Segmentasi Optic Cup dan Optic Disc Menggunakan U-Net Backbone Resnet50

Bachtiar Riza Pratama; Fetty Tri Anggraeny; Achmad Junaidi

doi:10.33795/jip.v11i4.7352

Authors

Bachtiar Riza Pratama Program Studi Informatika, Fakultas Ilmu Komputer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Indonesia
Fetty Tri Anggraeny Program Studi Informatika, Fakultas Ilmu Komputer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Indonesia
Achmad Junaidi Program Studi Informatika, Fakultas Ilmu Komputer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Indonesia

DOI:

https://doi.org/10.33795/jip.v11i4.7352

Keywords:

Segmentasi Citra, Optic Disc, Optic Cup, U-Net, ResNet50, Cup to Disc Ratio

Abstract

Glaukoma merupakan penyakit mata serius yang dapat menyebabkan kebutaan permanen. Salah satu indikator penting dalam diagnosis glaukoma adalah nilai Cup to Disc Ratio (CDR), yang diperoleh dari segmentasi area optic disc (OD) dan optic cup (OC) pada citra fundus retina. Penelitian ini mengembangkan model segmentasi berbasis U-Net dengan backbone ResNet50 untuk mendeteksi area OD dan OC secara otomatis. Data yang digunakan adalah dataset REFUGE sebanyak 1200 citra fundus dan mask ground truth. Sebelum pelatihan, dilakukan tahap pra-pemrosesan yang mencakup ekstraksi ROI optic disc menggunakan metode Normalized Cross-Correlation (NCC) dan peningkatan kontras dengan CLAHE.Model dievaluasi menggunakan metrik Dice Coefficient dan Intersection over Union (IoU) untuk mengukur akurasi segmentasi. Hasil segmentasi menunjukkan bahwa model menghasilkan nilai Dice Coefficient sebesar 0,9175 dan IoU sebesar 0,8976 untuk segmentasi optic disc, serta Dice sebesar 0,8924 dan IoU sebesar 0,8057 untuk segmentasi optic cup. Guna memperhalus bentuk kontur, diterapkan metode ellipse fitting pada hasil segmentasi sebelum perhitungan CDR. Nilai CDR yang diperoleh kemudian digunakan untuk mengklasifikasikan tingkat keparahan glaukoma.

Downloads

Download data is not yet available.

References

Alkhaldi, Nora A., and Ruqayyah E. Alabdulathim. 2024. “Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images.” Applied Sciences (Switzerland) 14(17). doi:10.3390/app14177795.

Almustofa, A. N., A. Handayani, and T. L.R. Mengko. 2022. “Optic Disc and Optic Cup Segmentation on Retinal Image Based on Multimap Localization and U-Net Convolutional Neural Network.” Journal of Image and Graphics(United Kingdom) 10(3): 109–15. doi:10.18178/joig.10.3.109-115.

Bernabe, Omar, Elena Acevedo, Antonio Acevedo, Ricardo Carreno, and Sandra Gomez. 2021. “Classification of Eye Diseases in Fundus Images.” IEEE Access 9: 101267–76. doi:10.1109/ACCESS.2021.3094649.

Das, Smita, Madhusudhan Mishra, and Swanirbhar Majumder. 2024. “Identification of Glaucoma from Retinal Fundus Images Using Deep Learning Model, MobileNet.” ECTI Transactions on Computer and Information Technology 18(3): 371–80. doi:10.37936/ecti-cit.2024183.256182.

Desiani, Anita, Bambang Suprihatin, Sugandi Yahdin, Ajeng I Putri, and Fathur R Husein. 2021. “Bi - Path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap - Smear Images.” 48(3).

Heraldi, Fachry Dennis. 2024. “Segmentasi Semantik Optic Disk Dan Optic Cup Pada Citra Fotografi Fundus Retina Dengan Transformer.” (13520139).

Mélik Parsadaniantz, Stéphane, Annabelle Réaux-le Goazigo, Anaïs Sapienza, Christophe Habas, and Christophe Baudouin. 2020. “Glaucoma: A Degenerative Optic Neuropathy Related to Neuroinflammation?” Cells 9(3): 1–14. doi:10.3390/cells9030535.

Minfei, Liang, Gan Yidong, Chang Ze, Wan Zhi, Schlangen Erik, and Šavija Branko. 2022. “Microstructure-Informed Deep Convolutional Neural Network for Predicting Short-Term Creep Modulus of Cement Paste.” Cement and Concrete Research 152(August 2021). doi:10.1016/j.cemconres.2021.106681.

Octavian, Octavian, Ahmad Badruzzaman, Muhammand Yusuf Ridho, and Bayu Distiawan Trisedya. 2024. “Enhancing Weighted Averaging for CNN Model Ensemble in Plant Diseases Image Classification.” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8(2): 272–79. doi:10.29207/resti.v8i2.5669.

Sharma, Neha, Sheifali Gupta, Deepika Koundal, Sultan Alyami, and Hani Alshahrani. 2023. “U-Net Model with Transfer Learning Model as a Backbone for Segmentation of Gastrointestinal Tract.”

Shinde, Rutuja. 2021. “Glaucoma Detection in Retinal Fundus Images Using U-Net and Supervised Machine Learning Algorithms.” Intelligence-Based Medicine 5: 100038. doi:10.1016/j.ibmed.2021.100038.

Shyamalee, Thisara, and Dulani Meedeniya. 2022. “Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification.” Machine Intelligence Research 19(6): 563–80. doi:10.1007/s11633-022-1354-z.

Tulsani, Akshat, Preetham Kumar, and Sumaiya Pathan. 2021. “Automated Segmentation of Optic Disc and Optic Cup for Glaucoma Assessment Using Improved UNET ++ Architecture.” Biocybernetics and Biomedical Engineering 41(2): 819–32. doi:10.1016/j.bbe.2021.05.011.

Veena, H N, A Muruganandham, and T Senthil Kumaran. 2022. “A Novel Optic Disc and Optic Cup Segmentation Technique to Diagnose Glaucoma Using Deep Learning Convolutional Neural Network over Retinal Fundus Images.” Journal of King Saud University - Computer and Information Sciences 34(8): 6187–98. doi:10.1016/j.jksuci.2021.02.003.

Virbukaite, Sandra, Jolita Bernataviciene, and Daiva Imbrasiene. 2024. “Glaucoma Identification Using Convolutional Neural Networks Ensemble for Optic Disc and Cup Segmentation.” IEEE Access 12(April): 82720–29. doi:10.1109/ACCESS.2024.3412185.