Technology, Tiruchengode, Tamil Nadu, India.
ABSTRACT
Glaucoma is an eye disease that damages the optic nerve. If corrective therapy is not taken, it causes the permanent blindness so this disease cannot be ignored. If the disease is identified at the early stage, the treatment will be helpful. Many research works describe the different techniques that widely incorporated in the detection of glaucoma. This project presents the improved version of the classification system for glaucoma diagnosis in ophthalmology. Ophthalmologists widely uses the fundus images to detect glaucoma, which is according to the studies of the World Health Organization(WHO), the second cause of blindness over the world.
In this project the SSDCGAN algorithm have been used for the automatic glaucoma assessment using fundus images. The another algorithm called SVM algorithm have been used for the comparison. There are two methods for automatic segmentation. The first method is the Stochastic Watershed transformation to segment the optic cup and also measures the clinical features such as the CDR (Cup/Disc Ratio). The second method used for the segmentation of optic cup and optic disc is the U-Net architecture. Next, automated glaucoma assessment using convolutional neural networks (CNNs). In this process, many Image Net-trained models are finely tuned and used as automatic glaucoma classifiers. This project addresses the problem of the retinal image synthesis. In this project, using the SS-DCGAN algorithm the glaucoma disease is detected automatically. SVM algorithm has also been implemented for the comparison of the both algorithm for the accuracy purpose.
Keywords: Fundus Images, Glaucoma, Optic Nerve, Optic Cup, Optic Disc, CDR
I. INTRODUCTION
Our association to nature is that the human senses. The human neural structure consolidates the somatic cell explosions of seeing, hearing, smelling, degusting and touching into a considerable whole. Our eyes square measure the foremost necessary sensory organs by on shot. From our sight, we have a tendency to see up to 80 percent of all experiences. There is a growing interest in developing electronic systems that scan giant numbers of individuals for eye diseases like glaucoma and diabetic retinopathy, and in providing machine-controlled sickness detection. Image processing is currently changing into useful and a valuable screening tool. Glaucoma damages optic nerve within the eye and thus results in the visual defect and loss of vision. Glaucoma looks to be developed and doesn’t occur in life till later. The elevated pressure, referred to as intraocular eye pressure, can have an effect on the optic nerve that carries the pictures to the brain. If this harm continues for an extended term, eye disease will cause irreversible loss of sight. Maybe the first symptom of this sickness is that the loss of vision, which might go utterly unheeded toll late within the sickness. Thus glaucoma is additionally referred to as the sneak vision thief. In severe cases if intraocular eye pressure will increase the humor evacuation pathway becomes utterly blocked. For such cases, there is extreme eye pressure, terrible headache, fuzzy vision.
Early glaucoma treatment can decrease the visual impairment risk by concerning 50 percent. Major explanation for vision loss is eye disease that is known by neurode generation of the optic nerve. It’s troublesome to revitalize the degenerated nerve fibres of the optic nerve then early identification andprompt treatment squaremeasure necessary to avoid visual harm. In body structure pictures, current works associated with eye disease noticeion focus solely on CDR estimation to detect glaucomatous events. CDR has, however, been found to be inconsistent in deciding what quantity OD harm eye disease causes as an example, some patients have little CDRs with substantial loss of the field of vision, whereas others have giant CDRs however very little loss of field of vision. So, we've got calculated CDR yet as RDR thereby classifying the pictures into four completely different stages.
II. METHODOLOGY
Image Enhancement:
The problem with the retinal image is that the standard of the noninheritable pictures is typically not smart. So, it's necessary to enhance the standard of the retinal pictures. The aim of this can be to take away noise from the retinal image further more on enhance image distinction and image quality. This stage consists of color area conversion, The color image is born-again into grey scale model for the rationale that operating with grey scale image is more well-off and straight forward than the color.
Detection of Optic Disc and Optic cup :
The blind spot is that the purpose within the eye wherever the nervus opticus fibers leave the tissue layer. The OD encompasses a vertical oval (elliptical) form and is split into 2 separate zones: the central zone or the cup and therefore the peripheral zone or neuro retinal rim. CNN to seek out the foremost probable pixels within the blind spot region. The explanations it have a tendency to used this technique square measure performance and since it wish our pipeline to be fully CNN-based. CNN may be a revolutionary network structure that has shown its power in fields of pc vision like classification, object detection and segmentation. It has a tendency to will create use of CNN within the study of structure pictures. Firstly, it have a tendency to use a basic CNN on that specialised layers square measure trained to seek out the pixels most likely in OD region. Then it have a tendency to mapped out candidate pixels furtherly via threshold. By calculative the middle of gravity of those pixels, the placement of OD is finally determined. The calyculus, a brighter portion in blind spot is extracted from inexperienced part of associate RGB image. thanks to the overlapping of the borders of disc and cup determination of calyculusis general. Hence, the 2vital operations, gap and shutting square measure applied.
