Classification of Ischemic Optic Neuropathy Using …Ischemic optic neuropathy (ION), is one of the most well-known disease which could lead to vision loss or blindness. ION impacts

Classification of Ischemic Optic Neuropathy Using Custom Image Processing Algorithm – Statistical Based Analysis

1Bassam Al-Naami, 2Nasr Gharaibeh, 3Thaier Hayajneh, 4Bassam J Mohd, and 5Abed-AlRazzaq Kheshman

*1, Corresponding AuthorBiomedical Engineering Dept, Hashemite University P.O. Box 150459, Zarqa 13115, Jordan, [email protected]

2AL-Huson University College, AL-Balqa Applied University, [email protected]

3,4 Compuetr Engineering Dept, Hashemite University P.O. Box 150459, Zarqa 13115, Jordan, {bassam,thaier}@hu.edu.jo

5Salt Hospital, Al-Balqa- Jordan [email protected]

Abstract

Ischemic optic neuropathy (ION), is one of the most well-known disease which could lead to vision loss or blindness. ION impacts the head of the optic nerve (ONH),referred to as anterior (AION), and the other parts of the optic nerve, referred to as posterior (PION).This article presents a customized image processing method based on low pass filtration and segmentation of the area around the ONH , which is the region of interest (ROI) to detect ION. The extracted RIOs features for the optic nerve were analyzed and employed by statistical analysis's such as descriptive analysis and test of hypothesis. To test the sensitivity of the proposed imaging method to discriminate images of individuals with ION from healthy ones, 76 fluorescein angiography images were analyzed. The images were classified based on participants status such as: healthy, ION over the age of 45, ION under the age of 45. The proposed method demonstrated a 95% accuracy in discriminating healthy images from those with ION, and consequently would help identify the clinical features of ION. Keywords: Ischemic optic neuropathy (ION), Fluorescent angiography, Filtration, Threshold-based histogram, Statistical descriptive analysis, T-test 1. Introduction

Ischemia is a restriction in the blood supply due to intrinsic factors in the blood vessels, resulting in

impairments in the tissues or the organs [1, 2]. Ischemic optic neuropathy (ION) is a clinical condition affecting the optic nerve and resulting in visual impairments or loss. Based on the affected part of the optic nerve, ION has primarily two types: anterior (AION) involving the optic nerve head and posterior (PION) involving ciliary arteries throughout the rest of the nerve [3]. AION is more common than PION [3, 4, and 5]. Depending on the etiology, AION can be classified as Arteritic AION (caused by inflammation of medium-sized blood vessels and more common in women and older adults over 50) and Non-arteritic AION (caused by a cardiovascular risk factors (e.g., hypertension) and more common in younger adults)[5, 6, 7]. AION typically causes acute unilateral vision loss lasting from few hours to days. PION is less common than AION and can be classified as of arteritic, non-arteritic, and post-surgical causes.

Fluorescein angiography is a diagnostic test used to detect ION by photographing blood vessels located posteriorly in the eye after injecting a blue sodium-based fluorescent dye in the bloodstream. Images from the test can further guide physicians in prescribing the proper treatment.

Regardless of the vision loss degree caused by a retinopathy or neuropathy disease, it is difficult to distinguish between the signs of retinopathy and neuropathy. Therefore, it is important to start the classic ION examination which is highly emphasizing on the use of test of visual field defects (VFD) based on the Goldmann perimeter detection method (figure 1)[8].

Classification of Ischemic Optic Neuropathy Using Custom Image Processing Algorithm – Statistical Based Analysis Bassam Al-Naami, Nasr Gharaibeh, Thaier Hayajneh, Bassam J Mohd,Abed-AlRazzaq Kheshman

International Journal of Digital Content Technology and its Applications(JDCTA) Volume 7, Number 10, June 2013 doi : 10.4156/jdcta.vol7.issue10.10

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The evaluation process of visual status is usually provided by experienced perimetrists or physicians

to get a reliable ophthalmic vision diagnosis. However, it has been reported in that a variety of ischemic optic neuropathies are misdiagnosed or ignored by physicians. Furthermore the vision field testing either manual kinetic or automated static perimetry is still critical in some cases [9-11].

