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Automated Diabetic Retinopathy Diagnosis System

ABSTRACT

DETECTION OF EXUDATES USING GUI

Automated diabetic retinopathy diagnosis system is an essential requirement due to producing diabetic retinopathy patients around the globe. The primary intention of the research is to identify exudates in digital fundus image for diabetic retinopathy. In this particular study, we offer an efficient way for identifying and classifying the exudates as gentle exudates and hard exudates. Aside from these, this analysis compares three methods particularly Contrast Small Adaptive Histogram Equalization, Histogram Equalization and Mahalanobis Distance for boosting a digital fundus image to identify and pick the best one to classify exudates in Retinal images by adopting graphical user interface by using MATLAB. From the findings of the study, in the image enlargement application of blood vessels, Mahalanobis distance is recognized as the best algorithm. It was noticeable from the research that the monitoring and detecting exudates in the fundus of the attention are essential for diabetics. Moreover, it implies that hard and delicate exudates are, the burkha tool of diabetic retinopathy that may be quantified automatically. Furthermore to these, it would appear that drawbacks must be solved to predict a proper detection method for exudates in digital fundus images. From your conclusions, it was visible that suited algorithm needs to be selected and verified on several images which provide likely and excellent benefits.

LIST OF TABLES

  1. Comparison of Histogram Equalization (HE),

Contrast Limited Adaptive Histogram Equalization (CLAHE)

and Mahalanobis Distance(MD). . . . . . 14

LIST OF FIGURE

  1. Image before enhancement
  2. Histogram before enhancement
  3. Image after histogram equalization
  4. Histogram after HE
  5. Image after CLAHE
  6. Histogram after CLAHE
  7. Image after Mahalanobis distance enhancement
  8. Histogram after Mahalanobis distance enhancement
  9. Flow chart of the method
  10. CIELab color space
  11. Input image
  12. K-means clustered image
  13. Morphological image
  14. Dilated image
  15. Eroded image
  16. Optic disk detection
  17. Exudates image
  18. Hard and delicate exudates
  19. Input DFI
  20. Enhancement ways of DFI
  21. Step-1 of exudate detection
  22. Step-2 giving input image
  23. Step-3 enhancing suggestions image
  24. Step-4 exudates image of unusual eye
  25. Normal eye end result showing no exudates

LIST OF ABBREVIATIONS

AHE Adaptive Histogram Equalization

CIE Fee Internationale de l'Eclairage

CLAHE Contrast Small Adaptive Histogram Equalization

CMYK Cyan, Magenta, Yellow, Key

DRD iabetic Retinopathy

DFI Digital Fundus Image

HE Histogram Equalization

MD Mahalanobis Distance

MM Mathematical Morphology

RGB Red, Green, Blue

RRGS Recursive Region Growing Segmentation

Chapter 1

Introduction

Research Track record:

Diabetic retinopathy is a common disease nowadays that can prevail in anyone having type 1 or type-2 diabetes. The chance of being inspired by this disease relies on the time duration of a person having diabetes. Long-term diabetes leads to greater blood glucose level that causes damage by changing the blood circulation in retinal blood vessels. It really is similar that in the last level DR shows no symptoms and therefore without facing medical research it isn't feasible to predict the presence of the condition. Exudative retinopathy is a disorder referenced by the occurrence of yellowish or white mass that is available due to leakage of protein and extra fat along with normal water from vessels of blood vessels in the retina. It's important to anticipate the exudates event in fundus oculi because the collection of these exudates can lead to complete lack of eye-sight (Manpreetkaur, 2015). Walter et al. (2001) has talked about that the disease of DR progressed exudates in eyesight fundus. The physicians regard exudates among the primary indications of DR seriousness. Exudates are yellowish location resided in fundus. This disease of diabetes causes leakage of substance from vessels of blood. For a long period, uncontrolled diabetes may develop as exudates in eyeball fundus. The exudates initiate to build up in little quantity and size. When the diabetes is not watched or controlled for years the number and size of exudates will expand. The exudates expansion in vision fundus could cause blindness. Tasman and Jaeger (2001) have stated that exudates appear as bright deposits of yellow-white on the retina scheduled to lipid leakage from unusual vessels. Their size and condition differ with various periods of retinopathy. These lesions are related to many diseases of retinal vascular involving DME (diabetic macular edema), DR (diabetic retinopathy), retinal venous obstruction, hypertensive retinopathy, rays retinopathy and retinal arterial microaneurysms, capillary hemangioma of retina and disease of the overcoat. Welfera et al. (2010) have stated that exudation is a hazardous case because it can result in a loss of perspective when existing in the central macular area. Thus such lesions must be forecasted, and appropriate medical involvement must be acquired to avoid damages to visual acuity of the patient. Automatic exudates diagnosis in DR patients' retinas could enhance early on prediction of DR and may support doctors observe the treatment improvement as time passes.

