PCA-based localization approach for segmentation of optic disc

Deep learning approaches based improved light weight U-Net with attention module for optic disc segmentation

Glaucoma is a major cause of blindness worldwide, and its early detection is essential for the timely management of the condition. Glaucoma-induced anomalies of the optic nerve head may cause variation in the Optic Disc (OD) size. Therefore, robust OD segmentation techniques are necessary for the screening for glaucoma. Computer-aided segmentation has become a promising diagnostic tool for the early detection of glaucoma, and there has been much interest in recent years in using neural networks for medical image segmentation. This study proposed an enhanced lightweight U-Net model with an Attention Gate (AG) to segment OD images. We also used a transfer learning strategy to extract relevant features using a pre-trained EfficientNet-B0 CNN, which preserved the receptive field size and AG, which reduced the impact of gradient vanishing and overfitting. Additionally, the neural network trained using the binary focal loss function improved segmentation accuracy. The pre-trained Attention U-Net was validated using publicly available datasets, such as DRIONS-DB, DRISHTI-GS, and MESSIDOR. The model significantly reduced parameter quantity by around 0.53 M and had inference times of 40.3 ms, 44.2 ms, and 60.6 ms, respectively.

Automated segmentation of optic disc using statistical region merging and morphological operations

Accurate Optic Disc (OD) segmentation is vital in designing systems that aid the diagnosis and evaluation of early phases of retinal diseases. However, in many images, the OD boundary is ambiguous, which makes the automated OD segmentation process very challenging. A method to segment OD based on statistical region merging and morphological operations is proposed in this paper. The proposed method is tested on standard databases MESSIDOR, DIARETDB1, DIARETDB0, and DRIONS-DB. The average overlap ratios are found to be 91.35% for DIARETDB1 images, 88.80% for DRIONS-DB images, 86.60% for DIARETDB0 images and 89.68% for MESSIDOR images, with average accuracies of 99.68%, 99.89%, 99.69%, and 99.93% respectively. A comparison with alternative methods showed that the proposed algorithm in OD segmentation is better than existing ones.