Therefore, this research objective is to increase the accuracy of ION diagnostic by developing a custom image processing algorithm-statistical based. Little research has been reported in investigating the damaging effect of retinopathy on visual functioning of the optic nerve. Thus, this article proposes a novel diagnostic method using fluorescent-based images of the optic nerve head area (optic disc and other areas). The proposed method was examined using 76 patient images. The patients were divided into three groups:28 patients under 45 years (IONU45), 22 patients above 45 years old (IONA45), and 26 healthy individuals. The custom image processing interface in figure 2 was designed as a baseline to convert the images to gray level. Then a low-pass filter was applied due to sharp cutoff characteristic in the Fourier domain causing a rectangular window for the passband. The rectangular pass-band window in the ideal low-pass filter causes ringing artifact in the spatial domain. In order to reduce the ringing artifact in the Fluorescent, images of the pass-band should have a smooth fall-off characteristic. In this

Image Acquisition

Image preprocessing and Enhancement: Convert to Gray level and Low pass

filter.

Test of hypothesis for normal and Ischemic Optic

Neuropathy cases.

Features extraction by Histogram of the ROI: Maximum and average pixel values for images

with ION under and above 45 years

Figure 2. Flow diagram for the proposed approach.

Results Box Plot for Ischemic Optic Neuropathy:

under 45 years(IONU45) above 45 years(IONA45)

Normal cases

Figure1. Visual field defects in nonarteritic anterior ischemic optic neuropathy, plotted with a Goldmann perimeter (using I-2e, I-4e, and V-4e targets), show inferior altitudinal defect with I-2e and inferior nasal defect with I-4e and V-4e isopters. The visual acuity in the eye was 20/20 [8].


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work, an overall focus on the threshold histogram for the region of optic disc and regions along the major optic nerve is verified. Therefore, this step followed by the histogram of region of interest (ROI) for the segmented areas. Extracted features were taken from the ROI, such as the maximum and the average pixel values were employed for the statistical analysis, namely, descriptive analysis and hypothesis testing.

1.1 Related work

Various research work were reported in the literature to identify the diseases related to Ischemic Optic Neuropathy (ION) and classify them into anterior (AION) and posterior (PION), where the optic nerve head (ONH) and the rest of the optic nerve were involved respectively. The proposed techniques are further classified into supervised and unsupervised, manual or semi automated segmentation for the blood vessels. Sometimes, statistical approaches are employed. The raw data (images) used in surveyed are Fluorescent Angiography images. A blood vessel detection method is presented by Marín et al. [[12 ]]. The target of this method is to determine the blood vessels from non vessels, where the pixels of extracted features are employed to form a 7-D vector of gray-level. Moment invariants-based features from preprocessed retinal images are fed later on to a neural network. Another automated vessel segmentation method using gray-level and moment invariants-based features was proposed by Lupascu [13]. Köse et al. [14] have developed a method based on the inverse segmentation to detect age related macular degeneration. Tavakoli et al. in [15] presented a new method for ONH detection by using Radon transform and multi-overlapping windows in fluorescein angiography fundus images. Multi- stage strategy for segmentation, called r-bends for glaucoma assessment, proposed by Joshi et al. [16]. They developed an automatic optical disk (OD) parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. Tobin et al. [17] presented another method based on the location of the optic nerve and macula using digital red- free fundus photography for detection of Diabetic retinopathy (RD) purposes. Gaussian mixture models in [18] has been used for optic cup segmentation to detect the Glaucoma. The technique is based on computer-aided diagnosis system designed by examining optic cup-to-disc ratio assessment. Lowell et al. [19] demonstrated a new method for detection of ONH boundaries in low resolution images using specialized template matching, and segmentation by a deformable contour model. The advantage of this approach is the ability to deal with fuzzy and blurred images with 83% of performance, which is less than accuracy obtained in our approach in this current work. Echegaray et al.[20] presented a method to detect the optic disc in fundus images to detect glaucoma based on use of automatic initialization of a level-set segmentation algorithm. Glaucoma, also can be detected by calculating the ratio of the area of intersection to the area of union as demonstrated by Muramatsua et al.[21]. Our approach has similar feature extraction step (from the ROI histogram for different regions ) to the methods in [17, 19 and 21]. As a result, the segmentation manner is closer to a clinical ground truth and the achieves an accuracy of confidence is 95%. The article is organized as follows. Initially, a review about the ION types and the related work is presented. Next, a detailed description of image preprocessing and extraction parameters from the ROI and data collection in the methodology is provided. Statistical analysis based on test of hypothesis and experimental results are described. Finally, the discussion and concluding remarks and future work directions are discussed in the last section. 2. Method and materials The phases of the proposed method are illustrated in Figure 2. Based on the extracted features from the histogram of region of interest (ROI), the test of hypothesis is applied to discriminate between images of Ischemic optic neuropathy (ION) and normal optic neuropathy (NON). Below is an introduction to the theoretical background of the proposed approach. 2.1 Image database


