Ординатура / Офтальмология / Английские материалы / Automated Image Detection of Retinal Pathology_Jelinek, Cree_2009

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of the presence of dot-hemorrhages and MAs are sufficiently similar there is usually no need for an automated MA detector to distinguish between them.

Microaneurysms are better visualized with fluorescein angiography with more MAs visible than in color fundus images. Better discrimination between MAs and dot-hemorrhages is also achieved as dot-hemorrhages tend to block out the background choroidal fluorescence and appear hypofluorescent. Nevertheless, other features, such as retinal pigment epithelium defects and capillaries seen end on, can appear similar to MAs and some care is needed when identifying individual MAs.

Microaneurysms are often the first sign of diabetic retinopathy. A positive correlation between the number of microaneurysms and the severity and likely progression of diabetic retinopathy has been demonstrated, at least for the early stages of the disease [2–5]. Microaneurysm formation and regression is a dynamic process with over half of the MA population observed to either form or regress within a 12 month period [6]. There is evidence that the turnover of microaneurysms, in addition to absolute counts, is a predictor of retinopathy [6; 7].

As microaneurysms are reasonably easy to describe, are a well visualizable target, and their numbers have definite clinical implications, they were one of the first lesions selected for automated image analysis. Early attempts focused on the detection of MAs in fluorescein angiographic images where they are better visualized. As fluorescein angiography poses an unacceptable health risk for eye-screening and color digital camera technology has advanced sufficiently, there is now a move to perform automated MA detection on color fundus images as an adjunct to automated eye screening.

6.2History of Automated Microaneurysm Detection

In the following an overview of the historical development of automated microaneurysm detectors is given.

6.2.1Early morphological approaches

The first attempts at automated microaneurysm detection date back to the early 1980s, and were on fluorescein angiographic images. Lay¨ et al. [8] described a morphological approach to MA detection. Image resolution and dynamic range were low (256 256 pixels with 100 gray levels), however, by using an imaging microscope to digitize film negatives, the digital image covered a small field-of-view of the macula so that MAs were well resolved. As negatives were used, the MAs appeared as dark dots, thus as localized minima in the intensity image. A tophat transform was applied to identify the local minima. Vessels were identified with the skeletonization of a further tophat transform using a linear structuring element at various orientations, and any local minima lying on vessels were eliminated as MAs. Ten angiographic images, containing a total of 177 MAs, were analyzed both by clinicians and the

automated MA detector. The automated MA detector achieved 58% sensitivity with 4.2 false detections per image for detecting and locating MAs .

This process was extended by Baudoin et al. [9] with extra morphological processing to remove false detections such as objects too small to be MAs. In this study 25 retinal images centered on the macula, containing a total of 936 MAs, were analyzed both by clinicians and the automated processing. The automated MA detector achieved 70% sensitivity for detecting and locating MAs with 4.4 false detections per image.

Spencer et al. [10] used matched-filtering to detect MAs and to remove falsedetections on vessels in angiographic images of the retina. Film negatives of the macula were projected onto a screen and digitized with a monochrome CCD camera at 512 512 pixel resolution. Preprocessing included radiometric correction for the illumination of the negatives, and subtractive shade-correction to remove choroidal fluorescence. MAs were identified by matched-filtering with a circularly symmetric two-dimensional Gaussian (s = 1 pixel) and a mask of size 5 5 pixels. Vessels were identified by matched-filtering with a mask of size 5 5 pixels and s = 2 pixels, and extra shape-analysis to identify long linear structures. Any MAs detected on the vessel network were removed. Testing was performed on six fluorescein angiographic images. Five clinicians also marked the positions of MAs, on both the digitized images and on standard photographs made from the film negatives. The automated detection performed almost as well as the clinicians working on the digitized images, however, the clinicians working on the digitized images performed poorly compared to the clinicians working on the photographs. It was concluded that the digitized image resolution was insufficient for reliable MA detection.

6.2.2The “standard approach” to automated microaneurysm detection

In response to the limits of the morphological processing developed by Baudoin and Lay,¨ and of the matched-filter approach of Spencer et al., researchers at Aberdeen University, Scotland, developed the image processing strategy for detection of MAs into what we shall call the “standard approach” of automated MA detection. This approach has since been adopted and enhanced by researchers not only at Aberdeen University, but also by a number of other groups. The standard approach [11] is described in the following and illustrated in Figure 6.1.

