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prediction and experiment results shows similar feature as the eye with little high-order aberration shown in Fig. 13.9. These results show the potential applications that may result from developing new ophthalmic devices and technology.

13.5. Conclusion

In this contribution, we have indicated the broad spectrum of potential applications of the accurate computational modeling of human ocular optics. The application areas are diverse, and the ability to provide reliable predictions offers significant advantages and benefits.

General and population based eye modeling are significant for gaining knowledge of visual optics and studying the development of eye diseases. The personalized eye modeling, on the other hand, provides promising features in assisting ocular surgery and in designing customized spectacle, contact, or intraocular lens. Both types of modeling could be applied to predict visual changes under specified environmental or physical conditions. Furthermore, the computer simulations of ophthalmic measurements that utilize eye-modeling techniques offer a comprehensible tool for medical training.

Our selection of a specific commercial optics code is offered only as an example of the capabilities that are available today. The detailed procedures and selections employed for this code that are described in this work are indicative of the options and parameters necessary to obtain quantitative solutions. Finally, careful verification of computational techniques is essential to ensure the reliability and accuracy of the predictions.

The authors would like to acknowledge the research support of the Center for Laser Applications of the University of Tennessee Space Institute and the support for eye modeling and KC research of the National Eye Institute grants R21 EY18385 and R21 EY018935.

References

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12.Goss, D.A.,VanVeen, H.G., Rainey, B.B., and Feng, B. Ocular components measured by keratometry, phakometry, and ultrasonography in emmetropic and myopic optometry students. Optom Vis Sci 74:489–495, 1997.

13.Dubbelman, M., Sicam, V., and Van der Heijde, G.L. The shape of the anterior and posterior surface of the aging human cornea. Vision Res 46:993–1001, 2006.

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Chapter 14

Automating the Diagnosis,

Stratification, and Management

of Diabetic Retinopathy Using

Content-Based Image Retrieval

in an Ocular Telehealth Network

Thomas P. Karnowski , Luca Giancardo , Deniz Aykac , Kenneth W. Tobin , Yaqin Li†, Seema Garg‡, Michael D. Abramoff§, Matthew T. Tennant¶, and Edward Chaum¶

Diabetic retinopathy (DR) is the leading cause of blindness among working aged adults in the industrialized world. Diabetes currently affects 246 million people worldwide and is anticipated to affect as many as 380 million people by 2025.1 Every year an additional seven million people develop diabetes worldwide. Currently, 80% of people with diabetes live in lowand middle-income countries. Diabetics in many of these countries, including India (40.9 million population) and China (39.8 million population), have limited access to health care resources. The largest increases in diabetes prevalence are taking place in these and other developing countries. Based upon the epidemiology of diabetes, the number of patients that will

Oak Ridge National Laboratory, Oak Ridge, TN 37831.

†Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN 38152.

‡School of Medicine, University of North Carolina, Chapel Hill, NC.

§Department of Ophthalmology & Visual Sciences, University of Iowa Health Care.

¶The University of Tennessee Health Sciences Center, Hamilton Eye Institute, 930 Madison Avenue, Suite 731, Memphis, TN 38163.

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need to be screened for DR will soon exceed one million patients per day, worldwide.

The seminal DR studies and the early treatment of DR studies have clearly demonstrated the efficacy of laser treatment in reducing the risk of severe and moderate vision loss from proliferative DR and macular edema, respectively. The Wisconsin epidemiology study of DR showed that 90% of Type 1 diabetics (insulin-dependent, onset <30 years of age) and up to 84% of Type 2 diabetics (onset after 30 years of age, +/− insulin dependence) have some degree of DR after 10–15 years.2,3 Despite the known association of diabetes with vision-threatening DR, less than half of the diabetic patients in technologically advanced health care systems such as the United States are screened for retinopathy in any given year.4 Remarkably, one-quarter of Type 1 diabetics and one-third of Type 2 diabetics have never had an eye examination.5

In its November 2005 report “Prevention of Blindness from Diabetes Mellitus,” the World Health Organization (WHO) noted that in many developing countries, there are too few medical and diagnostic resources to provide even basic eye care to the general population, much less specialized care for advanced DR. However, the use of photographic systems coupled with expert interpretation could increase the ability of primary care providers (PCPs) to detect and manage early DR. The WHO identified key goals of integrating photographic diagnostic systems into DR management:

(1) assessing system performance relative to the current gold standard of reading centers, (2) validating success in providing access, and (3) demonstrating health outcomes benefits over the current methods.