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The total number of the used images are 76 images of florescent retinal camera and were collected from the king Hussein medical city (KHMC) and other local Hospitals in Jordan between the years of 2007 and 2008. The fluorescent images were divided into three categories: I) 26 normal images, II) 28 images of IONU45, III) 22 images of IONA45, respectively. The reason for setting the threshold to 45 years to distinguish between IONs is that most of the critical variation of ION around the age of 45 years [9-11]. 2.2 Enhancement and segmentation

The enhancement and segmentation of IONs were performed as follows. Initially, a filtration stage is applied to improve the image quality, especially those with a small size or low contrast. Since, most of the images are fluorescent images contain ischemia, a low pass spatial filter produces good results on fluorescent images. In this stage the filtration is performed in the spatial domain. (i.e., the pixel of an image) using the LABVIEW® 7.1 software. A low pass filter allows the low frequency data, or data that does not change much from pixel to neighboring pixel, to pass through, removing the high frequency data, or data that changes rapidly from pixel to neighboring pixel [22-24].

Thresholding is one of the most important techniques for image segmentation. In this article, the segmentation employed to extract parameters relevant to the IONs is a histogram-based threshold. The images are subdivided into its constituents regions or objects, referred to as the region of interest (ROI). The ROI contains the arteries and veins in the ischemic eye image (Figure 3). The histogram step counts the total number of pixels in each grayscale value and plot it. The extracted information from the histogram are the minimum, maximum, mean, and standard deviation for the number of pixels (figures 4 and 5).

2.3 Statistical Analysis

Our approach is based on constructing the statistical information extracted from the histogram of an entire image or selected regions. The statistical analysis is designed using the three samples of people: 28 patients IONU45, 22 patients IONA45 and 26 are normal. This analysis is performed using two approaches: descriptive analysis and test of hypothesis.

Figure 3. Labview Front Panel GUI for ION

Pixe

ls n

umbe

r


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Pix

els

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Figure 4. Image processing result for ischemic eye (Under 45 years)

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Figure 5. Image processing result for ischemic eye (above 45 years)


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2.4 Descriptive analysis

Descriptive analysis is divided into many branches and gives many parameters describing the center or the variance of the population, by applying the most important parameters such as: average or arithmetic mean, median, mode, range, variance, standard deviation, the lower quartile (Q1) and the upper quartile (Q3) which are the 25th and 75th percentile of histogram, and the median, which is 50th percentile. Q1 is the value of x that exceeds one-four of the measurements and is less than the remaining, the position of Q1 is determined by 0.25(n+l). Q3 is the value of x that exceeds the three-fourth of the measurements and less than one fourth, the position of Q2 is determined by 0.75(n+ 1). If the output of the position is not an integer; the positions are determined by interpolation.

2.5 Test of hypotheses

To achieve the main object of this research, which is to classify images effected ION from normal cases, we employed the extracted features using testing of hypotheses to confirm the approach feasibility and the detection confidence level. The probability distribution of the two samples are calculated using the following formula:

(1) 5.0

niyprobabilit

n: number of sample, i: number of image. The test of the hypotheses is done by following steps[25]: Null hypotheses Ho :( µm- µb ) =D0 , where D0 is some difference between the mean values and we will consider D0 =0.Test statistics:

(2) 11

)21(

21

nnS

DXXt

p

(3) 2

)1()1(

21

221

nn

snnS p

Where Sp: pooled standard deviation.

Degree of freedom: (4) 221 nnd f

• Finding the critical value of 2

, , ttt is based on fdnn )2( 21 .

• Choosing value of α (type I error). • Finding the mean and the standard deviation for both samples.

Applying these steps on the analyzed samples, we could find the conclusion by reject or accept H0. As mentioned above, the test of hypotheses have the null hypotheses and the alternative. In the testing of hypotheses for the average pixels value the null hypotheses (H0) will be (for two tailed test); H0: µm = µb. The alternative hypothesis (Ha) will be (for two tailed test); Ha: µm >µb. 3. Results and discussion

Seventy six images of fluorescent retinal camera for the patients with age ranging from 30 to 65 years were tested to extract candidate features from each image modality. Preliminary results indicate that there is a difference in all statistical values for the extracted features in both normal and Ischemic images.

