Prerna Sethi and Hilary W. Thompson

been reported for automated MA detection using fluorescein angiography because of the modality’s increased sensitivity as MAs are best seen on fluorescein angiograms.1 These methods usually follow a sequence of computational processing steps. The first steps include image preprocessing for noise removal and contrast enhancement for discrimination between MAs and blood vessels. Next, image registration is performed to reduce the errors in alignment and scaling in the images so that the same eye images obtained at different visits are registered. Vasculature processing assists in the separation of vessel cross-section from the MA cross-section. Finally, classification is performed to discriminate the actual MAs based on the abnormality and severity of false detections, which are based on extracted features. The development of a fully automated system for MA detection and quantification will assist in prompt diagnosis at an early stage and prevent damage to the retina of DR patients.

9.1. Introduction

Diabetes is one of the most prevalent chronic diseases caused by an abnormal increase of glucose in the blood. Diabetes affects a large and varied portion of the population and is ranked the fifth deadliest disease in the United States. The International Diabetes Federation (IDF) indicates that diabetes currently affects 246 million people worldwide and is expected to affect 380 million by 2025. In terms of economic impact, the IDF projected that 232 billion dollars were spent globally for the treatment and prevention of diabetes and its associated complications. DR is an ocular complication of long-term diabetes that changes the tiny blood vessels in the retina by making them swell and causing them to leak fluid. DR is a progressive pathology and is one of the four main causes of problems associated with sight and is reported to be the number one cause of blindness in people younger than 50.2,3 Due to its damaging effects and pathologic complications in sight and vision, DR has attracted the interest of researchers in the fields of biomedical computation and ophthalmology.

DR is a progressive disease that has stages of severity, which are characterized by the number and types of lesions it produces on the retina. The symptoms of DR can drastically change the texture appearance of the retina. The tiny blood vessels in the retina can cause leakage resulting in

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the formation of features such as MAs, cotton-wool spots, hemorrhages, or exudates.4,5 Furthermore, the images of the disease can vary widely depending on the individual patient and the severity of the disease. Based on the number and type of lesions present, DR can be characterized into four stages6:

1.Mild nonproliferative retinopathy is the earliest stage of DR. During this stage, only MAs (small swellings in the retina’s tiny blood vessels) occur.

2.Moderate nonproliferative retinopathy occurs as the disease progresses. During this stage, several MAs and retinal hemorrhages are present, along with some blood vessels, which may cause retinal blockage. Cotton-wool spots and some venous bleeding may become visible.

3.Severe nonproliferative retinopathy is the stage in which many blood vessels are blocked, depriving several areas of the retina of adequate blood supply. The following one or more symptoms may be present at this stage:7

•more than 20 intraretinal hemorrhages in each of the four quadrants,

•definite venous bleeding in two or more quadrants, and

•prominent intraretinal microvascular abnormalities in one or more quadrants.

Severe nonproliferative retinopathy carries a 50% chance of progression to proliferative retinopathy in one year.

4.Proliferative retinopathy is the advanced stage of DR, in which the signals sent by the retina for nourishment trigger the growth of new blood vessels. These newly developed blood vessels are abnormal and fragile and have thin walls. They grow along the retina and along the surface of the vitreous gel that fills inside the eye. The leakage of these blood vessels can cause severe vision loss and even blindness.

DR can be treated in many ways, depending on the stage of the disease and the specific problem that requires the fastest physician attention and care. However, the retinal surgeon relies on several tests to both monitor the progression of the disease and make decisions for the appropriate treatment. A comprehensive eye examination includes a visual acuity test, a dilated eye examination, and tonometry, combined with traditional retinal

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imaging methods such as the use of fundus images, fluorescein angiograms, and optical coherence tomography (OTC) to provide information about the circulatory system and the condition of the fundus background (retinal structure). Among those tests, intravenous fluorescein angiography (IVFA) has emerged as one of the most important diagnostic procedures. IVFA is also employed to diagnose other retinal vascular pathologies, such as age-related macular degeneration (AMD, a degenerative condition of the most sensitive area of the retina), retinal vessel occlusions, and many other problems related to retinal circulation.8−10 Review of IVFA data requires individual, manual inspection of a series of images or of a single frame of color-filtered, monochrome views of the retinal circulation, where each individual frame is stored as a separate 35-mm slide or digital image. Currently, this image processing and analysis is performed manually, a time-consuming process that introduces the potential for inaccurate diagnosis.

MAs are visible early and continue their presence as DR progresses. Klien et al.1 have reported a strong correlation between an increase in the number of MAs over time and the early development of DR. However, in the normal fundus photographs, MAs may be confused with small dot hemorrhages due to reduced visibility. The IVFA procedure enhances the visibility of these lesions. After the venous filling phase, fluorescein angiograms depict MAs as small, round, hyperfluorescent objects and edges better defined than those in the retinal fundus camera images, making the detection of MAs from fluorescein angiograms a preferred method over the fundus camera images. Following this phase, manual MA counting can quantify the progress and stage of the disease. Computed MA detection can potentially be more rational than the manual process, which is prone to be tedious and time consuming, and introduces the potential for an inaccurate diagnosis. For the reasons stated above, the manual counting of MAs is not used in current clinical practice. Automated MA detection aids the early detection of DR, as it assists in quantifying MAs and differentiating them from other features. Therefore, in this chapter, we will focus on this type of lesion.

Our objective in this chapter is to present a comprehensive study on the current automated MA detection techniques. Several methods have been reported for automated MA detection using fluorescein angiography because of the modality’s increased sensitivity, as MAs are best seen

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