Tuesday 10 April 2012

DIGITAL IMAGE PROCESSING Retinal Image Analysis to Detect and Quantify Lesions Associated with Diabetic Retinopathy

Retinal Image Analysis to Detect and Quantify Lesions Associated                 with Diabetic Retinopathy

Abstract—An automatic method to detect hard exudates, lesion associated with diabetic retinopathy, is proposed. The algorithm found on their color, using a statistical classification, and their sharp edges, applying an edge detector, to localize them. A sensitivity of 79.62%  with a mean number of  3 false positives per image is obtained in a database of 20 retinal image with variable color, brightness and quality. In that way, we evaluate the robustness of the method in order to make adequate to a clinical environment. Further efforts will be done to improve its performance.Keywords— Diabetic retinopathy, hard exudates, image processing, retinal images.
DIABETIC retinopathy (DR) is a severe eye disease at affects many diabetic patients. It remains one of the leading causes of blindness and vision defects in developed countries. There exist effective treatments that inhibit the progression of the disease provided that it would-be diagnosed early enough. But DR is usually asymptomatic in its beginning, so diabetic patients do not undergo any eye examination until it is already too late for an optimal treatment and severe retinal damages have been caused. Regular retinal examinations for diabetic patient’s guarantee an early detection of DR reducing significantly the incidence of blindness cases. Because of great prevalence of diabetes, mass screening is time consuming and requires many trained graders to examine the fundus photographs searching retinal lesions. A reliable method for automated assessment of the presence of lesions in fundus images will be a valuable tool in assisting the limited number of professional and reducing the examination time. This paper focuses only in the automatic detection of one of the lesions associated with DR: hard exudates. They usually appear in the fundus photographs as small yellow-white patches with sharp margins and different shapes. Among lesions caused by DR, exudates are one of the mostoccurring early lesions [1]. So the detection and quantification of them will contribute to the mass screening and assessing of DR.Some investigations in the  past have identified retinal exudates in fundus images based on their gray level [2], [3],their high contrast [4-7] or their color [8],[9]. Because the brightness, contrast and color of exudates vary a lot among different patients and, therefore, different photographs, these method would not work in all the images used in clinical environment. The main improvement introduced by the technique described in this paper is its robustness to the variable appearance of retinal fundus images to obtain an optimal performance in all types of images, in contrast to these other approaches.
The method attempts to detect hard exudates using two features of this lesion: its color and its sharp edges. So hard exudates extraction is carried out in the following stages:
·         Detection of the optic disk and the blood vessels
·         Detection of yellowish objects in the image.
·         Detection of objects in the image with sharp edges.
·         Combination of the previous steps to detect Yellowish objects with sharp edges.
A.    Detection of the optic disk and the blood vessels.
 In order to localize these main features, we build on some works developed by other authors. We follow the method proposed in [7] to detect the center of the optic disk (OD). This method determined a number of candidate regions with the brightest pixels in intensity image. Then the PCA based model approach is applied to the candidate regions to give the final location of the OD. We also detect the disk boundary using a snake driven by an external fieldv(x,y)=[u(x,y),v(x,y)] called Gradient Vector Flow (GVF) [10] over the image

  (1)In this work the snake is initialized automatically as a circle placed in the center of the OD localized previously. The blood vessels are segmented applying the matchedfilter method described in [11] to enhance blood vessels and thresholding the image obtained.
B.  Detection of yellowish objects
The detection of this kind of objects is carried outperforming color segmentation based on the statistical classification method described in [8] and [9]. This method found on the fact that if a group of features can be defined so that the objects in an image map to nonintersecting classes in the feature space, then we can easily identify different objects classifying them into corresponding classes by a certain rule. For our algorithm, we have to discriminate between two classes: yellowish objects and background, which are perfectly characterized using only three color features (the luminance of the pixels in each plane (R,G,B)).  In order to map all the pixels in the image to one of these classes, an appropriate discriminant function has to be defined. Using the posterior probability and Bayes’ theory, we can obtained the minimum distance discriminant
Where i=1,...,N and N is the number of classes (in our caseN=2).So for each pixel X(xR,xG,xB), the distances Dyell(X) and Dback(X) are calculated. If Dyell(X) is less than Dback(X), then the pixel X is classified as yellowish lesion, otherwise it is classified as background. Cyell and Cback denote the center of each class in RGB space, which characterize the color of the yellowish objects and the background respectively. Therefore, one problem has to be resolved before applying this method: the definition of the features center Cyell and Cback. In [8] and [9], they are selected as a global value after obtaining them from different windows in training samples. In that way, it is taken for granted that all the images have the same fundus color, and that the exudates and the background appear with the same illumination and color. In practice, there is a wide variation in the color of fundus from different patient, strongly correlated to skin pigmentation and iris color. So, global values for Cyell and Cback can work in some images but fail in others. This problem can be resolved using specified feature centers for each image. To define them avoiding user interaction, we have to find pixels belonging to both classes in all the images. For background, we select a group of pixels that surrounds the contour of the OD obtaining in section A. And because of the fact that the OD usually has the same color as the exudates, the pixels that belong to the OD are used to identify the color of the yellowish objects. So we obtain for each fundus photograph the values of Cyell and Cback:

where m and  n are the number of pixels in yellowish and background region respectively that are used to calculate these centers and Yi and Bi are the vectors of the three color features in the different region Because of lighting variation, decreasing color saturation, skin pigmentation, etc, the color of lesions in some regions of an image may appear dimmer than the background color that is located in another region and would be wrongly classified. So it is of crucial importance to perform an adjustment for non-uniformity of illumination. But if a general method to avoid this phenomenon is applied, the color in some fundus photograph, due to the wide variation of this feature in different patients, could be modified introducing some strange effects. In this work, we use a new color image. This image is obtained performing an operation of the channels (N1, N2, N3) of the NTSC color space

and then converting the image obtained (N1´,N2, N3) into the RGB color space again. In that way, we improve both contrasting attributes of lesions and the overall color saturation in the image, achieving that the OD and the exudates appear with the same color independently of their location (Fig. 1.(b)). Hard exudates and other yellowish objects can be detected applying the minimum distance discriminant to all the pixels of this image, as shown in Fig. 1.(c). As well as hard exudates, other yellowish regions are detected, as the optic disk, other lesions (cotton wool spots and drusen), artifacts, etc.
C.  Detection of objects with sharp edges
An edge-finding operator can characterize the edge strength of the objects of an image. So, in our case, Kirsch’s mask (5) and different rotations of it are applied to the green component of the color fundus and the maximum response of them is selected to detect the edges in the fundus photograph.
Thresholding this image at grey level T1, we obtain objects with sharpest edges (Fig. 1.(d)). T1 is a parameter of the algorithm. If T1 is chosen too low, the sensitivity increases but the specificity decreases. Other objects in the images with sharp edges are also detected, as the optic disk, blood vessels, hemorrhages’, etc
D.  Combination of the two previous images
To detect only hard exudates and remove all the false positives introduces in the previous stages, we combine the two images obtained using a  Boolean operation, feature-based AND. In feature-based AND, ON pixels in one binary image are used to select objects (connected groups of ON pixels) in another image. Here we use the image with objects with sharp edges to select object in the image with yellowish elements, because in the last one the lesions are detected completely, not only their contours. In this way, we obtain lesions characterized by the two desired features: yellowish color and sharp edge. After that, we also get some false positives due to the papillary region and some artifacts near the vessels (because the reflection in young people). To reduce them, we remove a dilated version of the segmentation result of the detection of the OD and of the vessel in section. Fig.2.  shows the final image.

Fig.1.Images obtained applying the method to (a), (b) image after the enhancement, (c) detection of the yellowish objects, (d) detection of the objects with sharp edges.
Fig.2. Detection of hard exudates presented in Fig. 1. (a).
We have tested the algorithm on an data base of twenty576x768 digital images taken with a TopCon TRC-NW6SNon-Mydriatic Retinal Camera and have compared the results obtained by the algorithm with the performance of a specialist who marked the exudates on these images. For evaluation of the detection performance of the system the number of true and false positive clusters has to be determined for each image in the test set, while the segmentation threshold T1 is varied. In this way the true positive (TP) rate can be plotted as a function of the number of false positive (FP)  
Detections per image, using free-response receiver operating characteristic (FROC) curve. Each decision threshold results in a corresponding operating point on a curve. We believe that FROC analysis is an appropriate measure for our detection system, because there will be a trade-off between the TP rate and the number of FP detections per image. A true exudates is considered detected if the detected cluster overlaps at least 50% of its area. All findings outside the criterion are considered as false detections. The curve obtained is shown in Fig. 3., obtaining a T1=0.8 a sensitivity of  79.62%.
The best performance is achieved at the operation point with a sensitivity of 79.62% with a mean number of 3.2false detections per image. Some exudates are not detected due to their proximity to blood vessels or because they appear very faint, even after the proposed enhancement. Missing faint exudates has not a crucial importance since even human experts are not sure about some ambiguous regions. In the present work we have evaluated the system on an independent database of retinal images with variable characteristics to investigate its robustness. Due to the lack of a common database and a reliable way to measure the performance, it is difficult to compare the performance of our method related to those reported in the literature. Although some work [5], [7] show superior performance than our algorithm, the main improvement is that a good performance is obtained overall independently on the color, illumination, size, etc, keeping FPs low. This independence on the aspect of the image is obtained using a particular method for each image (to enhance them, to obtain the color of the background and exudates), unlike other authors which use global approaches for all of them. So the behavior of our algorithm is appropriate for a clinical environment. But there are some problems that deserve comment. First of all, the algorithm depends on other detection tasks, as the detection of the OD and blood vessels, making the results dependent of the successful of these methods. This indicates the further necessity of improving the robustness of these tasks. On the other hand, we have used the color of the OD to characterize yellowish regions but this cannot represent its real color. It could be a good idea to localize some exudates firstly and then use their color.