Gap is associate erosion that is performed when dilation. Closing may be a dilation that is performed when erosion.
Feature Extraction:
When segmenting exudates regions in retinal pictures, the relevant options of a picture ought to be extracted and it are often done exploitation grey Level Co-occurrence Matrix (GLCM). A co-occurrence matrix, additionally referred as a incidence distribution, is outlined over a picture to be the distribution of co-occurring
When feature extraction of retinal pictures, the obtained image is applied to SSDCGAN rule. Semi-supervised learning has been of nice interest each in theory and in observe as a result of it needs less human effort and provides higher accuracy. Given the scarce range of glaucoma-labelled pictures, this method will considerably facilitate the event of automatic eye disease assessment systems exploitation retinal pictures. For that reason, it have a tendency to determined to use the facility of the DCGAN to develop a semi-supervised learning technique for coaching a eye disease classifier and at an equivalent time a picture synthesizer. In this approach, to train a eye disease classifier creating use of a little set of glaucoma-labelled pictures, a group of unlabeled pictures and therefore the artificial pictures generated by the DCGAN to coach a eye disease classifier.
III. COMPARISON
SVM CLASSIFIER
Typically, a classification method needs dividing the information into testing and training sets. The classifier named Support Vector Machine is employed to differentiate the conventional eye from the glaucomatous eye.
SVM's aim is to come up with a training data model whereby the target values of the data are predicted only on the basis of attributes of test data.
FLOW CHART OF SVM CLASSIFIER
Figure: Flow chart of SVM classifier
IV. RESULTS AND DISCUSSION
SS-DCGAN ALGORITHM
Here dataset of 200 images were taken. Out of 200 retinal images,50 images are normal images and remaining 150 images are abnormal images. These images will have different resolution size. The dataset referred from the following link https://figshare.com/s/6e4cbba780b81a59964c
Figure.1: Input Image
The figure.1 shows RGB input image of the project with resolution of g(256x255).
Figure.2: Cup Image
The figure.2 shows the optic cup of the retinal image in figure.1. This image is obtained by dilating the image.
Dilation process makes ophthalmologists to see the entire retina.
Figure.3: Disc Image
The figure.3 shows the optic disc of the given retinal image in figure.1. This image is obtained by detecting the boundary of the retina and rim.
Figure-4: Input image, Disc segment image, Disc boundary image, Cup image and Cup boundary image.
The figure.4 shows the input image, disc segment image, disc boundary image, cup image, cup boundary images.
Initially, the retinal RGB image is separated into red, green and blue channels. The OD region is clearly visible in red channel image and hence it is further used for OD and OC region extraction. The morphologically closing function with disc shaped structuring element 10 (pixels) is now applied on the red channel image and morphologically opening function with disc shaped structuring element 20 (pixels) is applied on the same red channel image. The red channel is separated from RGB retinal image, which also shows the OC clearly. The independent component analysis method has been employed on this redchannel image to locate the OC region pixels with some redundant pixels. Next, the morphological opening function is applied on this image in order to remove the redundant pixels and locate the OC pixels. Finally, the binary image is constructed from this morphologically processed image. The pixels with white in color are marked as OC pixels in the RGB retinal image. The OC segmented image is now subtracted from OD segmented image to extract the rim area.
Figure.5: Real image and SSDCGAN image.
The figure.5 shows the real image taken from the dataset as labelled images and SSDCGAN image. By using the generator we obtaining the SSDCGAN image.
Figure.6: Feature extraction image.
The figure.6 shows the visualization of the HOG features and is obtained by resizing the grayscale image and by finding CDR(Cup to Disc Ratio) and RDR(Rim to Disc Ratio). This is done by extracting gradients and orientation of the edges.
Figure .7CDR and RDR value of the input retinal image.
The figure .7 shows the CDR and RDR value of a input retinal image. Here the diameter of the white cup like segment (optic cup) in the optic disc is compared with its total diameter of the optic disc. And RDR is calculated by comparing the rim region with optic disc region.
