A Novel Approach towards Automatic Glaucoma Assessment

Transcription

1 281 A Novel Approach towards Automatic Glaucoma Assessment Darsana S 1, Rahul M Nair 2 1 MTech Student, 2 Assistant Professor, 1,2 Department of ECE, Nehru College of Engineering and Research Centre Thrissur, University of Calicut, Kerala ABSTRACT Glaucoma is a progressive eye disease and is called Silent Theif of Sight. As it cannot be cured, detecting the disease in time is very important. Currently, glaucoma assessment is manually performed by trained ophthalmologists limiting its potential for population based glaucoma screening. Thus there is a need for an efficient automatic glaucoma assessment technique. This paper proposes automatic glaucoma assessment by combined analysis of fundus eye image and patient data. Fundus image feature extraction and ocular parameter evaluation are carried out for image level analysis. The techniques used for feature extraction include color model analysis, morphological processing, filtering and thresholding. Ocular parameters considered are Cup to Disc Ratio (CDR), Rim to Disc Ratio(RDR), cup to disc area ratio and Inferior Superior Nasal Temporal(ISNT) ratio of bloodvessels in disc region. The CDR, RDR and cup to disc area ratio based on optic disc, cup and rim are calculated using image measuring techniques. Mask generation and feature segmentation based on Array- Centroid method is proposed for RDR and ISNT ratio calculation. Image level classification makes use of ocular parameters and SVM is used to classify the images as normal or glaucoma suspect. Data level analysis uses patient data and classification is done with the help of risk calculator. Then a combined glaucoma risk analysis is performed to label a risk class to the patient. MATLAB software is used for developing user interface for the proposed approach. Performance analysis is carried out for image level, data level and combined glaucoma analysis. Keywords - Array- Centroid Method, Classification, Fundus Image, Glaucoma, Patient Data, I. INTRODUCTION The recent rapid advances in medical imaging and automated image analysis allow us to make significant advances in our understanding of life and disease processes, and our ability to deliver quality healthcare. Fundus eye image processing is now a core field of research for diagnosis of various eye disorders. Glaucoma assessment is one of the applications of fundus image processing. Glaucoma is a chronic and irreversible neurodegenerative disease in which the nerve that connects the eye to the brain is progressively damaged. Progression of the disease leads to loss of vision, which occurs gradually over a long period of time. Unawareness about the disease until it reaches the advanced stage is a major problem as it cannot be cured. According to World Health Organization, glaucoma is the leading cause of blindness that contributes to approximately 5.2 million cases of blindness and will increase to 11.2 million people by 2020[2]. So detecting the disease in time is critical and population based glaucoma assessment is very relevant to save the vision of millions. Glaucoma assessment performed by trained ophthalmologists limits its potential for population based glaucoma screening. There comes the need for an efficient automatic glaucoma assessment technique. The main ocular indicators of glaucoma are Optic CDR, RDR, ISNT rule, ISNT ratio, PPA, Notching etc. There are some nonocular factors like age, race, family history etc which determine the risk of glaucoma. Research is being done to fully automate the glaucoma assessment. J Cheng et al, (2013) [1] proposed a superpixel classification based disc and cup segmentations for glaucoma screening. C.B. Anusorn et al, (2013) [2] proposed a method to calculate the CDR automatically from fundus images. To automatically extract the disc, two methods making use of edge detection and variational level set are proposed and cup segmentation is evaluated using color component analysis and threshold level set method. K. Narasimhan et al, (2012) [3] proposed a semi automated method for glaucoma detection using CDR and ISNT ratio of a fundus image. O.Sheeba et.al (2014)[4] proposed automatic detection using artificial neural network. In the proposed method neural network is trained to recognize the parameters for the detection of different stages of the disease. The