3.1 Image preparation

In preprocessing stage the averaging spatial low pass filter is demonstrating a remarkable results to blur and smooth the high frequency details of the entire image before applying the level threshold of ROI , as shown in figures 3, 4 and 5. The histogram for ROI in both normal and ischemic eye are different, since the histogram of ROI in ischemic eye image (above and less than 45 years) reaches a higher level of pixel values than the histogram of ROI in normal eye image.


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3.2 Statistical result

In this study, statistical analysis and validation is applied for all eye fluorescein angiography images using two steps of testing. The first step is based on the descriptive analysis and calculating the box plot for each group of eye images (ION) with age above 45 years and less 45 years respectively. The second step is to use the test of hypothesis on the mean of the two ION age categories. 3.2.1 Descriptive result

The average pixel values, maximum pixel values, and standard deviation pixel values were extracted from the ROI eye images (76). It is observed that there is a significance difference in the mean pixels value between the normal and ION samples. The developed descriptive analysis for the normal, IONA45, IONU45 eye images basically were drawn using the maximum and the average pixel values respectively.

The performance of descriptive test is represent in table 1 for the maximum and average pixel values in all images. It is noted that there is a difference in the mean value of the maximum pixel values between the normal (162.8) and IONU(219.5), while the patients above 45 years had a higher level(250.72) which is clinically acceptable. On the other hand, it is seen that the mean value of the average pixel values for the patients above 45 years is the highest value (163.36). The box plots were drawn in figure 6 for the value of maximum pixels for the three cases (normal, IONU45, and IONA45). These box plots indicate that the median values of features for IONA45 are slightly higher than IONU45. It can be seen from the box plots that, the mean (center line) of the boxes in the three cases are distinct and hence the features are significant. It is also seen in figure 7; the box plot of average pixel values for IONA45 clearly indicates a higher performance among of the normal and IONU45 where the center line in IONA45 lays in the range 165-167. It is noted, due to the slight variation in the eye images (IONA45, IONU45), the statistics for the distribution of the most relevant features for the classification are presented in table 1 and figures 6 and 7 as well. These statistics were calculated by descriptive analysis (box plotting test) and test of hypothesis (next section) and demonstrated significance ability between the normal eye and the rest of older people ION.

Table 1. Statistical parameters for maximum and average pixel values for normal and Ischemic

Optic Neuropathy under and above 45 years (IONU45, IONA45) Subject Maximum pixel values Average pixel values

Parameters Normal IONU45 IONA45 Normal IONU45 IONA45 Mean 162.8 219.5 250.72 134.03 136.3 163.36

Standard deviation 9.27 23.6 5.46 8.48 13.9 8.75

Variance 86.08 557.22 29.92 71.91 193.45 76.59

The lower quartile (Q1) 153.5 198 249.5 125.375 132.5 156.5

median 166 221.5 252.5 135.75 134.5 167

the upper quartile (Q3) 170 239.75 253.25 141 146 170.125

Number of sample 26 28 22 26 28 22


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1. Test of the hypothesis for maximum number of pixel (normal Vs IONU45)

In order to make a conclusive decision about the difference between the normal and IONU45, the null hypothesis tested against the alternative one as below:

To perform this test we assume H0: μ1 = μ2 and Ha: μ1 > μ2, where Ho is the null hypothesis and Ha is the alternative hypothesis. If t- calculated value> t- tabulated value, we reject H0 and accept H1

Table 2. Test of the hypothesis for maximum number of pixel values

Normal Vs IONU45 year Normal Vs IONA45 year Parameters Value Parameters Value

Mean 1 of normal(n) 162.807 Mean 1 of normal 162.807 Mean of ischemic under45(iu) 219.5 Mean of ischemic above 45(ia) 250.727

Standard variation Sn 86.081 Standard variation Sn 86.081 Standard variation Siu 557.222 Standard variation Sia 29.922

t- calculated value 42.028 t- calculated value 134.76 Pooled standard deviation Sp 18.18 Pooled standard deviation Sp 7.77

Significant level α 0.05 Significant level α 0.05 t- tabulated value 2.0084 t- tabulated value 2.0147