Figure.8: Classification using SS-DCGAN
The figure.8 shows the level of glaucoma which is classified by SS-DCGAN.
In the same way, SS-DCGAN can classify the input retinal image as normal, mild, moderate and severe stages of glaucoma. The following figures shows the corresponding output images.
Figure.9: Input retinal image
The figure.9 shows the input retinal image of the project with resolution size of g(128x128).
Figure.10: Input image, Disk segment image, Disk boundary image, Cup and Cup boundary image.
Figure.11: CDR and RDR value of the input retinal image.
Figure.12: Normal image classified by SS-DCGAN.
The figure.12 shows the normal level of glaucoma classified by SS-DCGAN.
Figure.13: Input retinal image.
The figure.13 shows the input retinal image with the resolution size of g(128x128).
Figure.14: Input image, Disk segment image, Disk boundary image, Cup and Cup boundary image.
Figure.15: CDR and RDR value of the input retinal image.
Figure.16: Moderate glaucoma image classified by SS-DCGAN.
Table.1: Feature extraction and its values.
Img no
Diameter CUP DISC RATIO
Contrast Correlation Energy Homogeneity Smoothness Kurtosis Skewness Classification
1 89.7 0.56 0.41 0.44 0.23 0.83 0.96 3.01 0.09 Moderate
2 97.3 0.69 0.51 0.45 0.21 0.81 0.97 3.01 0.08 Moderate
3 79.6 0.47 0.47 0.48 0.21 0.82 0.96 3.01 0.08 Moderate
4 68.2 0.58 0.45 0.47 0.21 0.82 0.92 3.02 0.08 Moderate
5 47.07 0.41 0.41 0.46 0.23 0.83 0.96 3.03 0.09 Moderate
6 32.71 0.27 0.32 0.35 0.33 0.87 0.97 2.95 0.07 Normal
7 19.81 0.04 0.30 0.34 0.35 0.85 0.96 2.94 0.06 Normal
8 77.96 0.63 0.33 0.35 0.32 0.84 0.96 2.96 0.09 Moderate
9 76.85 0.48 0.42 0.45 0.23 0.82 0.96 2.97 0.08 Moderate
10 81.88 0.65 0.43 0.44 0.23 0.85 0.96 3.05 0.08 Moderate
11 31.43 0.38 0.40 0.45 0.24 0.83 0.93 3.01 0.08 Moderate
12 50.68 0.30 0.31 0.33 0.34 0.84 0.96 2.95 0.06 Moderate
13 132.80 0.30 0.36 0.42 0.27 0.83 0.94 2.99 0.09 Moderate
14 76.22 0.53 0.39 0.46 0.24 0.83 0.97 3.05 0.07 Moderate
15 133.22 0.33 0.40 0.44 0.24 0.82 0.77 2.98 0.08 Moderate
16 82.18 0.69 0.39 0.44 0.25 0.83 0.83 3.01 0.09 Moderate
The table 4.1 shows the features extracted from the input retinal images like CDR, Cupdiameter, Contrast, Correlation, Energy, Homogenity, Smoothness, Skewness, Kurtosis by which the trainer SS-DCGAN classified the input image as normal, moderate or severe level of glaucoma.
Here for normal retinal image CDR value is between 0.1 - 0.3 and for moderate glaucoma levelCDR value is 0.3 - 0.5 and for severe glaucoma level CDR value is greater than 0.7.