Commercially available high-quality mydriatic and nonmydriatic cameras generate high-quality digital data sets that have the potential to enhance our ability to screen large and at-risk populations for DR effectively to identify those in need of treatment, and thus, significantly reduced disease morbidity. Novel computer-based image analysis and diagnostic methods that can leverage this data have the potential to improve the sensitivity and specificity of remote retinal diagnosis in large patient populations, and to monitor therapeutic responses to treatment. The current paradigm applied to the remote assessment of retinal disease consists of “store and forward” digital retinal photography with subsequent image analysis by physicians or certified technicians at commercial and academic reading centers. The

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reading center method is an established and validated method, with high sensitivity and specificity for retinal disease detection and quantification in clinical trials.6−8 This method is labor intensive, which means that it is more costly and slower than fully automated systems.

To achieve the goals of large population-based DR detection and management, automated methods must be developed and implemented. Current and even future public health care delivery models based upon manual analysis of retinal images simply cannot accommodate the need to screen over one million patients every day for DR. A new paradigm is required. An example paradigm is the office EKG, in which a computer algorithm generates a diagnostic reading at the point of care, in real time. The application of computer-based image analysis to aid in the diagnosis of DR has the potential to facilitate disease detection and management by increasing throughput, reducing costs, and potentially automating diagnostic capabilities within integrated telemedical network delivery systems.

Current image analysis algorithms can detect the anatomic features of the retina and retinal lesions using color and monochromatic retinal images.9,10 A common approach is to locate of important anatomic structures in the eye, normalize images to accommodate illumination and contrast,11 segment the vascular structures,12,13 and exploit the geometric relationship that exists between the vasculature, optic nerve (ON), and macula in the retina.14,15 Other approaches to image analysis in the retina include location regression, pixel classification, and graph search algorithms,16 and techniques, such as dynamic contours, and model-based approaches17,18 to name a few.

Content-based image retrieval (CBIR) is the process of retrieving related images from very large database collections, based on their pictorial content.19,20 Pictorial content is defined by a set of intrinsic features extracted from an image that describe the attributes that the human brain uses to comprehend the image, such as the color, texture, shape, and regional structure of the image or specific objects within it. The pictorial content feature list becomes the index for storage and retrieval, and facilitates searches of large image datasets based upon the specific visual characteristics of a query image. An unknown query image is submitted to the CBIR library and its anatomic structures and lesions found, followed by feature extraction and analysis. The features become an index for searching in the database and are used to locate a population of similar images contained in the library,

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which can then be compared statistically to the query image. Low-level analyses employ feature description models and higher-level analyses use perceptual organization and spatial relationships to extract clinically relevant semantic information. The method also has the potential to utilize associated nonimage data (e.g. the clinical data such as disease history, previous treatments, and visual acuity), which may enhance the diagnostic capabilities. The database library representing the image population is constructed and an indexing tree is generated to facilitate rapid searching. The (Bayesian) probabilistic framework of CBIR permits us to make statistically relevant predictions regarding the presence, manifestations, and severity of common retinal diseases from digital images in an automated and deterministic manner.21 The CBIR method provides a unique ability to describe “off-normal” occurrences in images based on deviation from normal retinal anatomy.

Our objective in this chapter is to describe our research of the use of CBIR with biomedical image databases in the clinical setting of a regional telemedical network designed for the remote diagnosis of DR in underserved patient populations using high-throughput methods to meet the growing need for disease assessment and management. We first describe a network infrastructure for the automated diagnosis of DR that provides a method for low-cost, real-time diagnosis and patient referral in the primary care environment. Our telemedical network is designed for remote diagnostics and, thus, the design of the underlying network infrastructure emphasizes high-speed data transmission for real-time image analysis, secure data encryption, ease of installation, and transmission of patient-sensitive information to meet federal regulations.

We then describe the capabilities of CBIR to classify, predict, diagnose, and otherwise learn from the informational content encapsulated in historical image repositories. We will demonstrate these principles using specific examples from our biomedical applications to describe anatomic segmentation, statistical feature generation and indexing, efficient retrieval architectures, and predictive results in the evolving diagnostic retinal image search and analysis (RISA) network. The benefits of automation will reduce the cost of health care management and speed up health care delivery, critical elements for managing the health of rapidly expanding at-risk populations in the coming decades.

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