Variasi Thresholding untuk Segmentasi Pembuluh Darah Citra Retina
Abstract
Segmentasi pembuluh darah pada retina diperlukan pada deteksi dini penyakit Diabetic Retinopathy pada citra retina. Penelitian ini menggunakan tiga tahapan yaitu pre-processing, segmentasi dan post-processing yang akan membandingkan hasil dari 3 metode segmentasi yang menggunakan nilai Thresholding yaitu Adaptive Thresholding, Binary Thresholding, dan Otsu Thresholding. Hasil pengujian terhadap tiga metode yang digunakan menunjukan bahwa metode Binary Thresholding mendapat rata-rata akurasi, sensitivitas dan spesifisitas tertinggi yaitu 95%, 58%, 98%. Untuk Adaptive Thresholding mendapat rata-rata akurasi sebesar 91%, sensitivitas 36%, spesititiftas 97%. Dan metode Otsu Thresholding mendapatkan rata-rata akurasi 86%, sensitivitas 22%, dan spesifisitas 90%. Dari hasil ketiga metode ini dapat dilihat akurasi yang dihasilkan oleh metode Thresholding sudah sangat baik dalam melakukan segmentasi citra, tetapi nilai sensitivitas dari masing-masing metode Thresholding masih rendah. Hal ini dapat disimpulkan metode Thresholding masih sulit mendapatkan lebih banyak fitur pembuluh darah pada citra retina.
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T. Sabhanayagam, V. Prasanna Venkatesan, and K. Senthamaraikannan, “A Comprehensive Survey on Various Biometric Systems,” International Journal of Applied Engineering Research, vol. 13, no. 5, pp. 2276–2297, 2018.
H. E. Moss, “Retinal Vascular Changes are a Marker for Cerebral Vascular Diseases,” Current Neurology and Neuroscience Reports, vol. 15, no. 7, pp. 1–9, 2015, doi: 10.1007/s11910-015-0561-1.
U. T. V. Nguyen, A. Bhuiyan, L. A. F. Park, and K. Ramamohanarao, “An effective retinal blood vessel segmentation method using multi-scale line detection,” Pattern Recognition, vol. 46, no. 3, pp. 703–715, 2013, doi: 10.1016/j.patcog.2012.08.009.
R. C. Wihandika and N. Suciati, “Retinal Blood Vessel Segmentation with Optic Disc Pixels Exclusion,” I.J. Image, Graphics and Signal Processing, vol. 7, no. June, pp. 26–33, 2013, doi: 10.5815/ijigsp.2013.07.04.
J. Dash and N. Bhoi, “Retinal blood vessel segmentation using Otsu thresholding with principal component analysis,” Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018, no. Icisc, pp. 933–937, 2018, doi: 10.1109/ ICISC.2018.8398938.
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, 2016, doi: 10.1371/journal.pone.0158996.
L. Câmara Neto, G. L. B. Ramalho, J. F. S. Rocha Neto, R. M. S. Veras, and F. N. S. Medeiros, “An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images,” Expert Systems with Applications, vol. 78, pp. 182–192, 2017, doi: 10.1016/j.eswa.2017.02.015.
R. P. Singh and M. Dixit, “Histogram Equalization: A Strong Technique for Image Enhancement,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 8, pp. 345–352, 2015, doi: 10.14257/ijsip.2015.8.8.35.
C. Shi, Y. Wang, B. Xiao, and C. Wang, “OTSU guided adaptive binarization of CAPTCHA image using gamma correction,” Proceedings - International Conference on Pattern Recognition, vol. 0, pp. 3962–3967, 2016, doi: 10.1109/ICPR.2016.7900253.
Y. Zhu and C. Huang, “An Improved Median Filtering Algorithm for Image Noise Reduction,” Physics Procedia, vol. 25, pp. 609–616, 2012, doi: 10.1016/j.phpro.2012.03.133.
N. P. Singh, R. Kumar, and R. Srivastava, “Local entropy thresholding based fast retinal vessels segmentation by modifying matched filter,” in International Conference on Computing, Communication and Automation, ICCCA 2015, 2015, pp. 1166–1170, doi: 10.1109/CCAA.2015.7148552.
J. Flusser, S. Farokhi, C. Höschl, T. Suk, B. Zitová, and M. Pedone, “Recognition of Images Degraded by Gaussian Blur,” IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 790–806, 2016, doi: 10.1109/TIP.2015.2512108.
S. Pathan, P. C. Siddalingaswamy, and K. G. Prabhu, “A pixel processing approach for retinal vessel extraction using modified Gabor functions,” Progress in Artificial Intelligence, vol. 7, no. 1, pp. 1–14, 2018, doi: 10.1007/s13748-017-0134-4.
Y. Zhang, W. Li, L. Zhang, X. Ning, L. Sun, and Y. Lu, “Adaptive Learning Gabor Filter for Finger-Vein Recognition,” IEEE Access, vol. 7, pp. 159821–159830, 2019, doi: 10.1109/ACCESS. 2019.2950698.
F. Farokhian and H. Demirel, “Blood vessels detection and segmentation in retina using Gabor filters,” 2013 High Capacity Optical Networks and Emerging/Enabling Technologies, HONET-CNS 2013, pp. 104–108, 2013, doi: 10.1109/HONET.2013.6729766.
F. Farokhian, C. Yang, H. Demirel, S. Wu, and I. Beheshti, “Automatic parameters selection of Gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation,” Biocybernetics and Biomedical Engineering, vol. 37, no. 1, pp. 246–254, 2017, doi: 10.1016/j.bbe.2016.12.007.
R. Kushol, M. H. Kabir, M. S. Salekin, and A. B. M. Ashikur Rahman, “Contrast enhancement by top-hat and bottom-hat transform with optimal structuring element: Application to retinal vessel segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10317, pp. 533–540, 2017, doi: 10.1007/978-3-319-59876-5_59.
K. Ahmadi, A. Y. Javaid, and E. Salari, “Signal Processing : Image Communication An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization,” Signal Processing : Image Communication, vol. 32, pp. 33–39, 2015, doi: 10.1016/j.image.2015.01.001.
T. Y. Goh, S. N. Basah, H. Yazid, M. J. Aziz Safar, and F. S. Ahmad Saad, “Performance analysis of image thresholding: Otsu technique,” Measurement: Journal of the International Measurement Confederation, vol. 114, no. June 2017, pp. 298–307, 2018, doi: 10.1016/j.measurement.2017.09.052.
N. Senthilkumaran and S. Vaithegi, “Image Segmentation By Using Thresholding Techniques For Medical Images,” Computer Science & Engineering: An International Journal, vol. 6, no. 1, pp. 1–13, 2016, doi: 10.5121/cseij.2016.6101.
X. Ji, Y. Li, J. Cheng, and Y. Yu, “Cell Image Segmentation Based on an Improved Watershed Algorithm,” International Congress on Image and Signal Processing (CISP 2015), vol. 1, pp. 433–437, 2015.
A. Desiani, S. Yahdin, and A. Kartikasari, “Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 2, pp. 346–354, 2021, doi: 10.11591/ijai. v10.i2.pp346-354.
DOI: http://dx.doi.org/10.26418/jp.v7i2.47205
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