2 282 neuron model has been developed using feed forward backpropagation network. This paper proposes a novel approach towards automatic glaucoma assessment. The efforts taken to develop an automatic glaucoma assessment technique have an integration of fundus image analysis and patient data analysis. Finally a combined glaucoma risk analysis is performed and a risk class is labelled for each set of input. In fundus image analysis fundus image is preprocessed and features are extracted. The feature extraction involves optic disc segmentation, optic cup segmentation, optic rim segmentation and blood vessel extraction. An array centroid method is proposed to segment the extracted features into ISNT quadrants. Then ocular parameters such as CDR, cup to disc area ratio, Inferior RDR, Superior RDR and ISNT Ratio are calculated. SVM classifier is proposed for classification of image as glaucoma suspect or normal. A risk calculator analyses a set of patient data in the next stage and a risk level is assigned to each set. The patient data considered are age, race, family history related to glaucoma and last eye examination record. Finally glaucoma risk is analysed based on the results of image and data analysis. The five classes defined in this analysis include no risk, low risk, moderate risk, high risk and very highrisk. Development of an user interface allows any user to take advantage of the proposed technique. The paper is organized as follows. Section II describes the various stages of the proposed method. Section III shows experimental results. Conclusion and future scope are presented in section IV. Acknowledgement is given in section V. race, family history and medical examination records of the person being examined. Data level classification classifies the patient into one of the three classes, low risk, medium risk, high risk. The results of image level classification and data level classification is used for final glaucoma risk analysis.. Fig. 1 Flowchart of Proposed Method 2.1 FUNDUS IMAGE PROCESSING The fundus eye image represents the interior surface of the eye and is used as the input for processing. A sample input image is shown in Fig 2 II. METHODOLOGY The flow chart for the proposed method is shown in Fig. 1 The image level and data level processing are carried out inorder to analyse the glaucoma risk of a patient. In image level processing fundus eye image is used as input. Fundus image processing involves preprocessing and feature extraction. Ocular parameters to be estimated are Optic CDR, Optic RDR, cup to disk area ratio and ISNT ratio. Image level classification make use of SVM classifier and classify image as normal or glaucoma suspect. Data level processing uses patient s personal data as input. The risk calculator is used for patient data analysis. Patient data involves age, Fig. 2 Input Image The fundus image processing involves preprocessing and feature extraction. The four feature extraction processes are optic disc segmentation, optic cup segmentation, optic rim segmentation and blood vessel extraction. The block diagram in Fig 3 illustrates the entire process of fundus image processing.

3 283 The value channel is used for optic disc segmentation. The morphological closing operation is performed in the value channel image in order to remove the blood vessels. Then median filtering is done to remove noise and preserve edges. After filtering, thresholding is done using a manually selected threshold value, as the prior information about the region to be segmented is known. The optimum threshold value used is the top 1/10 of the normalized grayscale intensity. Again morphological opening is done inorder to remove the unwanted pixels around the segmented optic disc[2]. Fig. 4 shows the segmented optic disc. Fig. 3 Fundus Image Processing IMAGE PREPROCESSING Pre-processing includes RGB (Red Green Blue) separation, RGB to HSV (Hue Saturation Value) conversion, RGB to CMY (Cyan Magenta Yellow) conversion, histogram representation and channel selection. The RGB model provides three channel maps, Red, Green and Blue. Each of the channel represents the image in a different manner. HSV model also provides three channels, Hue, Saturation and Value, each with its own characteristics. CMY color space is the complement color space of RGB which also highlights the features within the image. From RGB, HSV and CMY color space the best channel has to be selected for segmentation of optic disc and cup and blood vessel extraction. The main aim of channel selection is to select the most appropriate channel which best describes the optic disc, optic cup and blood vessels. The channel selection depends on the color or intensity characteristics of optic disc, optic cup and blood vessels. The optic disc represents the brightest region within the retina and optic cup is the white region inside the optic disc. Blood vessels appear as thick red vessels within the image. By evaluating each channel it is found that the value channel best describes the brightest region in the fundus image and can be used for optic disc segmentation. The magenta channel in CMY color space best describes the white cup region and blood vessels within the fundus image. So this channel is selected for optic cup segmentation and blood vessel extraction OPTIC DISC SEGMENTATION (a) (b) Fig. 4 Segmented Optic Disc (a) Before Morphological Opening (b) After Morphological Opening OPTIC CUP SEGMENTATION The magenta channel is selected for optic cup segmentation. The contrast of the magenta channel is enhanced inorder to better represent the optic cup region. The morphological opening operation is done in the magenta channel inorder to remove the blood vessels in the cup region. Median filter is used to reduce the noise in the morphologically processed image. Then the cup region is segmented by thresholding where the threshold value is selected as the bottom 1/10 of the grayscale intensity. The segmented optic cup is shown in Fig 5. Fig. 5 Segmented Optic Cup OPTIC RIM SEGMENTATION

