Segmentasi Citra Pembuluh Darah Retina Menggunakan Operasi Morfologi Iteratif

Vita Nurdinawati(1), Atika Hendryani(2), Thareq Barasabha(3),


(1) Department of Electromedical Engineering Poltekkes Kemenkes Jakarta II
(2) Department of Electromedical Engineering Poltekkes Kemenkes Jakarta II
(3) Medical Faculty Brawijaya University

Abstract

Retinal vessel segmentation is part of the morphological extraction of retinal blood vessels that plays an essential role in medical image processing. Manual segmentation is possible to do, but it is time-consuming and requires special operators. Moreover, the possibility of variability between operators is vast. This study aims to answer the shortcomings of the manual segmentation process by automatically segmenting retinal blood vessels. The main contribution of this study is the use of a simple method to iteratively segment retinal blood vessels.  All processes in the segmentation are simulated using Matlab. The algorithm was evaluated by comparing the results of the automatic segmentation with 20 manually segmented images from the STARE dataset. The result show specificity 98.13%, accuracy 93.60%, sensitivity 56.42%, precision 80.48%, and the dice coefficient 64.06%. In conclusion, the automatic retinal blood vessel image segmentation process worked well.

Keywords

retinal segmentation; medical image processing; iteration process

Full Text:

PDF

References

N. Tamim, M. Elshrkawey, G. A. Azim, and H. Nassar, “Retinal blood vessel segmentation using hybrid features and multi-layer perceptron neural networks,†Symmetry (Basel)., vol. 12, no. 6, 2020, doi: 10.3390/SYM12060894.

M. K. Fadafen, N. Mehrshad, and S. M. Razavi, “Detection of diabetic retinopathy using computational model of human visual system,†Biomed. Res., vol. 29, no. 9, pp. 1956–1960, 2018, doi: 10.4066/biomedicalresearch.29-18-551.

Y. Guo and Y. Peng, “BSCN: Bidirectional symmetric cascade network for retinal vessel segmentation,†BMC Med. Imaging, vol. 20, no. 1, pp. 1–22, 2020, doi: 10.1186/s12880-020-0412-7.

Aastha and R. Gautam, “A review on retinal blood vessel segmentation methodologies,†Int. J. Sci. Technol. Res., vol. 8, no. 9, pp. 738–747, 2019.

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 Syst. Appl., vol. 78, pp. 182–192, 2017, doi: 10.1016/j.eswa.2017.02.015.

J. Almotiri, K. Elleithy, and A. Elleithy, “Retinal vessels segmentation techniques and algorithms: A survey,†Appl. Sci., vol. 8, no. 2, 2018, doi: 10.3390/app8020155.

Z. Yavuz and C. Köse, “Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification,†J. Healthc. Eng., vol. 2017, 2017, doi: 10.1155/2017/4897258.

A. Ali, A. Hussain, and W. M. D. Wan Zaki, “Segmenting retinal blood vessels with gabor filter and automatic binarization,†Int. J. Eng. Technol., vol. 7, no. 4, pp. 163–167, 2018, doi: 10.14419/ijet.v7i4.11.20794.

Erwin, a. Rohman, L. a. Nurjanah, Yurika, D. Sinta, and Q. Al’Afwa, “New techniques for segmentation and extraction retinal blood vessels,†J. Phys. Conf. Ser., vol. 1500, no. 1, 2020, doi: 10.1088/1742-6596/1500/1/012090.

J. Dash and N. Bhoi, “Retinal blood vessel segmentation using Otsu thresholding with principal component analysis,†in Proc. 2nd Int. Conf. Inven. Syst. Control, ICISC 2018, pp. 933–937, 2018, doi: 10.1109/ICISC.2018.8398938.

U. Ozkava, S. Ozturk, B. Akdemir, and L. Sevfi, “An Efficient Retinal Blood Vessel Segmentation using Morphological Operations,†in ISMSIT 2018 - 2nd Int. Symp. Multidiscip. Stud. Innov. Technol. Proc., pp. 1-7, 2018, doi: 10.1109/ISMSIT.2018.8567239.