Thus it can be inferred that exudates detection by computer could give a precise and quick medical diagnosis to specialist assessment and support the clinician to obtain timely decision to use medicine.

Problem Statement:

Diabetes is a speedily developing common disease among people globally which causes various organs dysfunction. Diabetic retinopathy is the principal blindness cause in individuals. Sometimes, scheduled to long-term diabetes, the retinal blood vessels are harmed, this eyesight disease is known as diabetic retinopathy. It is essential to automatically anticipate the lesions of diabetic retinopathy at an early on stage to prevent further lack of eye-sight. Exudates are significant diabetic retinopathy symptoms. Exudates are bright lesions that are an important indication of this disease. It is the major indicators of DR a major vision damage cause in diabetics.

Primary concern of the research

  1. Aim:

The primary goal of the analysis is to investigate an automated method for exudates in eyes.

  1. Objectives:
  • To examine the causes of exudates in diabetic retinopathy patients.
  • To assess the types of exudates found in digital finance images.
  • To evaluate different development methods used to predict the exudates in fundus images.
  • To determine the drawbacks of enhancement methods of exudates in digital fundus images.
  • To propose a encouraging algorithm to discover the exudates in digital fundus images.

Limitations of the analysis:

  1. This study is limited to diabetic retinopathy patients.
  2. This study is fixed to exudates recognition only.
  3. This research evaluates an robotic method for exudates in sight.

The structure of the thesis

This argument comprises of the next five chapters:

  1. Chapter 1: This is the launch section that gives the necessary research record andconcepts related to the study.
  2. Chapter 2: This chapter is the overview of books that analyzes several existing worksrelated to finding an automated way for exudates in sight.
  3. Chapter 3: This chapter describes the design of the machine that points out in detailabout the enhancement methods applied in digital fundus image for recognition of diabetic retinopathy.
  4. Chapter 4: This chapter discusses the execution plan of digital fundus images and compares different studies done by authors and depicts the results of the proposed system.
  5. Chapter 5: This is the conclusion section that provides the outcome of the study byanswering the research questions and advice for future improvement.

In addition compared to that, this thesis has bibliography made up of the sources used in collecting secondary data in the analysis and an appendix that has tools like questionnaires are utilized in the gathering main data for the study.

Chapter-2

Literature Review

Introduction:

This chapter has an overview on the diagnosis of exudates in digital fundus image for diabetic retinopathy. This chapter discusses at length about the digital fundus image. In addition to these, this chapter discusses at length about the classification of exudates in retinal images. Apart from these, this analysis provides the comparability of Histogram equalization (HE), compare limited adaptive histogram equalization (CLAHE) and Mahalanobis distance (MD) solutions to improve the digital fundus image for detection.

Literature on Digital fundus images

The benefits of digital imaging are rate of access to information (images), quick and accurate duplication, chronicling and transmission, and prompt usage of the outcomes. The imaging technique can be rehashed if the type of the fundamental result is deficient. Even though film-based images can be digitized (to register macular color thickness conveyance from two different wavelength-based pictures or even to evaluate the position of the optic nerve), quick access to the images is unrealistic, as it's important to develop the film first. This deferral keeps the picture from looking at the outcomes and this way redressing any issue in the procurement procedure, which can be efficiently completed in digital imaging at no extra cost. The digitization of fundus photographs was tended to by (Cideciyan et al. , 1991) who proposed a nonlinear rebuilding model fusing four parts: the eye, the fundus camera, the film and the scanner. Scholl et al. (2004) discovered digitized images to be valuable for assessing age-connected maculopathy and age-connected macular degeneration.

Comparison

Table 1: Evaluation of Histogram Equalization (HE), Compare Limited Adaptive Histogram Equalization (CLAHE) and Mahalanobis Distance (MD)

Histogram equalization

Contrast limited adaptive histogram equalization

Mahalanobis distance

This technique is based on the specification of the histogram.

