272	M. Iorga and G. Dougherty

The Waikato Microaneurysm Detector [26, 27] modified this procedure to work on full-color retinal images. The green plane of the retinal images was used to find all candidate objects. After normalization by subtracting a median filtered version of the image, noise was removed by median filtering using a small kernel. A top-hat transform was performed by morphological reconstruction [28], using an elongated structuring element at different orientations to detect the vasculature. Following the removal of the vasculature and a microaneurysm matched filtering step, an adaptive threshold isolated microaneurysm candidates and region-growing on the shade-corrected green plane at the positions of candidates was used to isolate the morphology of the underlying candidate. A number of features based on the color, intensity, and shape of the candidates were extracted [27], and a na¨ıve Bayesian classifier was used to assign a likelihood to each of the found candidate objects that it is a true microaneurysm. Sensitivity can be traded against specificity by varying a threshold probability that determines whether or not a candidate should be considered a microaneurysm.

A recent study [29], using a variation of this processing method and a k- nearest neighbor (k-NN) classifier, reported a sensitivity of 88.5% for detecting microaneurysms. An alternative method [30] used template matching in the wavelet domain to find the microaneurysm candidates. It assumed that microaneurysms at a particular scale can be modeled with two-dimensional, rotation-symmetric generalized Gaussian functions.

12.3 Image Databases

The STARE (see footnote 1) and DRIVE (see footnote 2) [31, 32] databases of retinal images have been widely used to compare various vessel segmentation algorithms. A subset of the DRIVE database is being used for an online challenge (the Retinopathy Online Challenge [21]) to compare algorithms used to detect microaneurysms.

The Messidor project database (see footnote 3) is the largest database of retinal images currently available on the Internet. It was established to facilitate studies on computer-assisted diagnoses of diabetic retinopathy, and comprises 1,200 color fundus images of the posterior pole acquired using a Topcon TRC NW6 camera [Topcon Medical Systems, Inc. (TMS), of Paramus, NJ] with a 45◦ field of view. The 24-bit RGB color images are of various sizes: 1,440 × 960, 2,240 × 1,488 or 2,304 ×1,536 pixels. Eight hundred of the images were acquired with pupil dilation and 400 without dilation.

Two diagnoses are provided by expert ophthalmologists for each image: the retinopathy grade and the risk of macular edema. The retinopathy grade [0 (normal), 1, 2 or 3] is based on the numbers of microaneurysms (MA) and hemorrhages (H), and whether there is neovascularization (NV = 1) or not (NV = 0), using

0:(MA = 0) AND (H = 0)

1:(0 < MA ≤ 5) AND (H = 0)

12 Tortuosity as an Indicator of the Severity of Diabetic Retinopathy

273

Fig. 12.1 Example images from the publicly available Messidor database: (a) grade 0, (b) grade 1, (c) grade 2, and (d) grade 3

2:(5 < MA < 15) OR (0 < H < 5) AND (NV = 0)

3:(MA ≥ 15) OR (H ≥ 5) OR (NV = 1)

Typical images, for grades 0 through to 3, are shown in Fig. 12.1.

This reflects the order in which these pathologies appear, viz. first microaneurysms, then hemorrhages, and then neovascularization (which results in PDR). Grade 1 corresponds to the R1 (minimal) and R2 (mild) grades, and grade 2 to the R3 (moderate) and R4 (severe) grades, the main categories of the early treatment diabetic retinopathy study (ETDRS) classification system [33] used in clinical trials. Hard exudates were used to grade the risk of macular edema. We looked at example images from all grades, with a view to exploring whether tortuosity might be a useful indicator of the severity of the retinopathy.

In addition to the Messidor project database images, each assigned a retinopathy grade, we were supplied with a second database of 82 different images (with names MA Originaux XX, where XX is the Image ID starting from 01: courtesy of Dr. Jean-Claude Klein, Center of Mathematical Morphology of MINES ParisTech). Each image is 1,440 × 960 pixels (and 118 pixels/cm). The microaneurysms in these images were particularly clear, and were manually identified by three expert ophthalmologists. We will refer to this database as the “marked database.”