Other issues concerning ADDR
One of the issues arising from the use of digital images for diabetic retinopathy screening is the time and space involved in capture and storage of the files. Currently, the use of image compression using utilities such as Joint Photographic Experts Group (JPEG) have not been recommended, although there is some evidence that while large file compression significantly reduces the ability of automated detection programs, a compression ratio of 1:12 or 1:20 would produce little reduction insensitivity Another consideration for diabetic screening is the use of routine mydriasis. Hansen et al. (2004a) address the impact of pharmaco-logically dilated pupils on ADDR. They report a change in sensitivity before and after pupil dilatation of 90%and 97%,respectively,for detection of ‘red lesions’(hemorrhages’/micro aneurysms) and specificity before and after pupil dilatation was reported as 86% and75%,respectively (n ¼ 165 eyes of 83 patients). The use of routine mydrias is for diabetic screening is controversial. Currently, the National Screening Committee in England and Wales have recommended routine my-driasis for all screened patients, whereas the Health Technology Assessment Board for Scotland  only recommend mydriasis under certain defined circumstances.
Whilst the detection of sight-threatening diabetic retinopathy has received the most attention with respect to automated digital image analysis, other pathologies offer potential to use this tool as well, including morphological evaluations of the optic nerve in glaucoma and themacular region in age-related macular degenerationand retinopathy of pre-maturity (ROP) Table 1 summarises sensitivities and specificities of selected studies of ADDR.
In this work we have evaluated an automated detection scheme for one of the primary signs of DR: hard exudates. This lesion was identified by its color, using a statistical classification, and its sharpness of its edges, applying a Kirsch operator. After applying our method to 20 fundus photograph, the detection sensitivity for the hard exudates jumped from 65% to 85% when the number of FPs was kept low (3/image). Our results suggest that the system is competent to complement the screening of DR of ophthalmologists in their daily practice because it is very robust in the face of changes of the characteristic of the images. Future work will address the issue of improving the sensitivity by improving the  results of other tasks, as the detection of OD and blood vessels, and trying to localize faint and small hard exudates.
Fig.3.  FROC curve for a database of 20 retinal images using the developed method.
[1] D. Klein, B. E.. Klein, S. E. Moss et al, “The Wisconsinepidemiologic study of diabetic retinopathy. VII. Diabeticnonproliferative retinal lesions,” Ophthalmol., vol. 94, pp. 1389–1400,  1986.
[2] N. P. Ward, S. Tomlinson, and C. J. Taylor, “Image analysis of fundus photographs – The detection and measurement ofexudates associated with diabetic retinopathy,” Ophthalmol., vol.96, pp. 80–86,  1989.
[3] R. Philips, J. Forrester, and P. Sharp, “Automated detection and quantification of retinal exudates,” Graefe’s Arch. Clin. Exp.Ophthalmol, vol. 231, pp. 90–94, 1993.
[4] K. Akita and H. Kuga, “A computer method of understanding ocular fundus images,” Pattern Recogn., vol. 21, no. 6, pp. 431–443, 1982.
[5] T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “Acontribution of image processing to the diagnosis of diabetic retinopathy – Detection of exudates in color fundus images of the human retina,”  IEEE. Trans. Med. Imag., vol. 21, no. 10, pp.1236–1243, Oct. 2002.
[6] H. Li and O. Chutatape, “Fundus image features extraction,” in Proc. 22nd Annual Int. Conf. of the IEEE Engin. Med. Biol. Soc., EMBS’00, Chicago, IL, pp. 3071–3073.


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