CLAHE is considered as the necessary preprocessing step, and it gets the tendency to generate the images for extracting the features of a pixel in the classification process.

This method has carried out by figuring out the pixels of the background images only by leaving the foreground images.

HE is relatively clear-cut technique and an invertible operator. Indiscrimination is one of the primary disadvantages of this method.

CLAHE is also denoted as the automated and efficient method to detect the exudates effectively.

The selective development of MD has generated the fewer artifacts for even more control than HE and CLAHE.

HE has used the neighborhood-based procedure on the pixels, and it has the tendency to operate based on the adjustment of histogram to obtain the new images proficiently.

The technique of CLAHE has the capability to provide the green route image development with high quality.

This method can produce the similar curve to the Gaussian-shaped curve ideally.

HE has uniformly sent out the output histogram utilizing the cumulated histogram like the mapping function.

CLAHE has limited the procedure of amplification by clipping the histogram at the predefined value.

MD algorithm has given better histogram result in comparison with HE and CLAHE

Research gap:

This analysis examines about the detection of exudates in digital fundus image for diabetic retinopathy. The research gap predicted in this research is that we now have many studies on the diagnosis of exudates in digital fundus image for diabetic retinopathy. But no studies have evidently motivated the successful techniques towards the detection of diabetic retinopathy in fundus images. Recognition and classification of diabetic retinopathy pathologies in fundus images have been investigated by Agurto (2012). He examined the effects of image compression and degradation on an automated diabetic retinopathy verification algorithm. In addition to these, the Agurto et al. (2012) investigated the detection of hard exudates and red lesions in the macula using the multi-scale procedure. Walter et al. (2002) completed an investigation to add the image control to the prognosis of diabetic retinopathy. Authors also centered on automatic recognition of diabetic retinopathy from eye fundus images (Manpreetkaur, 2015). There are also studies that are focused on coarse-to-fine technique for automatically determining exudates in color eyeball fundus images.

Chapter-3

Research Design

Introduction:

This part examines the design of the study to find out an automated method for finding exudates in eye. This analysis compares three methods namely CLAHE (Compare Limited Adaptive Histogram Equalization), Histogram Equalization (HE) and Mahalanobis Distance (MD) for boosting an electronic fundus image to discover and pick the best someone to classify exudates in Retinal images by implementing graphical interface in MATLAB.

Research design:

The reason of the analysis is to identify exudates in digital fundus image for diabetic retinopathy. In this specific study, we provide an efficient method for identifying and classifying the exudates as tender exudates and hard exudates. The retinal image observed in the CIELab space of the colour is pre-processed for removing noises. Further, a network of blood vessels is removed for facilitating diagnosis and removing the optic disk. At the same time, optic disc is removed using the approach of Hough transform. Prospect exudates are recognized using the technique of k-means clustering. Finally, exudates are classified as the tender and hard one by their threshold and border energy. Developed method has yielded better results.

Histogram Equalization:

Histogram equalization is a technique for adjusting image intensities to enhance contrast. HE is an operation that is dependant on histogram specs or modification to acquire new pictures. The aim of this contrast development technique is to get a new improved image that has a even histogram that only plots the consistency at each gray-level from 0 (dark-colored) to 255 (white). Each histogram represents the occurrence of occurrence of all gray-level in the image.

Figure 1: Image before enhancement

Figure 2: Histogram before equalization

Figure 3: Image after histogram equalization

Figure 4: Histogram after histogram equalization

Contrast Small Adaptive Histogram Equalization:

CLAHE is considered as a locally adaptive way for contrast enhancement. CLAHE is an enhanced version of adaptive HE (AHE) method. The strategy AHE has a realistic restriction that homogenous part in the image contributes to over-amplification of sound due to slim group of pixels are plotted to a complete range of visualization. In the meantime, it was noticed that distinction limited AHE (CLAHE) was designed for preventing this noise over-amplification in homogenous areas. CLAHE restricts the reasonable amplification in the image so that image appears like very real.

Figure 5: Image after CLAHE

Figure 6: Histogram after CLAHE

Mahalanobis Distance:

Image enhancement using the Mahalanobis distance method is performed by identifying the background image pixels and getting rid of them, giving only the foreground image. It is based on the assumption that in image community N, the background pixels has significantly different strength value than those of the foreground pixels. For every pixel (x, y) in the picture, the mean n (x, y) and the typical deviation Жn (x, y) of the statistical circulation of intensities in N are estimated. The sample means; n is used as the estimator for n (x, y) and the e sample standard deviation; Ж n is the estimator for Жn (x, y). In case the depth of pixel (x, y) is near the mean power in N, it is considered to belong to the background collection. As described mathematically in Eq. 1, the expression implies that pixel (x, y) belongs to if the stated condition is satisfied.

