Improving Optic Disc and Optic Cup Segmentation with Flip-Gamma Augmentation and SegFormer

Authors

  • Fitri Salamah Universitas Sriwijaya
  • Erwin Universitas Sriwijaya
  • Anita Desiani Universitas Sriwijaya

DOI:

10.33395/sinkron.v10i2.15996

Keywords:

Augmentation, Glaucoma, Retinal image, SegFormer, Segmentation

Abstract

The Cup-to-Disc Ratio (CDR) is widely used as a diagnostic indicator for glaucoma, although variations and irregularities can influence its accuracy in the Optic Disc (OD) and Optic Cup (OC). To overcome this challenge, automated image segmentation is used. However, image segmentation is challenged by image blurriness, noise, and uneven illumination, which can affect segmentation quality and increase the risk of misdiagnosis. To address these challenges, this study applies a combined Flip-Gamma Augmentation and SegFormer approach for OD and OC segmentation. Flip-Gamma augmentation increases image diversity and improves image quality by adjusting brightness and contrast. Meanwhile, the SegFormer uses a Transformer-based backbone and efficiently extracts multi-scale features to enhance segmentation performance. Experimental results on the Drishti-GS dataset show that applying Flip-Gamma (δ = 0.8, 0.9, 1.1, 1.2) is associated with improved segmentation performance across all classes, with sensitivity (90-99%), DSC (90-99%), IoU (82-99%), and ROC (94-99%), indicating consistent segmentation of OD, OC and background regions. Furthermore, a one-sided Mann-Whitney U test indicates differences in performance compared to other augmentation methods. These findings suggest that the proposed augmentation strategy is beneficial for segmentation on the Drishti-GS dataset. However, further validation on larger and more diverse datasets is required to assess generalizability.

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References

Acharya, Aditya, and A. Venkat Giri. 2020. “Contrast Improvement Using Local Gamma Correction.” Pp. 110–14 in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE.

Al-Bander, Baidaa, Bryan Williams, Waleed Al-Nuaimy, Majid Al-Taee, Harry Pratt, and Yalin Zheng. 2018. “Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis.” Symmetry 10(4):87. doi: 10.3390/sym10040087.

Bian, Xuesheng, Xiongbiao Luo, Cheng Wang, Weiquan Liu, and Xiuhong Lin. 2020. “Optic Disc and Optic Cup Segmentation Based on Anatomy Guided Cascade Network.” Computer Methods and Programs in Biomedicine 197:105717. doi: 10.1016/j.cmpb.2020.105717.

Chandan, and Ritula Thakur. 2018. “An Intelligent Model for Indian Soil Classification Using Various Machine Learning Techniques.” International Journal of Computational Engineering Research 08(9):33–41.

Deng, Yani, Xin Liu, Lianhe Shao, Kai Wang, Xihan Wang, and Quanli Gao. 2024. “Kidney Tumor Segmentation Based on DWR-SegFormer.” Electronics 13(16):3226. doi: 10.3390/electronics13163226.

Desiani, Anita, Yuli Andriani, Indri Ramayanti, Sigit Priyanta, Bambang Suprihatin, Chairu Nisa Apriyani, and Muhammad Arhami. 2024. “RIB-NET as Modification of CNN Architecture for Semantic Segmentation Of Optic Disc and Optic Cup.” Biomedical Engineering: Applications, Basis and Communications 36(06). doi: 10.4015/S1016237224500364.

Desiani, Anita, Member Erwin, 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.” International Journal of Computer Science 48.

Desiani, Anita, Sigit Priyanta, Indri Ramayanti, Bambang Suprihatin, Muhammad Gibran Al-Filambany, and Fitri Salamah. 2023. “Improved U-Net Performance with Augmentation for Retinal Optic Segmentation.” Pp. 284–89 in International Conference on Informatics, Multimedia, Cyber and Information Systems (ICIMCIS).

Gagan, J. H., Harshit S. Shirsat, Yogish S. Kamath, Neetha I. R. Kuzhuppilly, and J. R. Haris. Kumar. 2022. “Automated Optic Disc Segmentation Using Basis Splines-Based Active Contour.” IEEE Access 10(August):88152–63. doi: 10.1109/ACCESS.2022.3199347.

Gupta, Neeraj, Hitendra Garg, and Rohit Agarwal. 2022. “A Robust Framework for Glaucoma Detection Using CLAHE and EfficientNet.” Visual Computer 38(7):2315–28. doi: 10.1007/s00371-021-02114-5.

Jiang, Yuming, Lixin Duan, Jun Cheng, Zaiwang Gu, Hu Xia, Huazhu Fu, Changsheng Li, and Jiang Liu. 2020. “JointRCNN: A Region-Based Convolutional Neural Network for Optic Disc and Cup Segmentation.” IEEE Transactions on Biomedical Engineering 67(2):335–43. doi: 10.1109/TBME.2019.2913211.

Kaushik, Meenakshi, Prabhakar Tiwari, Tanuj Dada, and Rima Dada. 2024. “Beyond the Optic Nerve: Genetics, Diagnosis, and Promising Therapies for Glaucoma.” Gene 894(September 2023):147983. doi: 10.1016/j.gene.2023.147983.

Kumar, Abhinav, Anshul Sharma, Amit Kumar Singh, Sanjay Kumar Singh, and Sonal Saxena. 2023. “Data Augmentation for Medical Image Classification Based on Gaussian Laplacian Pyramid Blending with a Similarity Measure.” IEEE Journal of Biomedical and Health Informatics PP. doi: 10.1109/JBHI.2023.3307216.