4 284 Optic rim is the region between the optic disc and optic cup. Once the optic disc and optic cup are segmented the rim region can be obtained by subtracting the optic cup from optic disc as shown in Fig 6. temporal quadrant is the left quadrant in right eye and right quadrant in left eye. Inorder to get segments in ISNT quadrants a new Array Centroid method is proposed ARRAY CENTROID METHOD The array-centroid method based mask generation and feature segmentation is proposed to segment the optic disc, optic rim and blood vessels in disc region into four different quadrants (inferior, superior, nasal, temporal). The flow chart of method is shown in Fig 8. Fig. 6 Segmented Optic Rim Region BLOOD VESSEL EXTRACTION The blood vessels in the optic disc region have to be extracted inorder to calculate the ISNT ratio. The first step in blood vessel extraction is the contrast enhancement of selected magenta channel. The morphological filtering can filter out the blood vessels within the image. Thus tophat filtering is done using a square structuring element. The image is further enhanced and the histogram is analysed to set a threshold for thresholding. The blood vessels in the disc region as shown in Fig. 7 are obtained by complementing the disc region and then subtracting it from the blood vessel image. Fig 8 Flow Chart of Array Centroid Method The first step of mask generation is to find the centroid of the binary image. Then an array of size same as that of the image is considered and initialized with all its elements zero. Then the masks for the different quadrants are generated exploiting the characteristics of an array and the centroid values[5]. The mask generated using the above method is shown in Fig 9. Fig. 7 Blood Vessels in Disc Region 2.2 OCULAR PARAMETER EVALUATION For ocular parameter evaluation disc diameter, cup diameter, disc area and cup area are to be calculated using image measuring techniques. RDR and ISNT ratio calculation make use of the disc region, rim region and blood vessels in ISNT quadrants. Inferior quadrant is the lowermost quadrant and superior quadrant is the uppermost quadrant. The nasal quadrant is the right quadrant in right eye and left quadrant in left eye. The (a) (b) (c) (d) Fig. 9 Mask in Four Quadrants (a) Inferior (b) superior (c ) Right (d) Left The generated masks are further multiplied with the segmented features and thus obtain the segments in four quadrants

5 CUP TO DISC RATIO CDR is defined as the ratio of vertical cup diameter to vertical disc diameter. CDR is one of the important indicator of glaucoma because as glaucoma advance the cup enlarges until it occupies most of the disc area and there will be no change for optic disc. Thus increase in CDR indicates the pathological condition. For normal eye it is found to be 0.3 to 0.5. As the neuro-retinal degeneration occurs the ratio increases and at the CDR value of 0.8 the vision will be lost completely. CDR is obtained by taking the ratio of cup diameter and disc diameter in the vertical direction RIM TO DISC RATIO The cardinal clinical sign of glaucomatous optic neuropathy is thinning of the neuroretinal rim, with the regional preference for the superior and inferior poles of the optic disc. RDR is calculated for the inferior and superior quadrant as the ratio of rim area to disc area CUP TO DISC AREA RATIO The cup to disc area ratio is determined by taking the ratio of total cup area to total disc area. This area ratio is selected to assess the overall segmentation accuracy achieved in all directions unlike the CDR which reflects accuracy only in vertical direction ISNT RATIO ISNT ratio is calculated using the ratio of area of blood vessels in inferior superior to nasal temporal side of optic disc. There may be a shift in blood vessels to nasal side in glaucoma patients and thus the ISNT ratio will be less compared to normal person s image[3]. 2.3 RISK CALCULATOR DEVELOPMENT The non ocular parameters which determine the risk of glaucoma involves age, race, family history and last eye examination record. According to these patient data some weights are assigned for each case and finally total weight is calculated and risk level is decided as low, moderate or high. The standard risk calculator chart for this evaluation is given in Table 1 Parameters TABLE 1.RISK CALCULATOR CHART Non Ocular Parameter Evaluation Category Age < >75 Race Family History of Glaucoma Last Eye Exaination Record Caucasian/Other African/American ± in non relative +for parent(s) +for sibling(s) +for parent(s) and sibling(s) Within past two years 2-5 years >5 years 2.4 CLASSIFICATION Weight Point Risk Based on Total Score 4 High Risk =3 Moderate Risk 2 Low Risk The classification of fundus image as normal or pathological is not a simple task. The main reason is that there are many factors to be evaluated to suspect glaucoma. Thus here an image level classification, data level classification and a combined glaucoma risk analysis is performed IMAGE LEVEL CLASSIFICATION The term image classification refers to the labeling of images into one of a number of predefined categories.here image level classification makes use of SVM classifier to classify the image into normal or glaucoma suspect. Classification is based on the ocular parameters evaluated. Here training of SVM is carried out with a set of 30 images. SVM is trained first with the 15 normal images and then with 15 glaucoma images. After training the SVM, testing is done with 70 images. SVM classified each image into either normal or glaucoma suspect. After classification a weighting score is assigned for each class as shown in Table 2. TABLE I.IMAGE LEVEL CLASSIFICATION Class Score Normal 0 Glaucoma Suspect 2