Degree of freedom dƒ 52 Degree of freedom dƒ 46 Number of sample n1 26 Number of sample n1 26 Number of sample n2 28 Number of sample n2 22

From the table 2 the t- calculated value (42.028)> t- tabulated value (2.008) and since the t-

measured is located in the rejection region, we reject the null hypotheses Ho and strongly conclude that the maximum pixel value for IONU45 is greater than the maximum pixel value for normal images with 95% confidence. On the other hand the same theory was employed on the IONA45 in table 2, where the t- calculated value (134.76)> t- tabulated value (2.014) and also we strongly conclude that the maximum pixel value for IONA45 is greater than the maximum pixel value for normal images with 95% confidence.

2. Test of the hypothesis for average number of pixel (normal Vs IONU45) :

The t-test for difference between two sample means will be used, this is because the two samples

have approximately linear probability plot. The parameters below are used to calculate the (t) statistics and compared to the tabulated value.

In order to perform this test we assume Ho: μn = μiu and Ha: μn > μiu, If t- calculated value> t- tabulated value so reject Ho and accept Ha . Then the mean of average value of the number of pixel of IONU45 is higher than the mean of average value of the number of pixel for normal eye as shown in

120

130

140

150

160

170

Normal IONA45 IONU45Figure 7. Box plot of Average pixels values

Ave

rage

pix

els

num

ber

160

180

200

220

240

260

Normal IONA45 IONU45Figure 6. Box plot of max pixels values

Max

Pix

els

Num

ber


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table 3. Table 3 illustrates the t- calculated value (2.637) > t- tabulated value (2.008), and since the t (calculated) is located in the rejection region, we reject the null hypotheses H0 and strongly conclude that there is a difference between the mean value of IONU45 and the mean value of normal images with 95% confidence. Then the average value of the number of pixel of IONU45 is higher than the average value of the number of pixel of normal. It is also noted from the table (3) that the t- calculated value (40.619) > t- tabulated value (2.014) for IONA45 compared with the normal case with higher mean value of the average than the normal.

Table 3. Test of the hypothesis for average number of pixel values

Normal Vs IONU45 year Normal Vs IONA45 year Parameters Value Parameters Value

Mean 1 of normal(n) 134.03 Mean 1 of normal 134.038 Mean of ischemic under45(iu) 136.303 Mean of ischemic above 45(ia) 163.36

Standard variation Sn 71.91 Standard variation Sn 71.918 Standard variation Siu 193.45 Standard variation Sia 76.59

t- calculated value 2.637 t- calculated value 40.619 Pooled standard deviation Sp 11.619 Pooled standard deviation Sp 8.6

Significant level α 0.05 Significant level α 0.05 t- tabulated value 2.0084 t- tabulated value 2.0147

Degree of freedom dƒ 52 Degree of freedom dƒ 46 Number of sample n1 26 Number of sample n1 26 Number of sample n2 28 Number of sample n2 22

3.3 Comparison against other methods In order to compare the proposed method to other retinal optic nerve segmentation algorithms, the

extracted features from segmented area were used as measures of method performance. Since, some of these measurements were reported by other authors, the choice facilities the comparison. Table 4 summarizes the comparison against the following published methods: Tobin et al. [18], Tan et al. [19], Lowell et al. [20], and Muramatsua et al. [21].

Clearly the common point between the proposed approach and others reported in literature is that all of them are determining to segment the optic disk area. The proposed method proves especially useful for detection of optic disk including the optic nerves located around the OD, and it has also the advantages (among others ): simplicity, manual segmentation around the optical nerve after processing and enhancement procedure in which physicians applied by a simple Labview GUI. Furthermore, the extracted features (average and maximum pixels value) analyzed in a simple statistical techniques such descriptive analysis and test of hypothesis, homogeneity of dataset of fluorescein angiography images, and it is focused on detection of one disease which is the ION. In addition to, our system was trained on the entire dataset.

