j.optlastec.2018.06.061.

[22] P. Singh, R. Mukundan, and R. De Ryke, “Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization,” J. Digit. Imaging, vol. 33, no. 1, pp. 273–285, Feb. 2020, doi: 10.1007/s10278-019-00211-5.

[23] Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, “Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction,” IEEE Access, vol. 6, pp. 11782–11792, 2018, doi: 10.1109/ACCESS.2018.2797872.

[24] S. H. Majeed and N. A. M. Isa, “Iterated Adaptive Entropy-Clip Limit Histogram Equalization for Poor Contrast Images,” IEEE Access, vol. 8, pp. 144218–144245, 2020, doi: 10.1109/ACCESS.2020.3014453.

[25] Z. Jiang, J. Yepez, S. An, and S. Ko, “Fast, accurate and robust retinal vessel segmentation system,” Biocybern. Biomed. Eng., vol. 37, no. 3, pp. 412–421, 2017, doi: 10.1016/j.bbe.2017.04.001.

[26] S. Pal, S. Chatterjee, D. Dey, and S. Munshi, “Morphological operations with iterative rotation of structuring elements for segmentation of retinal vessel structures,” Multidimens. Syst. Signal Process., vol. 30, no. 1, pp. 373–389, Jan. 2019, doi: 10.1007/s11045-018-0561-9.

[27] K. Kipli et al., “Morphological and Otsu’s Thresholding-Based Retinal Blood Vessel Segmentation for Detection of Retinopathy,” Int. J. Eng. Technol., vol. 7, no. 3.18, p. 16, Aug. 2018, doi: 10.14419/ijet.v7i3.18.16665.

[28] W. - and Y. Palgunadi, “Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 6, pp. 417–422, 2019, doi: 10.14569/IJACSA.2019.0100654.

[29] F. Krüger, “Activity, Context, and Plan Recognition with Computational Causal Behaviour Models,” University of Rostock, 2016. Available at: Google Scholar.

[30] S. Yahdin, A. Desiani, A. Amran, D. Rodiah, and Solehan, “Pattern recognation for study period of student in Mathematics Department with C4.5 algorithm data mining technique at the Faculty of Mathematics and Natural Science Universitas Sriwijaya,” J. Phys. Conf. Ser., vol. 1282, no. 1, pp. 1–6, Jul. 2019, doi: 10.1088/1742-6596/1282/1/012014.

[31] A. Desiani, N. R. Dewi, A. N. Fauza, N. Rachmatullah, M. Arhami, and M. Nawawi, “Handling Missing Data Using Combination of Deletion Technique, Mean, Mode and Artificial Neural Network Imputation for Heart Disease Dataset,” Sci. Technol. Indones., vol. 6, no. 4, pp. 303–312, Oct. 2021, doi: 10.26554/sti.2021.6.4.303-312.

[32] A. Desiani, S. Yahdin, A. Kartikasari, and I. Irmeilyana, “Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment,” IAES Int. J. Artif. Intell., vol. 10, no. 2, pp. 346–354, Jun. 2021, doi: 10.11591/ijai.v10.i2.pp346-354.

[33] Erwin and H. R. Damayanti, “Supervised Retinal Vessel Segmentation Based Average Filter and Iterative Self Organizing Data Analysis Technique,” Int. J. Comput. Intell. Appl., vol. 20, no. 01, pp. 1–13, Mar. 2021, doi: 10.1142/S1469026821500036.

[34] T. Chakraborti, D. K. Jha, A. S. Chowdhury, and X. Jiang, “A self-adaptive matched filter for retinal blood vessel detection,” Mach. Vis. Appl., vol. 26, no. 1, pp. 55–68, Jan. 2015, doi: 10.1007/s00138-014-0636-z.




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