Those images would later be mixed to judge the MD image, which is often segmented using the threshold t to identify the background pixels.

Figure 7: Image after MD enhancement

Figure 8: Histogram after MD enhancement

Summary:

This research compares three methods particularly CLAHE, HE, MD to improve an electronic fundus image to detect and choose the best someone to classify exudates in Retinal images by implementing graphical user interface in MATLAB. It had been visible from the above results that prospect exudates are recognized using the approach of Mahalanobis Distance advancement.

Chapter 4

Implementation Plan, Discussion, and Results

Introduction:

This chapter reveals the implementation plan of diagnosis of exudates in digital fundus images by proposed approach. The results of proposed method are also shown.

Implementation Plan:

The suggested system is put in place using the digital fundus images. DFIs (digital fundus images) are crucial to find the pathological simple fact that would lead to different diseases. However, digital fundus images have many lighting and contrast issues which make enhancement a key point. Subsequently, digital fundus images must be developed to permit for good visualization to gratify ophthalmologists to attempt their diagnosis. The below figure shows the implementation plan of diagnosis of exudates in digital fundus images:

Figure 9: Flow chart of the method

4. 3 Transformation from RGB color space to CIELab color space

A Lab color space is a color-opponent space with dimension L* for lightness and a* and b* for the color-opponent sizes, based on nonlinearly compressed CIE XYZ color space coordinates.

The CIELab color level is an approximately consistent color range. In a standard color level, the differences between your points plotted in the colour space match the aesthetic difference between your colors plotted. The CIELab color space is organized in a cube form. The L* runs from top to bottom. The utmost for L* is 100, which presents a perfect reflecting diffuser. The least for L* is zero, which signifies dark. The a* and b* axes have no specific numerical limits. Positive a* is red, Negative a* is renewable. Positive b* is yellowish, Negative b* is blue.

Figure 10: CIELab color space

It is perceptual standard color space. Perceptual uniformity means how two colors change from seeing when individuals observe that two colors. Hence standard color areas were defined in such way that all the colors are set up by the perceptual difference of the colors.

The L part closely matches individuals understanding of lightness, and by having it as an unbiased quantity to regulate, it could be used to make exact color corrections without affecting the a* and b* color twins. RGB or CMYK color spots are made to model the output of physical devices somewhat than human visual perception. This color model is used in this work to recognize even a little intensity variation.

K-means Clustering

K-means clustering is a way of vector quantization, formerly from signal handling, that is obtainable for cluster examination in data mining. K-means clustering aims to partition n observations into k clusters where each observation belongs to the cluster with the nearest mean, offering as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. K-Means algorithm is chosen because of its simplicity. In such a work, k-means clustering is utilized to partition the data into groupings for discovering exudates locations.

K-means Usage inside our project:

Because of the computational ease of the k-means algorithm over other clustering algorithms, we made a decision to use the k-mean clustering in the proposed work. The k-mean clustering algorithm is a specific circumstance of the generalized hard clustering algorithms. It really is applied when point representatives are widely-used, and the squared Euclidean Distance is followed to measure the dissimilarities between vectors and cluster staff. The k-means algorithm is given below.

The steps involved in K-Means algorithm are:

  1. Select an initial partition with k clusters
  1. Generate a new partition by assigning each structure to its closest cluster centre.
  2. Compute new cluster centers.
  3. Continue to do steps 2 and 3 until centroids do not change.

Figure 11: Input image

Figure 12: k-means clustered image

Blood vessel detection

To facilitate exudates removal from the pre-processed image, blood vessel network is recognized and then taken out from the picture using Morphological operations. Morphological procedures can readily be used in medical image evaluation as it facilitates powerful tools to extract pathologies. The morphological operations employed in the proposed work are given below.

An important part of making use of morphological functions is to select the form and size of structuring element. Inside the proposed work, a ball-shaped structuring aspect of size 8, was found to be optimum for eradicating the blood vessel network from the retinal images of local database

Morphological Image Processing:

Mathematical morphology (MM) is a theory and way of the research and treatment of geometrical buildings, based on set theory, lattice theory, topology, and arbitrary functions. MM is most commonly put on digital images, but it could be applied as well on graphs, surface meshes, solids, and many other spatial structures.