On the other hands, Tobin et al. [18] has the highest performance (90.4%) among others published in literature, as shown in Table 4 . The slightly lower performance of the STARE dataset for this “Complete” population (i.e., 87.7% versus 90.4%) is primarily a result of the differences in enlargement, field of view, and image quality between the two sets. In Tan et al. [19] the target of this approach is to identify the glaucoma optic cup using (71) images of SiMES database. However, the use of GMM with expectation maximization (EM) algorithm is very sensitive to initialization and noise; and this may then affect its performance. Lowell et al. [20] have developed an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images. This method did not have the perfect performance (83%), because it is affected by the variable nature of the images and the presence of distractor boundaries concentric with the desired rim. Muramatsua et al. [21] provided a comparison analysis between three methods (ACM, FCM, and ANN) with average performance is (89%). These methods could not increase the performance because the majority of them were based on the contour modeling on the basis of the edge characteristics, and the different databases captured by different camera systems may possess different characteristics.


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Table 4 Performance results compared to other methods results.

Method type

Author(s)

Used data

Optic nerve detection performance %

Automatic detection of the optic nerve (ON) and localization of the macula

using digital red-free fundus photography.

Tobin et al. [17]

STARE database , 81 fundus images

90.4

Mixture model-based (GMM) approach for optic cup segmentation

compared with ARGALI software for cup segmentation

Tan et al. [18]

SiMES database, 71 glaucoma images

Achieved an improvement of 8.1%

in cup area overlap and 14.1% from the ARGALI cup segmentation

segmentation of the optic nerve head boundary in low-resolution images

Lowell et al. [19]

DR , 100 images 83

Segmentation of optic disc area in retinal fundus images for glaucoma

assessment then compared three diffrent methods that employed active contour model (ACM), fuzzy c-mean (FCM) clustering, and artificial neural

network (ANN)

Muramatsua et al. [21].

Gifu University Hospital, Japan.

80 images for retinal fundus

89

Segmentation of ONH and around areas covering the major optic nerves using threshold based histogram. The extracted features are employed for descriptive and test of hypothesis

statistical analysis.

Current work

Jordanian Hospitals. 76 fluorescein

angiography images for ION diseases.

95

4. Conclusion In this article, a novel method for analyzing and classifying Ischemic optic neuropathy in eye

images using 3 sets of available data (76 image) was developed. The method consists of preprocessing of fluorescent angiography images using Labview software. The essential part of this method was utilizing the suitable histogram – threshold segmentation tool in order to extract the valuable parameters and information about the anterior ischemic optic neuropathy from the region of interest. The developed method was combined with statistical validation part such box plotting descriptive test and test of hypothesis, where the result were promising in distinguishing between ION disease for people older than 45 years with significance accuracy about 95%. Nevertheless, the method has some limitation like the difficulty of extraction the pixel values underneath of the optic veins only, the method can further be improved by taking more diverse images and experiments that would assist doctors to make their diagnostic decisions properly in a less time and money cost. This method compared with other application in previous section and achieves its objective.

5. Conflict of interest The authors have no conflict of interest. There are no personal or financial relationships with other

people or organizations that influence the work inappropriately.


