Fengshou Yin

Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening

Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.

Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI

Glaucoma is a leading cause of permanent blindness. However, disease progression can be limited if detected early. The optic cup-to-disc ratio (CDR) is one of the main clinical indicators of glaucoma, and is currently determined manually, limiting its potential in mass screening. In this paper, we propose an automatic CDR determination method using a variational level-set approach to segment the optic disc and cup from retinal fundus images. The method is a core component of ARGALI, a system for automated glaucoma risk assessment. Threshold analysis is used in pre-processing to estimate the initial contour. Due to the presence of retinal vasculature traversing the disc and cup boundaries which can cause inaccuracies in the detected contours, an ellipse-fitting post-processing step is also introduced. The method was tested on 104 images from the Singapore Malay Eye Study, and it was found the results produced a clinically acceptable variation of up to 0.2 CDR units from the manually graded samples, with potential use in mass screening.

Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis

The vertical Cup-to-Disc Ratio (CDR) is an important indicator in the diagnosis of glaucoma. Automatic segmentation of the optic disc (OD) and optic cup is crucial towards a good computer-aided diagnosis (CAD) system. This paper presents a statistical model-based method for the segmentation of the optic disc and optic cup from digital color fundus images. The method combines knowledge-based Circular Hough Transform and a novel optimal channel selection for segmentation of the OD. Moreover, we extended the method to optic cup segmentation, which is a more challenging task. The system was tested on a dataset of 325 images. The average Dice coefficient for the disc and cup segmentation is 0.92 and 0.81 respectively, which improves significantly over existing methods. The proposed method has a mean absolute CDR error of 0.10, which outperforms existing methods. The results are promising and thus demonstrate a good potential for this method to be used in a mass screening CAD system.

Model-based optic nerve head segmentation on retinal fundus images

The optic nerve head (optic disc) plays an important role in the diagnosis of retinal diseases. Automatic localization and segmentation of the optic disc is critical towards a good computer-aided diagnosis (CAD) system. In this paper, we propose a method that combines edge detection, the Circular Hough Transform and a statistical deformable model to detect the optic disc from retinal fundus images. The algorithm was evaluated against a data set of 325 digital color fundus images, which includes both normal images and images with various pathologies. The result shows that the average error in area overlap is 11.3% and the average absolute area error is 10.8%, which outperforms existing methods. The result indicates a high correlation with ground truth segmentation and thus demonstrates a good potential for this system to be integrated with other retinal CAD systems.

Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio

The ratio of the optic cup to disc (CDR) in retinal fundus images is one of the principal physiological characteristics in the diagnosis of glaucoma. Currently the CDR is manually determined which can be subjective and limits its use in mass screening. To automatically extract the disc, a variational level set method is proposed in this paper. For the cup, two methods making use of color intensity and threshold level set are evaluated. A batch of 73 retinal images from the Singapore Eye Research Centre was used to assess the performance of the determined CDR to the clinical CDR, and it was found that the threshold and variational level set methods produced 97% accuracy in the determined CDR results, an 18% improvement over the color intensity method. The results indicate potential applicability of the methods for automated and objective mass screening for early detection of glaucoma.

Automatic optic disc segmentation with peripapillary atrophy elimination

Optic disc segmentation from retinal fundus image is a fundamental but important step for automatic glaucoma diagnosis. In this paper, an optic disc segmentation method is proposed based on peripapillary atrophy elimination. The elimination is done through edge filtering, constraint elliptical Hough transform and peripapillary atrophy detection. With the elimination, edges that are likely from non-disc structures especially peripapillary atrophy are excluded to make the segmentation more accurate. The proposed method has been tested in a database of 650 images with disc boundaries marked by trained professionals manually. The experimental results by the proposed method show average m1, m2 and mVD of 10.0%, 7.4% and 4.9% respectively. It can be used to compute cup to disc ratio as well as other features for application in automatic glaucoma diagnosis systems.

Optic disc region of interest localization in fundus image for Glaucoma detection in ARGALI

Glaucoma is the second leading cause of permanent blindness worldwide. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Correctly determining the optic disc region of interest (ROI) will produce a smaller initial image which takes much lesser time taken to process compared to the entire image. The earlier ROI localization in the ARGALI system used a grid based method. The new algorithm adds a preprocessing step before analyzing the image. This step significantly improves the performance of the ROI detection. A batch of 1564 retinal images from the Singapore Eye Research Centre was used to compare the performance of the two methods. From the results, the earlier and new algorithm detects the ROI correctly for 88% and 96% of the images respectively. The results indicate potential applicability of the method for automated and objective mass screening for early detection of glaucoma.

Sparse Dissimilarity-Constrained Coding for Glaucoma Screening

Objective: Glaucoma is an irreversible chronic eye disease that leads to vision loss. As it can be slowed down through treatment, detecting the disease in time is important. However, many patients are unaware of the disease because it progresses slowly without easily noticeable symptoms. Currently, there is no effective method for low-cost population-based glaucoma detection or screening. Recent studies have shown that automated optic nerve head assessment from 2-D retinal fundus images is promising for low-cost glaucoma screening. In this paper, we propose a method for cup to disc ratio (CDR) assessment using 2-D retinal fundus images. Methods: In the proposed method, the optic disc is first segmented and reconstructed using a novel sparse dissimilarity-constrained coding (SDC) approach which considers both the dissimilarity constraint and the sparsity constraint from a set of reference discs with known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to compute the CDR for the testing disc. Results: The proposed method has been tested for CDR assessment in a database of 650 images with CDRs manually measured by trained professionals previously. Experimental results show an average CDR error of 0.064 and correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-the-art methods. Our proposed method has also been tested for glaucoma screening. The method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images, respectively, outperforming other methods. Conclusion: The proposed method achieves good accuracy for glaucoma detection. Significance: The method has a great potential to be used for large-scale population-based glaucoma screening.

Automatic glaucoma diagnosis from fundus image

Glaucoma is currently diagnosed by glaucoma specialists using specialized imaging devices like HRT and OCT. Fundus imaging is a modality widely used in primary healthcare. An automatic glaucoma diagnosis system based on fundus image can be deployed to primary healthcare clinics and has potential for early disease diagnosis. A mass glaucoma screening program can also be facilitated using such a system. We present an automatic fundus image based cup-to-disc ratio measurement system; and demonstrate its potential for automatic objective glaucoma diagnosis and screening. It provides strong support to use fundus image as the modality for automatic glaucoma diagnosis.

THALIA - An automatic hierarchical analysis system to detect drusen lesion images for amd assessment

Age-related macular degeneration (AMD) is a leading cause of permanent blindness. In its early stage AMD is characterized by drusen which are extracellelur deposits in the retina. In this paper, we present THALIA, an automatic system for the detection of drusen images for AMD assessment. First, the macular region of interest is detected using a seeded mode tracking approach. The macular region of interest is then mapped into a new representation using a hierarchicial word transform (HWI). In HWI, dense sampling is first carried out to generate structured pixels which embed local context. These structured pixels are then clustered using hierarchical k-means. The HWI image is subsequently classified using a SVM-based classifier. We have tested THALIA on a dataset of 350 images and obtained an accuracy of 95.46%. Results are promising for further validation of the THALIA system.