Topological and geometrical continuous-space concepts such as size, condition, convexity, connection, and geodesic distance, were unveiled by MM on both ongoing and discrete spaces. MM is also the foundation of morphological image processing, which consists of a set of providers that transform images based on the above characterizations.

The basic morphological operators are erosion, dilation, beginning, and closing.

Dilation:

Dilation is one of both first providers in the area of mathematical morphology, the other being erosion. The principal effect of the operator on a binary image is to little by little enlarge the restrictions of parts of foreground pixels (i. e. white pixels, typically). Thus areas of foreground pixels expand while slots within those regions become smaller. The dilation operator will take two bits of data as inputs. The first is the image which is to be dilated. The second reason is a (usually small) group of coordinate things known as a structuring element (generally known as a kernel). It really is this structuring factor that determines the complete effect of the dilation on the suggestions image.

Figure 13: Dilated image

Erosion:

Erosion is one of both first providers in the region of mathematical morphology, the other being dilation. The main effect of the operator on the binary image is to erode away the boundaries of regions of foreground pixels (i. e. white pixels, typically). Thus areas of foreground pixels shrink in proportions, and openings within those areas become much larger. The erosion operator needs two pieces of data as inputs. The foremost is the image which is usually to be eroded. The second reason is a (usually small) set of coordinate tips known as a structuring aspect (also referred to as a kernel). It really is this structuring component that determines the complete effect of the erosion of the type image.

Figure 14: Eroded image

Figure 15: Morphological image

Hough Transform

The Hough transform is an attribute extraction technique found in image analysis, computer eye-sight, and digital image handling. The goal of the technique is to find imperfect instances of objects within a particular class of figures by way of a voting procedure. This voting process is completed in a parameter space, from which object individuals are obtained as local maxima in a so-called accumulator space that is explicitly made by the algorithm for computing the Hough transform.

In this project work, round Hough transform is employed to discover optic drive in a retinal image. Elimination of optic drive is essential for recognition of exudates. In the event the optic disk is not eliminated from a picture, there is a chance of determining optic disk as exudates, which leads to the false effect.

Circular Hough Transform Algorithm works is provided below.

Step1: Convert color retinal image into grayscale

Step2: Create a 3D Hough array (accumulator) with the first two measurements representing thecoordinates of the circle origin, and the 3rd dimension represents the radii.

Step3: Perform advantage detection using the Canny advantage detector. For every border pixel, increment thecorresponding elements in the Hough array.

Step4: Collect applicant circles, and then erase similar circles.

Step5: Circle the object.

Figure 16: Optic disc detection

Classifying Hard and Soft exudates

The final step is to classify the exudates as hard and delicate based on the threshold value and edge energy. Edge electric power calculation must remove the exudates with sharp edges which can be a characteristic feature of hard exudates. We preferred canny operator over Kirsch operator for edge energy diagnosis. The hard exudates are extracted by merging this edge energy and a

Threshold value. To get the very soft exudates subtract the hard exudates image from the picture which has both types of exudates. Hard exudates and very soft exudates are categorized by using research amount value of white pixels in exudates image.

Figure 17: Exudates image

Figure 18: Hard and soft exudates

Chapter 5

Results and Conclusion

Introduction:

This section reveals the results and finish of the study by responding to research questions and recommendations for future studies.

Results:

From the proposed system the results bought are that the exudates are forecasted, then it is classified as hard, and smooth exudates and the severity level is projected. The first shape shows the input as a genuine image:

Figure 19: Suggestions image

Source: Author

In another figure the enhancement methods are put on digital fundus images for detection of diabetic retinopathy:

Figure20: Enhancement ways of DFI

Next, the exudates are found which is depicted in the below group of figures:

Figure 21: Step-1 of exudate detection

Figure 22: Step-2 providing input image

Figure 23: Step-3 boosting the suggestions image

Figure 24: exudate image of excessive eye

Repeating the same procedure for normal eye and is also shown in the next figure:

Figure 25: Normal attention output displaying no exudates

The results of the study reveal that Mahalanobis Distance is the greatest algorithm for the arteries image enhancement program. Throughout the test, we've found typically 88% sensitivity and 60% accuracy and reliability.