Lei, Haijun, Weixin Liu, Hai Xie, Benjian Zhao, Guanghui Yue, and Baiying Lei. 2022. “Unsupervised Domain Adaptation Based Image Synthesis and Feature Alignment for Joint Optic Disc and Cup Segmentation.” IEEE Journal of Biomedical and Health Informatics 26(1):90–102. doi: 10.1109/JBHI.2021.3085770.

Li, Tianping, Xiaolong Yang, Zhenyi Zhang, Zhaotong Cui, and Zhou Maoxia. 2025. “Mix-Layers Semantic Extraction and Multi-Scale Aggregation Transformer for Semantic Segmentation.” Complex & Intelligent Systems 11(1):36. doi: 10.1007/s40747-024-01650-6.

Liu, Yong, Jin Wu, Yuanpei Zhu, and Xuezhi Zhou. 2024. “Combined Optic Disc and Optic Cup Segmentation Network Based on Adversarial Learning.” IEEE Access 12(June):104898–908. doi: 10.1109/ACCESS.2024.3435552.

Maiyanti, Sri Indra, Anita Desiani, Syafrina Lamin, Puspitashati, Muhammad Arhami, Nuni Gofar, and Destika Cahyana. 2023. “Rotation-Gamma Correction Augmentation on Cnn-Dense Block for Soil Image Classification.” Applied Computer Science 19(3):96–115.

Mazraedoost, Sargol. 2024. Computers in Biology and Medicine.

Nuli, Uday A., Shrinivas D. Desai, and Gururaj N. Bhadri. 2024. “Study on Limited Data Problem - Semantic Segmentation of Retinal Fundus Images.” Procedia Computer Science 233:782–92. doi: 10.1016/j.procs.2024.03.267.

Rudiansyah, Anita Desiani, Dian Palupi Rini, Lucky Indra Kesuma, Fitri Salamah, and Silfani Cahaya Putri. 2024. “Median Filter and U-Net Architecture for Robust Segmentation Nucleus and Cytoplasm on Pap Smear.” Pp. 779–84 in 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE.

Shah, Shivam, Nikhil Kasukurthi, and Harshit Pande. 2019. “Dynamic Region Proposal Networks For Semantic Segmentation In Automated Glaucoma Screening.” Pp. 578–82 in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE.

Sivaswamy, Jayanthi, Subbaiah Krishnadas, Arunava Chakravarty, Gopal Joshi, and Ujjwal. 2015. “A Comprehensive Retinal Image Dataset for the Assessment of Glaucoma from the Optic Nerve Head Analysis.” JSM Biomed Imaging Data Pap 2.

Sule, Olubunmi Omobola, Serestina Viriri, and Abdultaofeek Abayomi. 2020. “Effects of Image Enhancement Techniques on CNNs Based Algorithms for Segmentation of Blood Vessels: A Review.” Pp. 1–6 in 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE.

Thakur, Niharika, and Mamta Juneja. 2019. “Optic Disc and Optic Cup Segmentation from Retinal Images Using Hybrid Approach.” Expert Systems with Applications 127:308–22. doi: 10.1016/j.eswa.2019.03.009.

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.

Wu, Hanqiong, Gangrong Qu, Zhifeng Xiao, and Fan Chunyu. 2024. “Enhancing Left Ventricular Segmentation in Echocardiography with a Modified Mixed Attention Mechanism in SegFormer Architecture.” Heliyon 10(15):e34845. doi: 10.1016/j.heliyon.2024.e34845.

Xie, Enze, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo. 2021. “SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.”

Yin, Rui, Zihan Luo, Pei Zhuang, Zhuoyi Lin, and Chee Keong Kwoh. 2021. “VirPreNet: A Weighted Ensemble Convolutional Neural Network for the Virulence Prediction of Influenza A Virus Using All Eight Segments.” Bioinformatics 37(6):737–43. doi: 10.1093/bioinformatics/btaa901.

Yu, Hanwen, Xin Ye, Wanjing Hong, Rui Shi, Yi Ding, and Chengcheng Liu. 2024. “A Cascading Learning Method with SegFormer for Radiographic Measurement of Periodontal Bone Loss.” BMC Oral Health 24(1):325. doi: 10.1186/s12903-024-04079-y.

Yu, Ying, Chunping Wang, Qiang Fu, Renke Kou, Fuyu Huang, Boxiong Yang, Tingting Yang, and Mingliang Gao. 2023. “Techniques and Challenges of Image Segmentation: A Review.” Electronics 12(5):1199. doi: 10.3390/electronics12051199.

Yusro, M., E. Suryana, K. Ramli, D. Sudiana, and K. M. Hou. 2019. “Testing The Performance of A Single Pole Detection Algorithm Using The Confusion Matrix Model.” P. 77066 in Journal of Physics: Conference Series. Vol. 1402. IOP Publishing.

Zhou, Wei, Jianhang Ji, Yan Jiang, Jing Wang, Qi Qi, and Yugen Yi. 2023. “EARDS: EfficientNet and Attention-Based Residual Depth-Wise Separable Convolution for Joint OD and OC Segmentation.” Frontiers in Neuroscience 17. doi: 10.3389/fnins.2023.1139181.

Zhu, Qianlong, Xinjian Chen, Qingquan Meng, Jiahuan Song, Gaohui Luo, Meng Wang, Fei Shi, Zhongyue Chen, Dehui Xiang, Lingjiao Pan, Zuoyong Li, and Weifang Zhu. 2021. “GDCSeg-Net: General Optic Disc and Cup Segmentation Network for Multi-Device Fundus Images.” Biomedical Optics Express 12(10):6529. doi: 10.1364/BOE.434841.

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How to Cite

Salamah, F., Erwin, & Desiani, A. (2026). Improving Optic Disc and Optic Cup Segmentation with Flip-Gamma Augmentation and SegFormer. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(2), 1157-1168. https://doi.org/10.33395/sinkron.v10i2.15996