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6. References

[1] W. Shuqin, X. Zhitao, W. Jun, G. Lei, Z. Fang, X. Jiangtao, X, "Fundus blood vessels detection based on pulse coupled neural network", JDCTA, Vol. 6, No. 15, pp. 467 ~ 474, 2012. [2] H.D. Pomeranz, A.R. Bhavsar, "Nonarteritic ischemic optic neuropathy developing soon after use of sildenafil (viagra): a report of seven new cases", J Neuroophthalmol.,Vol. 25, no. 1, pp. 9-13, 2005. [3] A. Boshier, N. Pambakian, S. A. Shakir, "A case of nonarteritic ischemic optic neuropathy (NAION) in a male patient taking sildenafil", Int J Clin Pharmacol Ther., Vol. 40, no. 9, pp. 422-423, 2002. [4] V. Egan, H. Pomeranz, "Sildenafil (Viagra) associated anterior ischemic optic neuropathy", Arch Ophthalmol., Vol. 118, no. 2, pp. 291-292, 2000. [5] G. Athappilly, V. Pelak, N. Mandava, and J. Bennett, "Ischemic optic neuropathy", Neurological Research, Vol. 30, no. 8, pp. 794-800, 2008. [6] S. S. Hayreh, "Ischemic optic neuropathy", Progress in Retinal and Eye Research, Vol. 28, no. 1, pp. 34-62, 2009. [7] S. L. Bernsteina, M. A. Johnson, and N. R. Miller,"Nonarteritic anterior ischemic optic neuropathy (NAION) and its experimental models", Progress in Retinal and Eye Research, Vol. 30, no. 3,pp. 2011,167-187. [8] S. S. Hayreh and B. Zimmerman, "Visual Field Abnormalities in Nonarteritic Anterior Ischemic Optic Neuropathy", Arch Ophthalmol., Vol. 123, no. 11, pp. 1554-1562, 2005. [9] R. Behbehani, "Clinical approach to optic neuropathies", Clin Ophthalmol., Vol. 1, no. 3, pp. 233–246, 2007. [10] P. Pianka, Y. Almog, O. Man, M. Goldstein, B. A. Sela and A. Loewenstein "Hyperhomocystinemia in patients with nonarteritic anterior ischemic optic neuropathy, central retinal artery occlusion, and central retinal vein occlusion", Ophthalmology, Vol. 107, no. 8, pp. 1588-92, 2000. [11] V. A Deramo, R. C. Sergott, J. J. Augsburger, R. Foroozan, P. J. Savino and A. Leone, "Ischemic optic neuropathy as the first manifestation of elevated cholesterol levels in young patients", Ophthalmology, Vol. 110, no. 5, pp. 1041-1046, 2003. [12] D. Marín, A. Aquino, Manuel Emilio Gegúndez-Arias, and José Manuel Bravo, "A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features", IEEE transactions on medical imaging, Vol. 30, no. 1, pp. 154-158, 2011. [13] C. A. Lupascu, D. Tegolo, E. Trucco, "Retinal Vessel Segmentation Using AdaBoost", IEEE Transactions On Information Technology In Biomedicine, Vol. 14, no. 5, pp. 1267-74,2010. [14] C. Köse, U. Şevik, C. İkibaş H. Erdöl, "Simple methods for egmentation and measurement of diabetic retinopathy lesions in retinal fundus images", Comput. Methods Programs Biomed., Vol. 107, no. 2, pp. 274-293, 2012. [15] M. Tavakoli, M. H .B. Toosi, R. Pourreza, T. Banaee, H. R. Pourreza, "Automated optic nerve head detection in fluorescein angiography fundus images", IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 3057 – 3060, 2011. [16] G. D. Joshi, J. Sivaswamy, S. K. Krishnadas, "Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment", IEEE Transactions on Medical Imaging, Vol. 30, no. 6, pp. 1192-1205, 2011. [17] K.W. Tobin, E. Chaum, V. P. Govindasamy, and T. P. Karnowski, "Detection of Anatomic Structures in Human Retinal Imagery", IEEE Transactions on Medical Imaging, Vol. 26, no. 12, pp. 1729-1739, 2007. [18] N. M. Tan, J. Liu, D. W. K. Wong, F. Yin, J. H. Lim and T. Y. Wong, "Mixture model-based approach for optic cup segmentation", Annual International Conference of the IEEE , pp. 4817-4820, 2010. [19] J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher and L. Kennedy," Optic nerve head segmentation", IEEE Transactions on Medical Imaging, Vol. 23, no. 2, pp. 256-264, 2004. [20] S. Echegaray, P. Soliz and W. Luo, "Automatic initialization of level set segmentation for application to optic disc margin identification", IEEE International Symposium on Computer-Based Medical Systems, pp. 1-4, 2009.


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[21] C. Muramatsua, T. Nakagawab, A. Sawadac, Y. Hatanakad, T. Haraa, T. Yamamotoc and H. Fujitaa, "Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods", Computer methods and programs in biomedicine, Vol. 101, no. 1, pp. 23–32, 2011. [22] B. Al-Naami, A. Bashir, H. Amasha, J. Al-Nabulsi, and A. Almalty, "Statistical Approach for Brain Cancer Classification Using a Region Growing Threshold", Journal of Medical Systems, Vol. 35, no. 4, pp. 463–471, 2011. [23] A. Al-Bashir, B. Al-Naami, "A fusion technique based on image - statistical analysis for detection of throat cancer types", Jordan Journal of Mechanical and Industrial Engineering, Vol. 4, no. 6, pp. 677 – 684,2010. [24] R. C. Gonzalez, E. R. Woods and L. S. Eddins, Digital image processing, second Edition, Prentice Hall, USA, 2004. [25] C. D. Montgomry and G. C. Runger, Applied Statistics and Propability for Engineers, Fourth Edition, John Wiley and Sons, USA, 2008.


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Classification of Ischemic Optic Neuropathy Using …Ischemic optic neuropathy (ION), is one of the most well-known disease which could lead to vision loss or blindness. ION impacts