Conclusion

Automated diabetic retinopathy detection has become an important research because of the severity of upsurge in cause of blindness on the list of diabetes patients. DR is brought on mainly by the modifications in retina's arteries due to increased degree of blood glucose. Exudates are one of the major signals of DR. Exudates are proteins and lipids that leaks and deposits from destroyed vessels of blood within the retina. The exudates development in the fundus of the attention may cause blindness. Thus it can be concluded that monitoring and detecting exudates in the fundus of the attention are essential for diabetic patients.

In prior methods, the advancement of the fundus image was not accurate. Therefore the diagnosis of exudates has turned into a severe concern. For the diagnosis of exudates in the fundus, previously MM and Threshold based mostly techniques are used, but the results are not accurate. It could result in exhibiting blood vessels, optic disc etc. as exudates. This may lead to wrong diagnosis.

So, in the proposed method accurate and easy simplify able techniques are utilized. Such as for example MD technique for better enlargement of DFI and additional program of k-means clustering technique after MM and Threshold based mostly techniques. This can discover and classify the exudates.

The experimental results show that in the 3 augmentation methods, the Mahalanobis distance method boosts the insight image even as can easily see that in the histogram produced, all the intensities of the pixels in an image are clipped to certain in which all the pixels have average intensity ideals. Later after applying the proposed method the precision of locating the exudates is high that can be seen in the result by classifying into gentle and hard exudates.

Thus it can be concluded that various kinds research have suggested numerous algorithms and the appealing algorithm is chosen to find exudates in image precisely.

Future opportunity

Further this research can be utilized in the diagnosis of hemorrhage, and microaneurysms that could be put into exudates detection so that it enhances the capability to test the DR. The future research must fix the situation of growing the sensitivity by improving the outcomes. Further classification to designate the scope of diabetic retinopathy can be carried out. A complete system can be advanced you can use to predict entire possible anatomical organs and abnormalities. In the future research, it can even be examined to incorporate further area knowledge to recognize abnormal lesions such as neovascularization and hemorrhage.

References:

  1. Manpreetkaur, M K (2015), Auto Detection of Diabetic Retinopathy from Eyeball Fundus Images: A Review, Proceedings of 3rd International Conference on Developments in Executive & Technology (ICAET-2015).
  2. Schneider, C. , Rasband, W. , & Eliceiri, K. (2012). NIH Image to ImageJ: 25 years of image research. Characteristics Methods, 9(7), 670-675.
  3. Walter, T. , Klein, J. , Massin, P. , & Erginay, A. (2002). A Contribution of Image Processing to the Identification of Diabetic Retinopathy-Detection of Exudates in Color Fundus Images of the Man Retina. IEEE Trans. On Medical Imaging, 21(10), 1236-1243.
  4. Tasman, W. , & Jaeger, E. (2001). The Wills Eye Medical center: Atlas of Clinical Ophthalmology(2nd ed. ). Lippincott Williams and Wilkins Publisher.
  5. Welfera, D. , Scharcanskia, J. , & Marinho, D. (2010). A coarse-to-fine technique for automatically detecting exudates in color vision fundus images. Computerized Medical Imaging and Images, 34, 228-235.
  6. Osareh A, Shadgar B and Markham R (2009), "A Computational-Intelligence- Based Approach for Diagnosis of Exudates in Diabetic Retinopathy Images, " IEEE Trans on Information Technical in Biomedicine, Vol. 13, No. 4, pp. 535-545.
  7. Shahin E M, Taha T E, Al-Nuaimy W, El Rabaie S, Zahran O F and Abd El-Samie F E (2012), "Automated Detection of Diabetic Retinopathy in Blurred digital fundus images" Proceedings of International Seminar on Computer Executive (ICENCO 2012), Cairo, Egypt.
  1. Lalonde M, Gagnon L, and Boucher M C (2003), "Non-recursive paired traffic monitoring for vessel removal from retinal images, " Vis. Software, pp. 61-68.
  2. Sanchez C I, Hornero R, Lopez M I, Aboy M, Poza J, Abasolo D, Sanchez C I, Hornero R, Lopez M I, Aboy M, Poza J and Abasolo D (2008), "A book automatic imageprocessing algorithm for detection of hard exudates based on retinal image evaluation. ", Med. Eng. Phys. , vol. 30, no. 3, pp. 350-357.
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