Z. Jiang, H. Zhang, Y. Wang, and S. B. Ko, “Retinal blood vessel segmentation using fully convolutional network with transfer learning,†Comput. Med. Imaging Graph., vol. 68, no. April, pp. 1–15, 2018, doi: 10.1016/j.compmedimag.2018.04.005.

N. Memari, A. R. Ramli, M. I. Bin Saripan, S. Mashohor, and M. Moghbel, “Retinal Blood Vessel Segmentation by Using Matched Filtering and Fuzzy C-means Clustering with Integrated Level Set Method for Diabetic Retinopathy Assessment,†J. Med. Biol. Eng., vol. 39, no. 5, pp. 713–731, 2019, doi: 10.1007/s40846-018-0454-2.

A. Hoover, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,†IEEE Trans. Med. Imaging, vol. 19, no. 3, pp. 203–210, 2000, doi: 10.1109/42.845178.

M. Alam, T. Son, D. Toslak, J. I. Lim, and X. Yao, “Combining ODR and blood vessel tracking for artery–vein classification and analysis in color fundus images,†Transl. Vis. Sci. Technol., vol. 7, no. 2, 2018, doi: 10.1167/tvst.7.2.23.

D. A. Palanivel, S. Natarajan, and S. Gopalakrishnan, “Retinal vessel segmentation using multifractal characterization,†Appl. Soft Comput. J., vol. 94, p. 106439, 2020, doi: 10.1016/j.asoc.2020.106439.

D. Sutaji, C. Fatichah, and D. A. Navastara, “Segmentasi Pembuluh Darah Retina pada Citra Fundus Menggunakan Gradient Based Adaptive Thresholding dan Region Growing,†Regist. J. Ilm. Teknol. Sist. Inf., vol. 2, no. 2, p. 105, 2016, doi: 10.26594/register.v2i2.553.

S. Aswini, a. Suresh, S. Priya, and B. V. Santhosh Krishna, “Retinal vessel segmentation using morphological top hat approach on diabetic retinopathy images,†in Proc. 4th IEEE Int. Conf. Adv. Electr. Electron. Information, Commun. Bio-Informatics, AEEICB 2018, pp. 1–5, 2018, doi: 10.1109/AEEICB.2018.8480970.

R. P. Sari, “Enhancement Citra Fundus Retina Menggunakan CLAHE dan Wiener Filter,†Ars, vol. 4, no. 1, pp. 978–979, 2018.

M. Santoso, T. Indriyani, and R. E. Putra, “Deteksi Microaneurysms pada Citra Retina Mata Menggunakan Matched Filter,†INTEGER J. Inf. Technol., vol. 2, no. 2, pp. 59–68, 2017, [Online]. Available: https://ejurnal.itats.ac.id/integer/article/view/180/97.

M. Garg and S. Gupta, “Generation of binary mask of retinal fundus image using bimodal masking,†J. Adv. Res. Dyn. Control Syst., vol. 10, no. 6, pp. 1777–1786, 2018.

K. Yota, E. Aryanto, and I. K. E. Purnama, “Segmentasi Pembuluh Darah Pada Citra Retina Menggunakan Max-Tree dan Attribute Filtering,†in SNATI, vol. 2009, 2009.

F. Sabaz and U. Atila, “ROI detection and vessel segmentation in retinal image,†Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 4W6, pp. 85–89, 2017, doi: 10.5194/isprs-archives-XLII-4-W6-85-2017.

L. Farosanti and C. Fatichah, “Perbaikan Segmentasi Pembuluh Darah Tipis Pada Citra Retina Menggunakan Fuzzy Entropy,†JUTI J. Ilm. Teknol. Inf., vol. 17, no. 2, p. 135, 2019, doi: 10.12962/j24068535.v17i2.a857.

Y. Yu and H. Zhu, “Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified attention U-Net,†Eurasip J. Image Video Process., vol. 2021, no. 1, 2021, doi: 10.1186/s13640-021-00546-6.

C. Bhardwaj, S. Jain, and M. Sood, “Automatic blood vessel extraction of fundus images employing fuzzy approach,†Indones. J. Electr. Eng. Informatics, vol. 7, no. 4, pp. 757–771, 2019, doi: 10.11591/ijeei.v7i4.991.

Refbacks

  • There are currently no refbacks.