Automatic Retinal Image Analysis for the detection of Glaucoma

Gopal Datt Joshi

Glaucoma is recognized to be the second most common cause of blindness. Early detection and treatment of glaucoma is hence important for the prevention of disease. Manual screening for glaucoma at a larger scale is challenging as availability of skilled manpower in Ophthalmology is low. The research focus of this thesis is to investigate the potential of retinal image analysis in glaucoma assessment towards developing automatic glaucoma screening system.

The task of glaucoma detection in a retinal image involves assessment of multiple retinal changes (indicators) associated directly or indirectly with the loss of underlying nerve fibers. Foremost, the task of analyzing both intra- and peri-papillary indicators from the colour retinal image is of prime importance to gather complete information. The intra-papillary indicators include retinal changes occurring within the optic disk (OD) region whereas peri-papillary indicators account for the changes occurring in the periphery of the OD region. The OD consists of a small crater-like depression in its center, physiologically known as cup region. The annular region formed between cup and OD boundaries is called \textit{rim} which represents the amount of nerve fiber bundles entering to the OD from the different areas of retina. The enlargement in the cup region called cupping is the most important intra-papillary indicator to assess the glaucomatous damage.

In this thesis, a set of solutions are developed for detecting intra-papillary indicators by segmenting the OD and cup regions from colour retinal images. A region-based active contour model is proposed to segment the OD. This extends the standard region-based active contour model by including \textit{localized} image information as a support domain around each point of interest on the contour for accurate segmentation. This model is further strengthened by the integration of information from the multiple image feature channels. The experimental results conducted on a fairly large number of images show that the proposed method is more robust and accurate than two other existing methods overall, and particularly in dealing with challenges posed by peri-peripheral atrophy regions and irregular shape of OD.

For cup segmentation methods are designed for a single monocular retinal image and sequentially acquired pair of monocular images. While the former uses vessel bend information to estimate the cup boundary the latter uses multi-view geometry and a novel problem formulation. In this formulation the objective of the underlying problem is redefined to detecting the depth discontinuity in retinal surface unlike computing precise depth/disparity, a crucial step used in earlier approaches. In other words, cup boundary points are now defined by depth discontinuities (edges) introduced by the cup region in the retinal surface. The proposed solution based on this formulation gives several advantages and flexibility over earlier solutions. Our experiment results show applicability of the proposed formulation to simultaneously as well as sequentially acquired stereo image pairs which were earlier restricted to simultaneously acquired stereo image pair.

The presence of peri-papillary indicators such as peri-papillary atrophy (PPA) and retinal nerve fiber (RNFL) defect are considered useful to gain confidence in the findings of intra-papillary indicators. However, the detection of peri-papillary indicators is generally considered challenging mainly due to the high amount of inter-image variations and also due to uncertainty in their occurrences. A detection strategy is proposed which is motivated from the saliency exhibited by these indicators in the presence of their local surround. This saliency aspect is represented by a set of context-based image features.

The information obtained from the analysis of intra- and peri-papillary indicators are finally integrated to arrive at a decision on the presence of glaucoma. The proposed integration is at the level of image features. Moreover, the regional level distributions of local image structures are computed to achieve robustness against inter-image variations. The final classification step uses aforementioned information to classify glaucoma in a retinal image. An extensive evaluation is carried out on a large test set of 1154 retinal images. The proposed system and existing CAD-based solutions are compared on various performance metrics. The designed system for glaucoma detection is assessed against three glaucoma fellows and two general ophthalmologists to understand the potential of the presented system as a screening system.

The assessment results are encouraging as the proposed system performs as well as general ophthalmologists. Our experiment results indicate that intra-papillary indicators play a crucial role in the detection of glaucoma as compared to peri-papillary indicators. However, the combination of these indicators leads to a better detection performance. It is also noticed that the clinically identified intra-papillary measurement are not sufficient to capture a wide range of glaucomatous disk changes.


Year of completion:  July 2014
 Advisor : Prof. Jayanthi Sivaswamy


Related Publications

  • Jayanthi Sivaswamy, S R Krishnadas, Gopal Dutt Joshi, Madhulika Jain, Ujjwal, Syed Tabish A. - Drishit-GS: Retinal Image Dataset for Optic Nerve Head(ONH) Segmentation Proceedings of the IEEE International Symposium on Biomedical Imaging, 29 April-2 May 2014, Beijing, China. [PDF]

  • Gopal Datt Joshi, Jayanthi Sivaswamy, Prashanth R and S R Krishnadas - Detection of Peri-papillary Atrophy and RNFL Defect from Retinal Images Proceedings of International Conference on Image Analysis and Recognition , Aveiro 2012. [PDF]

  • Gopal Datt Joshi,, Jayanthi Sivaswamy and S. R. Krishnadas - Depth discontinuity-based cup segmentation from multi-vieew colour retinal images IEEE Transactions on Biomedical Engineering, 59(6), pp. 1523-1531,2012. [PDF]

  • Gopal Datt Joshi, Jayanthi Sivaswamy and S.R. Krishnadas - Optic Disk and Cup Segmentation from Monocular Colour Retinal Images for Glaucoma Assessment IEEE Transactions on Medical Imaging, 30(1), pp. 1192-1205, June,2011. [PDF]

  • Gopal Datt Joshi, Rohit Gautam, Jayanthi Sivaswamy and S. R. Krishnadas - Robust Optic Disk Segmentation from Colour Retinal Images Proceedings of Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP'10),12-15 Dec. 2010,Chennai, India. [PDF]

  • Gopal Datt Joshi, Jayanthi Sivaswamy, Kundan Karan, Prashanth R and S.R. Krishnadas - Vessel bend-based cup segmentation in retinal images Proceedings of 20th International Conference on Pattern Recognition (ICPR'10),23-26 Aug. 2010, Istanbul, Turkey. [PDF]

  • Gopal Datt Joshi, Jayanthi Sivaswamy, Kundan Karan and S.R. Krishnadas - Optic Disk and Cup Cup Boundary Detection Using Regional Information Proceedings of IEEE Internation Symposium on Biomedical Imaging: From Nano to Macro(ISBI'10), pp.948-951, 14-17 April, 2010, Rottendam, Netherlands. [PDF]

Clinical Abstracts

  • L. Chakrabarty, R. Krishnadas, G. D. Joshi, J. Sivaswamy - Automated Fundus Image Assessment:A Novel Screening Method for Glaucoma, 28th Congress of the Asia-Pacific Academy of Ophthalmology (APAO-AIOS), Hyderabad, 2013 & ASIA-ARVO, New Delhi, 2013.