First, digitized retinal images are shade-corrected to even up the background illumination of the retinal image. Shade-correction is normally performed by creating an image of the background illumination often estimated by gross median or mean filtering of the retinal image. The background image is either subtracted from or divided into the retinal image. On fluorescein angiographic images subtractive shadecorrection is normally performed to remove choroidal background fluorescence as the choroidal illumination is an additive process (e.g., see [11–13]; also see [14] for more description of the illumination contaminants to angiographic images). Microaneurysm detection on color retinal images is best with divisive shade-correction as

FIGURE 6.1

Flow diagram of the “standard approach” to microaneurysm detection.

intensity shading is mainly due to changing reflectances of the retina, a multiplicative process.

In the standard approach, a matched-filter with a circularly symmetric 2-D Gaussian as the kernel is used to highlight MAs, but this process tends to false-detect on vessels. Vessels are therefore first detected by the morphological opening of the shade-corrected retinal image by a long linear structuring element that is rotated to various angles to pick up all vessel segments. The length of the structuring element is chosen to be short enough to fit inside curved vessels, and long enough that it cannot fit inside MAs [11] so that it detects vessels (and other large extended features) but not MAs. The detected vessels are then subtracted from the shade-corrected retinal image to effect a tophat transform. The resultant image contains small objects such as MAs and also, in practice, small objects left over from the vessels, such as boli of fluorescein if angiographic images are used.

The tophat transformed image is thresholded to detect likely MAs, which we refer to as candidates. One could terminate the process at this stage and take all candidates as detected MAs, however it is normally found that an acceptable sensitivity leads to an unacceptable specificity. Therefore the candidate locations are used to initiate region growing on the shade-corrected retinal image to delineate the underlying morphology of the candidate. Shape and intensity features are measured on the region-grown object and a classifier is used to refine MA detection. A much better specificity can then be achieved with very little loss in sensitivity. Because the candidates are region grown this method typically cannot detect MAs attached to vessels.

This approach was first described by Spencer et al. [11], who tested the process on fluorescein angiographic film negatives digitized at a resolution of 1024 1024 pixels covering a 35° field of the macula. The preprocessing included illumination correction to correct for vignetting incurred in the digitization. The tophat transform used a linear structuring element 11 pixels long rotated to eight orientations. The matched filter kernel, of size 11 11 pixels, was a two-dimensional circularly sym-

Hipwell et al. [16] of Aberdeen University modified the automated MA detector of Cree et al. [13] to work on red-free images of 1024 1024 pixel resolution of a 50° FOV retina either centered on the macula or nasal of the optic disc. Against a testing set of 62 images, containing a total of 88 MAs that broadly reflected the level of retinopathy expected in an eye-screening population, the automated MA detector achieved 43% sensitivity with 0.11 false-positives per image for detecting and correctly locating MAs. This less sensitive operating point was chosen as being more suitable for eye-screening as only one MA per image needs to be detected to identify retinopathy. The poorer ability to detect MAs in red-free images compared to using fluorescein angiography seems to have adversely affected the overall ability of the automated MA detector in this study. In an extensive trial using 3783 directly digitized red-free images from the diabetic eye-screening program the automated MA detector achieved a sensitivity of 81% at a specificity of 93% for detecting images that contained microaneurysms, and a sensitivity of 85% at a specificity of 76% for the detection of subjects with retinopathy [16]. It was concluded that the automated MA detector would be useful in a screening context to remove half the images that contained no retinopathy, thus would reduce the load of subsequent manual screening.

The Aberdeen automated MA detector [16], in conjunction with an automated exudate detector [17], was also reported in a comparative evaluation of digital imaging, retinal photography, optometrist examination, and automated retinopathy detection [18]. The automated detection again used red-free images of 1024 1024 pixel resolution of a 50° field-of-view (FOV). A total of 586 participants, of which 157 had retinopathy, were included. The automated retinopathy detector achieved 83% sensitivity at a specificity of 71% for detecting any retinopathy. This compares to 83% sensitivity at 79% specificity for manual detection of retinopathy from the digital red-free images, and 89% sensitivity at 89% specificity for manual detection of retinopathy from color slides. Olson et al. [18] conclude that the automated retinopathy detection is not yet ready for sole use as the screening mechanism but could form an important adjunct to reducing the workload of manual screening.

