Combination Contrast Stretching and Adaptive Thresholding for Retinal Blood Vessel Image
Abstract
To diagnose diabetic retinopathy is to segment the blood vessels of the retinal, but the retinal images in the DRIVE and STARE datasets have varying contrast, so the enhancement is needed to obtain a stable image contrast. In this study, image enhancement was performed using the Contrast Stretching and continued with segmentation using the Adaptive Thresholding on retinal images. The image that has been extracted with green channels will be enhanced with Contras Stretching and segmented with Adaptive Thresholding to produce a binary image of retinal blood vessels. The purpose of this study was to combine image enhancement techniques and segmentation methods to obtain valid and accurate retinal blood vessels. The test results on DRIVE were 95.68 for accuracy, 65.05% for sensitivity, and 98.56% for specificity. The test results of Adam Hoover’s ground truth on STARE were 96.13% for, 65.90% for sensitivity, and 98.48% for specificity. The test results for Valentina Kouznetsova’s ground truth on the STARE were 93.89% for accuracy, 52.15% for sensitivity, and 99.02% for specificity. The conclusion obtained is that the processing results on the DRIVE and STARE datasets are very good with respect to their accuracy and specificity values. This method still needs to be developed to be able to detect thin blood vessels with the aim of being able to improve and increase the sensitivity value obtained.
Downloads
References
Ensemble Approach for Diabetic Retinopathy Detection,” IEEE Access, vol. 7, pp. 150 530–150 539, 2019.
[2] K. Naveed, F. Abdullah, H. A. Madni, M. A. Khan, T. M. Khan, and S. S. Naqvi, “Towards Automated Eye Diagnosis: An
Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3d Filter,” Diagnostics, vol. 11, no. 1, pp.
1–27, 2021.
[3] Murinto, S. Winiarti, D. P. Ismi, and A. Prahara, “Image enhancement using piecewise linear contrast stretch methods based
on unsharp masking algorithms for leather image processing,” Proceeding - 2017 3rd International Conference on Science in
Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017,
vol. 2018-Janua, pp. 669–673, 2017.
[4] E. Erwin and D. R. Ningsih, “Improving Retinal Image Quality Using The Contrast Stretching, Histogram Equalization, and
CLAHE Methods with Median Filters,” International Journal of Image, Graphics and Signal Processing, vol. 12, no. 2, pp.
30–41, 2020.
[5] N. Chervyakov, P. Lyakhov, D. Kaplun, D. Butusov, and N. Nagornov, “Analysis of The Quantization Noise in DiscreteWavelet
Transform Filters for Image Processing,” Electronics (Switzerland), vol. 7, no. 8, 2018.
[6] H. Michalak and K. Okarma, “Region based adaptive binarization for optical character recognition purposes,” 2018 International
Interdisciplinary PhD Workshop, IIPhDW 2018, pp. 361–366, 2018.
[7] O. Ali and N. Muhammad, “A comparative study of automatic vessel segmentation algorithms,” in 3rd International Conference
on Computing, Mathematics and Engineering Technologies (iCoMET), 2020, pp. 1–6.
[8] T. Mapayi, S. Viriri, and J.-r. Tapamo, “Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding
Techniques,” Computational and Mathematical Methods in Medicine, vol. 2015, pp. 1–15, 2015.
[9] Y. Li, H. Gong, W. Wu, G. Liu, and G. Chen, “An Automated Method Using Messian Matrix and Random Walks for Retinal
Blood Vessel Segmentation,” Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015,
vol. 1, no. Cisp, pp. 423–427, 2016.
[10] A. Desiani, D. A. Zayanti, R. Primartha, F. Efriliyanti, and N. A. C. Andriani, “Variasi Thresholding untuk Segmentasi Pembuluh
Darah Citra Retina,” Jurnal Edukasi dan Penelitian Informatika JEPIN, vol. 7, no. 2, pp. 255–262, 2021.
[11] A. Akagic, E. Buza, and S. Omanovic, “Pothole Detection: An Efficient Vision Based Method Using RGB Color Space Image
Segmentation,” 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics,
MIPRO 2017 - Proceedings, pp. 1104–1109, 2017.
Combination Contrast Stretching . . . (Anita Desiani)
12 r ISSN: 2476-9843
[12] Vargaz vazques D, R. r. J, and J. A. R. Shancez, “Face segmentation using mathematical morphology on single faces,” IEEE,
pp. 3–6, 2016.
[13] Nurliadi, P. Sihombing, and M. Ramli, “Analisis Contrast Stretching Menggunakan Algoritma Euclidean Untuk Meningkatkan
Kontras Pada Citra Berwarna,” vol. 03, no. 2013, pp. 26–38, 2016.
[14] U. Erkan, L. G¨okrem, and S. Enginolu, “Different applied median filter in salt and pepper noise,” Computers and Electrical
Engineering, vol. 70, pp. 789–798, 2018.
[15] M. A. D´ıaz-Cort´es, N. Ortega-S´anchez, S. Hinojosa, D. Oliva, E. Cuevas, R. Rojas, and A. Demin, “A multi-level thresholding
method for breast thermograms analysis using Dragonfly algorithm,” Infrared Physics and Technology, vol. 93, no. August, pp.
346–361, 2018.
[16] X. Yan, M. Jia, W. Zhang, and L. Zhu, “Fault Diagnosis of Rolling Element Bearing Using A New Optimal Scale Morphology
Analysis Method,” ISA Transactions, vol. 73, pp. 165–180, 2018.
[17] W. Lilik, R. Imam, and P. Yudi, “Comparative Analysis of Image Steganography using SLT,DCT and SLT-DCT Algorithm,”
Matrik : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 1, pp. 169–182, 2020.
[18] S. Pal, S. Chatterjee, D. Dey, and S. Munshi, “Morphological operations with iterative rotation of structuring elements for
segmentation of retinal vessel structures,” Multidimensional Systems and Signal Processing, vol. 30, no. 1, pp. 373–389, 2019.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