23 41.36 0.37 0.37 0.42 0.27 0.83 0.97 3.08 0.08 Moderate
24 67.91 0.62 0.41 0.44 0.24 0.82 0.95 2.99 0.09 Moderate
25 88.12 0.52 0.46 0.47 0.21 0.82 0.95 3.02 0.07 Moderate
26 91.04 0.37 0.42 0.46 0.22 0.82 0.96 2.99 0.07 Moderate
27 75.41 0.46 0.50 0.48 0.21 0.81 0.95 2.99 0.08 Moderate
28 29.17 0.30 0.39 0.43 0.25 0.83 0.96 3.00 0.09 Moderate
29 84.65 0.72 0.49 0.46 0.20 0.81 0.93 3.01 0.08 Moderate
30 154.51 0.35 0.53 0.48 0.21 0.82 0.94 3.01 0.08 Severe
31 37.71 0.24 0.36 0.41 0.27 0.83 0.96 3.01 0.09 Normal
32 60.20 0.35 0.46 0.43 0.22 0.82 0.94 2.96 0.09 Moderate
33 79.18 0.50 0.51 0.47 0.21 0.81 0.90 3.00 0.09 Moderate
34 47.54 0.51 0.43 0.45 0.23 0.82 0.91 2.98 0.08 Moderate
35 103.8 1.33 0.51 0.46 0.23 0.83 0.96 2.96 0.07 Severe
36 82.28 0.55 0.42 0.47 0.21 0.81 0.95 0.95 0.07 Moderate
37 45.05 0.41 0.43 0.44 0.23 0.82 0.96 3.05 0.09 Moderate
38 95.46 0.46 0.41 0.46 0.23 0.8 0.94 3.06 0.08 Moderate
39 46.41 0.44 0.36 0.39 0.28 0.83 0.95 2.97 0.07 Moderate
40 55.11 0.60 0.46 0.47 0.21 0.82 0.98 3.05 0.08 Moderate
41 63.45 0.31 0.34 0.42 0.29 0.84 0.94 2.99 0.09 Moderate
42 71.39 0.48 0.36 0.43 0.27 0.84 0.96 2.94 0.08 Moderate
43 97.3 0.60 0.38 0.47 0.20 0.81 0.96 3.02 0.06 Moderate
44 78.46 0.26 0.36 0.39 0.29 0.83 0.93 2.98 0.08 Normal
45 105.76 1.13 0.53 0.45 0.25 0.83 0.95 3.01 0.08 Severe
46 86.16 0.36 0.41 0.45 0.23 0.83 0.82 2.94 0.08 Moderate
47 61.61 0.55 0.43 0.47 0.22 0.82 0.90 3.04 0.09 Moderate
48 75.19 0.52 0.39 0.42 0.25 0.83 0.95 3.03 0.08 Moderate
49 63.26 0.59 0.51 0.46 0.21 0.82 0.95 3.09 0.09 Moderate
50 85.87 0.48 0.39 0.45 0.25 0.83 0.95 2.98 0.06 Moderate
OUTPUT OF SVM ALGORITHM
Figure.17.Segmentation of Cup and Disc image
The figure.17 shows the segmentation of input retinal image. OD region is obtained by seperating the green channel from the RGB image and the OD and OC regions are extracted.
Figure.18: Iteration of OD region
The figure.18 shows the iterations of OD region. This image is obtained by segmenting an image on a pixel-by- pixel basis.
Figure.19: Segmented OD
The Figure.19 is segmented OD.This image is obtained by choosing a threshold and comparing it with each pixel. Based on this comparison the pixels are classified as fore-ground or background image.
Figure. 20: OD segmentation with 60 Iterations The figure.20 shows the OD segmentation of the input retinal image with 60 iterations.
Figure. 21: 60 Iterations
The figure.21 shows 60 Iterations. This image is obtained by the decreasing the threshold value of the input retinal image.
Figure.22: CDR value of the input retinal image The figure.22 shows the CDR value of the input retinal image.
Figure.23: Classification using SVM algorithm The figure.23 shows the glaucomatous image classified by SVM algorithm.
Similarly, the non- glaucomatous image(Normal image) was also classified by SVM algorithm. The following images are the results of non-glaumatous image.
Figure.24: Segmentation of Cup and Disc image
Figure.25:OD segmentation with 60 Iterations
Figure.26: CDR value of the input retinal image
Figure.27: Classification of non-glaucomatous image using SVM algorithm
V. CONCLUSION
In this project, we have given many contributions for automatic glaucoma assessment and optic disc and optic cup segmentation algorithms using retinal anatomical structure pictures . Glaucoma is detected by using RGB channel, CDR, RDR, ISNT rule. CDR and RDR are 2 parameters that are calculated using optic disc and optic cup.
When the ratio of CDR exceeds 0.3, then it's referred to as glaucoma eye. If it's below 0.3 then it's non glaucomatic . The eye disease pictures are more classified as normal, mid, severe. Glaucoma disease is based on the CDR and RDR. We have a tendency to applied morphological strel functions to the images extracted. We had compared our project with the another algorithm called SVM Classifier. Thus the proposed work to segment the eye with the help of machine learning techniques has been done using MATLAB.
Algorithm Accuracy SS-
DCGAN
90.17
SVM 94.61