6 DATA LEVEL CLASSIFICATION Data level classification is done using the risk calculator. Here each patient is being classified into one of the three levels of glaucoma risk. After classification a weighting score is assigned for each class for further analysis as in Table 3. TABLE 3.DATA LEVEL CLASSIFICATION Risk Level Score Low 0 Moderate 1 High COMBINED GLAUCOMA RISK ANALYSIS This is the final classification and determines the risk of glaucoma for the patient. It is obtained by the sum of scores obtained in image level and data level classification. In this approach five risk levels are defined and each patient is assigned into one of the risk level for glaucoma. The combined glaucoma risk analysis chart is given in Table 4 TABLE 4.COMBINED GLAUCOMA RISK ANALYSIS Image Level Result Data Level Result Image Level Score Data Level Score Total Score Risk For Glaucoma Normal Low No Risk Normal Moderate Low Risk Normal High Moderate Risk Glaucoma Suspect Low Moderate Risk Glaucoma Moderate High Risk Suspect Glaucoma Suspect High Very High Risk III. EXPERIMENTAL RESULTS The development of an efficient user interactive environment for automatic glaucoma assessment is the solid outcome of this work. The user interface is developed using MATLAB GUI. The developed user interface is shown in Fig 10. Fig. 10 Developed User Interface The performance of the proposed technique is analysed in three sections. In the first section the performance of image based classification is analysed. A total of 70 images are tested using the trained SVM which include 25 glaucomatous image and 45 normal images. Out of 70 images 67 images are classified correctly. The

7 287 performance analysis is done by calculating sensitivity, specificity and accuracy. The results obtained are 97.7% sensitivity, 92% specificity and 95.7% accuracy. The performance of risk calculator is analysed in the second section. Risk calculator effectively calculates the score for every set of data inputs assuring high accuracy. Finally the combined glaucoma risk analysis is analysed in the third section. The classification accuracy of this stage is a clear reflection of above stages. The risk labeled to each patient at this classification level will be a valuable reference for the clinicians for their further assessment. IV. CONCLUSION AND FUTURE SCOPE An efficient technique for automatic glaucoma assessment is proposed in this work. The image based and data based integrated analysis accelerates the potential for classifying the sample inputs. In image based analysis fundus image serves as the input. The preprocessing of image and feature extraction which includes optic disc segmentation, optic cup segmentation, optic rim segmentation and blood vessel extraction are carried out in the first stage. Then the ocular parameters such as CDR, Cup to Disc Area ratio, Inferior and Superior RDR, and ISNT Ratio are calculated making use of array centroid method and image measuring techniques. Image based classification is performed using SVM classifier which classifies image as either normal or glaucoma suspect and a weighting score is assigned to each class. Data level analysis using the patient data set including age, race, family history, last eye examination record is performed with developed risk calculator. The calculator calculates a risk score based on data input and based on that each patient is classified into one of the three risk groups; low, moderate and high. A weight score is assigned to these classes also. Finally a combined glaucoma risk analysis is done based on the results of image and data based classification. This classification gives one of the five risk labels; no risk, low risk, moderate risk, high risk, and very high risk to each patient. Development of a user interface is the highlight of this work which enables any user to make use of the advantage of the proposed technique. Performance analysis for the proposed work is carried out and the results are upto the expectations. So it can be concluded that the proposed approach is an efficient technique for glaucoma assessment which can save the vision of millions. The potential limitation of this approach is that sometimes pathological condition exists in patient without reflecting any of the factors considered in the analysis. It is well projected in image based classification. Out of 70 images 3 are misclassified,it is not the limitation of classifier it is the limitation of the approach towards classification. The proposed work can be extended by integrating more factors in image based analysis which reflects the glaucoma symptoms. The factors that can be included are notching, disc hemorrhage, inter eye symmetry, peripappilary atrophy etc. Analysis of these factors require in depth processing of the fundus image. Another way to enhance the performance of automatic assessment is to use 3D fundus images. The main barrier for such a work is the unavailability of 3D images. The high cost of obtaining 3D images makes it inappropriate for a large scale screening. But 3D image based analysis will boost the performance of glaucoma assessment as it can assess the depth information of the eye. Thus a compromise in cost and risk of availability can achieve more efficient technique. V. ACKNOWLEDGEMENT The authors are grateful to Dr. Rajesh Radhakrishnan. M. S Ophtalmic Surgeon and Glaucoma Consultant, Adithya Kiran Eye Care Centre Palakkad for providing the fundus image photographs and the guidance and support given for this work. REFERENCES [1] J. Cheng, J. Liu, Y. Xu, F. Yin, D.W. K. Wong, N. M. Tan, D. Tao, C.Y. Cheng, T. Aung, and T. Y. Wong, Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening, IEEE Transactions on Medical Imaging., vol.32, no. 6, pp , June [2] C. B.Anusorn, W. Kongprawechnon, T.Kondo, S. Sintuwong and K. Tungpimolrut, Image Processing Techniques for Glaucoma Detection Using the Cup to Disc Ratio, Thammasat International Journal of Science and Technology., vol.18, no.1, pp.22-33,jan-march [3] K. Narasimhan, K. Vijayarekha, K. A. JogiNarayana, P. SivaPrasad and V. SathishKumar, Glaucoma Detection From Fundus Image Using

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