270	M. Iorga and G. Dougherty

unfavorable clinical consequences [7–9]. The tortuosity of intracranial arteries, for example, has been implicated in the risk of aneurysm formation due to the high shear stress weakening the outer walls of the arteries [10]. The process may be similar in diabetic retinopathy. As the disease develops, increased tortuosity of the retinal blood vessels may result in the weakening of the outer walls and precede the formation of microaneurysms, which can leak fluid into the retina and cause swelling of the macula.

Microaneurysms are often the first clinical sign of diabetic retinopathy, and are seen as intraretinal deep red spots 10–100 μm in diameter. The significance of microaneurysm counts and their close correlation with the severity of the disease are well documented [11,12]. Microaneurysm formation and regression are dynamic processes [13], where microaneurysms form and then later clot and regress. More than 50% of them either form or regress within a 12-month period [14]. There is evidence that turnover, as well as absolute counts, is an early indicator of retinopathy progression [12, 15]. Rupture of microaneurysms gives rise to small round dot hemorrhages, which are indistinguishable from microaneurysms in color fundus images. Hemorrhages present a range of size, color, and texture from the dot hemorrhages, through blotch (cluster) hemorrhages to larger boat-shaped or flame-shaped hemorrhages. A pattern recognition approach may well be required to reliably detect all the variants.

White lesions comprise exudates, cotton wool spots, and drusen. Hard exudates are caused when weakened blood vessels in the eye leak lipids onto the retina, which in turn block it from sensing light. This results in blurred or completely obstructed vision in some areas of the eye. Since exudates are made of lipids, they appear as light yellow in fundus images. Early automated detection systems used thresholding of red-free images [16, 17], while a more recent study used a multilayer neural network to detect exudates [18]. The appearance of microaneurysms and hard exudates in the macular area is more serious, and is identified as “Diabetic Maculopathy” so as to highlight the potential sight-threatening nature of this condition.

As the disease advances (preproliferative retinopathy), circulation problems cause the retina to become more ischemic and cotton wool spots to become more prevalent. In proliferative retinopathy (PDR), new fragile blood vessels can begin to grow in the retina in an attempt to restore the malnourished area and prevent it from dying. These new blood vessels may leak blood into the vitreous, clouding vision. Other complications of PDR include detachment of the retina due to scar tissue formation and the development of glaucoma, an eye disease resulting in progressive damage to the optic nerve.

12.2 Automated Detection of Diabetic Retinopathy

Many studies have been initiated worldwide to develop advanced systems for the automated detection and monitoring of diabetic retinopathy. They comprise image analysis tools for detecting and measuring common lesions (such as

12 Tortuosity as an Indicator of the Severity of Diabetic Retinopathy

271

microaneurysms, hemorrhages, and exudates) and may include indexing and automated retrieval techniques applied to image databases. A major issue is how to accurately and objectively assess results from different studies.

Microaneurysms are among the first signs of the presence of diabetic retinopathy and their numbers correlate well with the severity of the disease in its early stages [12, 13]. Since microaneurysms are extremely small and similarly colored to the background, they are very tedious to measure manually. Most publications on automated microaneurysm detection use monochromatic data from one of the color planes of a fundus image, even though full color information is available. (Microaneurysms are better visualized with fluorescein angiography but it is more invasive and therefore less practical for screening purposes). The green plane normally contains the best detail; the red plane, while brighter and sometimes saturated, has poorer contrast; and the blue plane is dark and of least use. Hemoglobin has an absorption peak in the green region of the spectrum, so that features containing hemoglobin (e.g., microaneurysms) absorb more green light than surrounding tissues and appear dark, giving the green plane a higher contrast. Red light penetrates deeper into the layers of the retina and is primarily reflected in the choroid, explaining the reddish appearance of fundus images. Because red light has a lower absorption than green in the tissues of the eye, the red plane has less contrast. Blue light is mostly absorbed by the lens, and then by melanin and hemoglobin, and is the most scattered light, so the blue plane shows very little contrast.

However, the appearance of color retinal images can vary considerably, especially between people of different race [16, 19], so that it may be prudent to use all the color information and to employ some form of color normalization. For example, divisive shade correction can be applied to each of the color planes, and then the individual contrasts normalized to a specific mean and standard deviation [20]. This process retains the overall shape of the color image histogram, but shifts the hue (which is often dominated by the ratio of green to red in retinal images) to be consistent between images.

12.2.1 Automated Detection of Microaneurysms

Over the years, a variety of algorithms have been used to automatically detect microaneurysms [21]. For example, a morphological top-hat transformation with a linear structuring element at different orientations was used to distinguish connected, elongated structures (i.e., the vessels) from unconnected circular objects (i.e., the microaneurysms) [22]. A shade-correction preprocessing step and a matched filtering postprocessing step were then added to the basic detection technique [23–32].1,2,3

1http:www.ces.clemson.edu/ahoover/stare.

2http://www.isi.uu.nl/Research/Databases/DRIVE/.

3http://messidor.crihan.fr.