Automated Microaneurysm Detection in Fluorescein Angiograms for Diabetic Retinopathy

and feature-extraction procedures, automate image registration, and provide animation of images taken at different time intervals for an objectiveand computer-assisted image analysis. In this section, we discuss some of the computational, integrated systems that have been developed to analyze retinal images. Tsai et al.66 proposed an integrated image analysis tool called RIVERS (retinal image vessel extraction and registration system), which can be found at http://cgi-vision.cs.rpi.edu/cgi/RIVERS/. The tool encompasses the capabilities of automated vessel tracing, sub-pixel image registration, and the animation of images for ease of visualization. Cree et al.67 developed an automated system for the detection of MAs, which incorporates the techniques for image preprocessing, registration of images, fovea detection, and identifying MAs and their turnover. Later, Cree et al. proposed another MA detector, the Waikato MA detector,68 which is an automated system for the detection of MAs in retinal images that consists of the algorithms for shade correction, top-hat transform by morphological reconstruction (a Gaussian function template), and adaptive threshold determination. The MA detector was tested for 758 images, achieving the sensitivity and specificity of 85% and 95%, respectively. Jelinek et al.69 utilized the Waikato MA detector and compared its performance with that of optometrists on a dataset of 543 images with no retinopathy and 215 images affected with retinopathy. They observed that the optometrists achieved a 97% sensitivity and 88% specificity in contrast to the MA detector, which achieved 85% sensitivity and 90% specificity in detecting the retinopathy.

9.5. Conclusion

In this chapter, we have attempted to summarize the automated techniques for the detection of MAs in fluorescein angiographic sequences. MAs are important lesions for detecting and determining a patient’s stage of DR, since MAs are visible at an early stage and continue their presence in the later stages of the disease. An automated system for the detection of MAs encompasses the steps of image preprocessing, image segmentation, image registration, and image classification. A fully validated automated system can identify the retinal lesions and can provide significant advantages in cost and time for physicians and for early diagnosis of the disease.

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