A number of other groups have used the standard approach as a starting point for developing automated MA detectors. Mendonc¸a et al. [19] implement the standard approach for detecting MAs in fluorescein angiographic images but only report results of testing on one image. Acha and Serrano [20] used the preprocessing of Mendonc¸a et al. [19] and extended the classifier to include features that are based on the correlation of various shape filters with the candidates. On five angiographic images with a mean of 100 MAs per image they achieve 82% sensitivity with 3.3 false positives per image. Serrano et al. [21] further enhance the image processing strategy by using the errors of linear prediction coefficients as a means of adaptively thresholding the matched-filtered image to generate seed-points for region-growing. The classifier appears to be essentially the same as Acha and Serrano [20]. On 11 angiographic images containing a total of 711 MAs they report a sensitivity of 91% for detecting MAs at 7 false-positives per image.

Yang et al. [22] report on the detection of MAs in low-resolution color retinal images. The green-plane of the color images is used for preprocessing and detection

of candidates. The removal of blood vessels in the tophat stage is improved with the use of a morphological reconstruction [23]. Notably, they dispense with the matched-filtering stage and threshold the tophat processed image to generate seed points for region growing. Basic shape features as well as the hue of the candidates as determined from the full-color information is used in classification. A preliminary test on 46 images (of healthy and diseased retinae) achieved 90% sensitivity at 80% specificity for detecting presence of MAs in the diseased retinal images. Lalonde et al. [24] use this MA detector in their RetsoftPlus retinal analysis software.

Raman et al. [25, 26] in a study on the effects of image resolution on automated diabetic retinopathy (DR) detection use an MA detector based on the standard approach. A tophat transform is used to isolate small objects in the shade-corrected image, and matched-filtering with a Gaussian model to detect vessels. Any small objects detected in the tophat that occur on vessels were removed. A genetic algorithm processes the MA detections from three 45° fields of an eye to arrive at a report on the presence of DR for the eye [26]. Color retinal images are used in the study but no mention is made of how the color information is used. On high resolution images (10 DR; 8 no diabetic retinopathy [NDR]) a sensitivity of 90% at a specificity of 88% was achieved for detecting retinopathy. The resolution of the images is not stated (other than they are digitized images of 35 mm film of 35° FOV using mydriasis), and the images were used for both training and testing. On low resolution images (640 480 pixels of a 45° FOV; nonmydriatic direct digitization) of 91 subjects (35 DR; 56 NDR), that form an independent test set, a sensitivity of 69% with a specificity of 66% was achieved for detecting retinopathy. This reinforces the conclusion of Spencer et al. [11] that digitization at approximately quarter megapixel resolution is insufficient for automated detection of microaneurysms.

Niemeijer et al. [27] find that the standard approach for segmenting candidates fails to segment all true MAs, thus they develop a new method to generate candidates that is based on pixel classification. After shade-correcting the image, the pixel intensities and firstand second-order Gaussian derivatives at various scales are provided to a k-nearest neighbor classifier that produces a probability map for each pixel to be a small red lesion. This is thresholded and filtered to remove vessel segments and objects that are too large, leaving the candidates. They test the standard approach and their new pixel-based approach for generating candidates and find that both methods fail to detect 14% of true microaneurysms, but when combined together into a hybrid system that figure is reduced to 10%. When coupled with the usual region-growing and final classification to detect MAs, the hybrid system achieved 100% sensitivity at 87% specificity for detecting the presence of retinopathy in the 50 training images (27 DR; 23 NDR) of 768 576 pixel resolution.

The standard approach suffers from other known problems. The region growing applied to delineate candidates can be unreliable if the candidate is on or near a vessel, in clusters of microaneurysms, or is in a region of unusual background texture. Most papers based on the standard approach terminate region growing when intensity increases beyond a locally determined threshold. If the resulting grown object does not accurately reflect the underlying shape, then the measured features are likely to be inaccurate, resulting in false-detections or missed microaneurysms. The