Bridging Quantitative and Qualitative of Glaucoma Detection

� Abstract≤Glaucoma diagnosis involves extracting three features of the fundus image; optic cup, optic disc and vernacular. Present manual diagnosis is expensive, tedious and time consuming. A number of researches have been conducted to automate this process. However, the variability between the diagnostic capability of an automated system and ophthalmologist has yet to be established. This paper discusses the efficiency and variability between ophthalmologist opinion and digital technique; threshold. The efficiency and variability measures are based on image quality grading; poor, satisfactory or good. The images are separated into four channels; gray, red, green and blue. A scientific investigation was conducted on three ophthalmologists who graded the images based on the image quality. The images are threshold using multi- thresholding and graded as done by the ophthalmologist. A comparison of grade from the ophthalmologist and threshold is made. The results show there is a small variability between result of ophthalmologists and digital threshold.

[1]  Biswajit Mondal.  Slit-Lamp Biomicroscopy in Primary Eye Care , 2013 .

[2]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[3]  Hiroshi Fujita,et al.  Automatic measurement of cup to disc ratio based on line profile analysis in retinal images , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  E. Anastasopoulos,et al.  Glaucoma — The Importance of Early Detection and Early Treatment , 2007 .

[5]  Bülent Sankur,et al.  Color image segmentation using histogram multithresholding and fusion , 2001, Image Vis. Comput..

[6]  Zhu Yan-wei.  Fuzzy Cluster Analysis and Application Based on PCA , 2011 .

[7]  Jayanthi Sivaswamy,et al.  Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment , 2011, IEEE Transactions on Medical Imaging.

[8]  M. Madheswaran,et al.  Extraction of Blood Vascular Network for Development of an Automated Diabetic Retinopathy Screening System , 2009, 2009 International Conference on Computer Technology and Development.

[9]  Yazhu Chen,et al.  A Computer-based Diagnosis System for Early Glaucoma Screening , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[10]  K. Duraiswamy,et al.  AN EFFICIENT DECISION SUPPORT SYSTEM FOR DETECTION OF GLAUCOMA IN FUNDUS IMAGES USING ANFIS , 2012 .

[11]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Digital Fundus Images , 2009, Journal of Medical Systems.

[12]  Wen-Xiong Kang,et al.  The Comparative Research on Image Segmentation Algorithms , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[13]  J. Liu,et al.  Automatic glaucoma diagnosis from fundus image , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[15]  László G. Nyúl,et al.  Retinal image analysis for automated glaucoma risk evaluation , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[16]  S. Karthikeyan,et al.  Neuroretinal rim Quantification in Fundus Images to Detect Glaucoma , 2010 .

[17]  K. Narasimhan,et al.  AN EFFICIENT AUTOMATED SYSTEM FOR GLAUCOMA DETECTION USING FUNDUS IMAGE , 2011 .

[18]  M. Madheswaran,et al.  Enhancement of optic cup to disc ratio detection in glaucoma diagnosis , 2012, 2012 International Conference on Computer Communication and Informatics.

[19]  J. Liu,et al.  Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[20]  Huiqi Li,et al.  A model-based approach for automated feature extraction in fundus images , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[21]  G Maclennan,et al.  The accuracy of accredited glaucoma optometrists in the diagnosis and treatment recommendation for glaucoma , 2007, British Journal of Ophthalmology.

[22]  Prasanta K. Panigrahi,et al.  Multilevel thresholding for image segmentation through a fast statistical recursive algorithm , 2006, Pattern Recognit. Lett..

[23]  A. Sukesh Kumar,et al.  Early detection of retinal nerve fiber layer defects using fundus image processing , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[24]  Mei-Ling Huang,et al.  Glaucoma detection using adaptive neuro-fuzzy inference system , 2007, Expert Syst. Appl..

[25]  Kenneth W. Tobin,et al.  Automatic detection of retina disease: Robustness to image quality and localization of anatomy structure , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  J. Nayak,et al.  Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

[27]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.

[28]  Hermawan Nugroho,et al.  Analysis of foveal avascular zone in colour fundus images for grading of diabetic retinopathy severity , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.