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Designing a Diagnostic System of Patients Suspected with Glaucoma Using Retinal Fundus Images

Fateme Moghbeli, Mostafa Langarizadeh, Navid Nilforoushan, Hossein Eghbalian Arani, Azam Orooji
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Abstract

Introduction: Glaucoma is one of eye diseases and is a group of disorders that cause damages to optic nerve. If it does not cure; it can lead to permanent vision loss. There is no sign and pain for this disease and it appears in form of blindness in very advanced phase of its progression. Digital imaging is a useful tool for non-invasive measurements in medical field. By using image processing techniques, many medical images could be processed and analyzed, helping experts to detect diseases, decreasing cost and time of tests and helping to do screening significantly. The main purpose of this study was to suggest an algorithm in order to detect glaucoma suspects in retinal fundus images.

Material and Methods:  This study was an original applied study and its algorithm was developed in MATLAB environment using image processing toolbox. It was evaluated by using existed images obtained from RIM-ONE database. In the present paper, Otsu multi-level thresholding algorithm was used along with MICO algorithm to segment the optic cup and disc and estimate their vertical diameter.

Results:  The algorithm was run on 124 images containing 57 images of glaucoma suspects and 67 images of healthy retina. The sensitivity and specificity were 0.93 and 0.90 respectively.

Conclusion:  Suggested algorithm worked fast because light pre-processing has been done. However, there is no system with 100% correct diagnosis, the results obtained from suggested system was acceptable. Thus using such systems could be helpful for practitioners.

References

Wang S. Image processing of OCT glaucoma images and information theory analysis, University Of Denver; 2009.

zur Glaukomerkennung S. Diffusion Tensor Imaging Analysis of the Visual Pathway with Application to Glaucoma. 2012.

Liu Y-Y, Ishikawa H, Chen M, Wollstein G, Schuman JS, Rehg JM. Longitudinal Modeling of Glaucoma Progression Using 2-Dimensional Continuous-Time Hidden Markov Model. Med Image Comput Comput Assist Interv: Springer; 2013:444-451.

Zhang Z, Kwoh CK, Liu J, et al. MRMR optimized classification for automatic glaucoma diagnosis. Paper presented at: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE2011.

Zhang Z, Srivastava R, Liu H, et al. A survey on computer aided diagnosis for ocular diseases. BMC medical informatics and decision making. 2014;14(1):80.

Haleem MS, Han L, van Hemert J, Li B. Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review. Computerized Medical Imaging and Graphics. 2013;37(7):581-596.

Zheng Y, Stambolian D, O’Brien J, Gee JC. Optic disc and cup segmentation from color fundus photograph using graph cut with priors. Med Image Comput Comput Assist Interv: Springer; 2013:75-82.

Kauppi T. Eye fundus image analysis for automatic detection of diabetic retinopathy. Lappeenranta University of Technology; 2010.

Langarizadeh M, Mahmud R, Ramli A, Napis S, Beikzadeh M, Rahman WWA. Effects of enhancement methods on diagnostic quality of digital mammogram images. Iranian Journal of Cancer Prevention. 2012;3(1):36-41.

Zarghani H, Bahreyni Toossi MT. Evaluation of Organ and Effective Doses to Patients Arising From Some Common X-Ray Examinations by PCXMC Program in Sabzevar, Iran. Iranian Journal of Medical Physics. 2016;12(4):284-291.

Odstrčilík J. Analysis Of Retinal Image Data To Support Glaucoma Diagnosis, University Of Technology, Brno; 2014.

Fengshou Y. Extraction of features from fundus images for glaucoma assessment, National University of Singapore; 2011.

Safdari R, Kadivar M, Langarizadeh M, Nejad AF, Kermani F. Developing a fuzzy expert system to predict the risk of neonatal death. Acta Informatica Medica. 2016;24(1):34.

Karami M, Fatehi M, Torabi M, Langarizadeh M, Rahimi A, Safdari R. Enhance hospital performance from intellectual capital to business intelligence. Radiol Manage. 2013;35(6):30-35.

Maghsoudi B, Langarizadeh M, Nilforushan N. Decision support system for age-related macular degeneration. Iranian Journal of Medical Physics. 2017.

Wong DW, Liu J, Tan NM, et al. An ensembling approach for optic cup detection based on spatial heuristic analysis in retinal fundus images. Paper presented at: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE2012.

Bayani A, Shahmoradi L, Langarizadeh M, Radmard A, Nejad A. Quality improvement of liver ultrasound images using fuzzy techniques. Acta Informatica Medica. 2016;24(6):380-384.

Langarizadeh M, Mahmud R, Ramli A, Napis S, Beikzadeh M, Abdul Rahman W. Effects of image processing techniques on mammographic phantom images: A pilot study. Pertanika Journal of Science and Technology. 2011;19(1):67-76.

Langarizadeh M, Moghbeli F. Applying Naive Bayesian Networks to Disease Prediction: A Systematic Review. Acta Informatica Medica. 2016;24(5):364.

Cheng J, Liu J, Tao D, et al. Superpixel classification based optic cup segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: Springer; 2013.

Fumero F, Alayón S, Sanchez J, Sigut J, Gonzalez-Hernandez M. RIM-ONE: An open retinal image database for optic nerve evaluation. Paper presented at: Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on2011.

Li C, Gore JC, Davatzikos C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnetic resonance imaging. 2014;32(7):913-923.

Langarizadeh M, Mahmud R. Breast Density Classification Using Histogram-Based Features. Iranian Journal of Medical Informatics. 2012;1(1):22.


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