The server web GUI provides an interface for authorized access of the diagnostic result. For the consulting ophthalmologist, the diagnostic result, together with the images and patient metadata, is reviewed through the web GUI for confirmed digital signature, encryption, and report to the referring physician. For a referring physician, the web GUI provides a secure access to the diagnostic result along with patient information. The server web GUI can be accessed through fixed or mobile electronic devices, provided that it has browser access to the web.

The encrypted PDF report contains patient metadata, the fundus image from both eyes, the diagnosis, and recommendation plan, encrypted and electronically signed by a consulting ophthalmologist. The report can either be accessed by an authorized user through the web GUI, or as a password-protected e-mail attachment for the referring physician.

5.2.3Performance Evaluation of the Network

Rapid data transmission and high throughput is one of the key features of the telemedical network. To validate the performance of the designed network infrastructure, the average time from image acquisition to report generation is applied as a metric for performance evaluation. The following system response time is measured by observing 60 sessions of data transmission between a fundus camera in an active clinic and the diagnostic server. In our implementation of the diagnostic network, we provide the ophthalmologist with the ability to manually assign a diagnosis before the automatic diagnosis engine is fully incorporated into the system. In the experiment, we estimated that the automatic diagnosis can be assigned within 20 s. The average time frame for each processing step is shown in the following table. A conservative estimation of the average response time from image acquisition to the generation of a diagnostic and management report for the end user is less than 2 min (Table 5.1). The network structure provides sufficiently high throughput for image analysis and diagnosis of diabetic eye disease [13].

Table 5.1 System response time in a real clinic setting

5.3Automated Image Analysis Overview

Our research in automated image analysis for ophthalmology pertains broadly to telemedical applications and intelligent systems for automated or semiautomated screening. In the former case, even when no automatic disease assessment is attempted, there is a requirement for image analysis to assess the quality of submitted images. In the latter case, we note, there has been active research in automated retina screening for some time (see review in [14]). Broadly, image processing and analysis of fundus images consist of defining the anatomical location (isolating the vessels, optic nerve, and macula) and identifying the pathology of the retina through segmentation of lesions such as microaneurysms and exudates.

In our research, we perform these basic steps, grouped into functional modules, as shown in Fig. 5.2. The first processing block, quality estimation, utilizes vessel segmentation as well as machine learning techniques. Images of sufficient quality and related exam information are saved in a database for physician diagnosis.

Our method features a parallel processing flow for computer diagnosis of the retina images. The anatomic structures (optic nerve and macula) are located using properties of the vessel segmentation and an optic nerve location algorithm described below. The lesion detection focuses currently on microaneurysms and exudates, the major features of diabetic retinopathy. The lesion population description module computes

Fig. 5.2 Functional modules for retinal image management, feature extraction, and diagnostic assignment using CBIR. Quality estimation is performed, and images of sufficient quality are submitted to the system for anatomic localization of the optic nerve and macula. Lesion detection

is then performed, characterized, and submitted to the image library as an image retrieval, where similar images are found in the database and their corresponding diagnosis used to estimate the disease state of the query image

Fig. 5.3 Quality estimator. The vascular tree of the image is segmented, and the color of the original image is measured using an RGB histogram. The vessel density within the annular/wedge regions is measured and passed to a pattern classifier function which produces a numeric

measurement of the similarity between the submitted image and a library of good examples. Submissions that are above a threshold are deemed “good” quality and are entered into the network for review

time in the primary care setting so that inadequate images may be retaken. Therefore, we required a fast method for timely feedback to the camera operator, with computational constraints of a standard personal computer such as that used in fundus camera platforms. Our development work here is summarized, but more detail is available in [19] and [20]. Broadly, the assessment consists of segmenting the vessel structure, saving this result for subsequent processing, then estimating the quality based on the vessel structure and fundus color.

example [21–25], with a comparison of methods using a public database with hand-segmented data published in [26]. The study found that all reviewed methods performed reasonably well, with the method of [24] virtually identical to that of the second observer. However, several methods scored almost as well, and in our work, we implemented the method described in [27, 28] which uses morphological reconstruction elements to estimate the vascular tree. Our implementation in the telemedical network is fairly fast, with a mean time estimate of less than 1 s while running on a 1.66 GHz Intel-based PC server.

5.3.2Vascular Tree Segmentation

Vessel segmentation is important in general for three main reasons: some ocular diseases (e.g., retinal vein occlusions) may be diagnosed through analysis of the vascular tree; the distinct characteristics of the vessels can provide landmarks for registration of multiple images of the same retina (e.g., for applications in fluorescein angiography); and finally, the vessels assist in locating the optic nerve and fovea. In our system, we add a fourth reason: quality assessment, since generally, good quality images will have a well-defined vessel structure. Consequently, there has been much research on vessel segmentation for retina images and in medical imaging in general, for

5.3.3Quality Evaluation

In the actual quality evaluation, the vascular image is divided into annular and wedge-shaped regions which are then used to estimate the coverage of the vascular tree. This process is summarized in Fig. 5.3. Images which do not achieve adequate coverage in some regions will be regarded as an outlier by the machine learning system (or classifier) and will fail the quality assessment. In addition, the color of the fundus is measured by using a histogram of the RGB values. The region coverage and color measurements are then combined into a single feature vector. During initial development, a set of images from the telemedical