All people with diabetes have the risk of developing diabetic retinopathy, a vision-threatening complication. Despite advances in diabetes care over the years, diabetic retinopathy remains a potentially devastating complication. Early detection and timely intervention or treatment can reduce the incidence of blindness due to diabetic retinopathy. Recent years, diagnosis based on digital retinal imaging has become an alternative to traditional face-to-face evaluation. The potential benefits of automated analysis of digital images of diabetic retinopathy have been shown in existing studies. However, no current computer-based systems can achieve the same level of performance of human experts. This proposal takes a new perspective in developing a computer-aided system for improved diagnosis of diabetic retinopathy, by exploring novel computational methods for retrieving clinically-relevant images from archived database with prior diagnosis information, for a given novel image. Images are considered as being clinically relevant if they contain the same types of lesions with similar severity levels. Research and development on content-based retinal image search/retrieval is still in its infancy, with only limited success, largely due to the challenge of explicitly coding expert-knowledge into a computational algorithm. To deal with the challenge, this research project takes a distinctly different approach engaging a machine-learning approach, where a labeled image set is used to train a computer algorithm for analyzing other new images, with the focus of training on similarity in clinical relevance instead of image features. The training is enabled in part by the investigators'existing research on computer-based lesion simulation.
One specific aim of the research is to build a content-based image retrieval system that can provide a clinician with instant reference to archival images that are clinically relevant to the image under diagnosis. This is an innovative way of exploiting vast expert knowledge hidden in libraries of previously-diagnosed digital images of diabetic retinopathy for a clinician's improved performance in diagnosis. Another specific aim is to build an image information management system for diabetic retinopathy that supports the deployment of the retrieval system in a realistic clinical setting. In addition to the retrieval system, the direct outcome of the research also includes automated evaluation algorithms for diabetic retinopathy images with potentially improved performance compared with existing methods. In particular, the design of the proposed work allows different configurations of the resultant system according to the specific needs of a physician.
This project develops a computer-based system for improved diagnosis of diabetic retinopathy, a vision- threatening complication in people with diabetes. Early detection and timely intervention or treatment can reduce the incidence of blindness, and the computer-based system can potentially improve not only the speed but also the accuracy in diagnosing or screening patients with diabetic retinopathy.
| Chandakkar, Parag Shridhar; Venkatesan, Ragav; Li, Baoxin (2017) MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images. J Med Imaging (Bellingham) 4:034003 |
| Li, Baoxin; Li, Helen K (2013) Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends. Curr Diab Rep 13:453-9 |
| Venkatesan, Ragav; Chandakkar, Parag; Li, Baoxin et al. (2012) Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features. Conf Proc IEEE Eng Med Biol Soc 2012:1462-5 |
