Texture based on geostatistic for glaucoma diagnosis from fundus eye image

Glaucoma is an ocular disorder that can permanently damage patient vision. Initially, it reduces the visual field, and may cause blindness. Effective methods for early detection is crucial for avoiding significant damages of the patient vision. The use of CAD (Computer-Aided Detection) and CADx (Computer-Aided Diagnosis) systems has contributed to increase the chances of detection and precise diagnoses, assisting experts’ decision making on treatment regarding glaucoma. This paper proposes a method that analyzes the texture of the optical disk image region to diagnose glaucoma. Such analysis is done using the Local Binary Pattern (LBP) to represent the optic disk region, and geostatistical functions to describe texture patterns. The obtained texture features are used for classification based on Support Vector Machine. The proposed method presented as best results a sensitivity of 95%, accuracy of 91% and specificity of 88% in the diagnosis of glaucoma. The method has proved to be promising in assisting glaucoma diagnosis.

[1]  Jayanthi Sivaswamy,et al.  Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs , 2016, Journal of glaucoma.

[2]  S. Sudha,et al.  Glaucoma detection from retinal images , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[3]  M. Dubey.  Design of Genetic Algorithm Based Fuzzy Logic Power System Stabilizers in Multimachine Power System , 2008, 2008 Joint International Conference on Power System Technology and IEEE Power India Conference.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Anselmo Cardoso de Paiva,et al.  Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index , 2014, Artif. Intell. Medicine.

[6]  Joachim Hornegger,et al.  Computer-Aided Diagnostics and Pattern Recognition: Automated Glaucoma Detection , 2015 .

[7]  T. Wong,et al.  Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. , 2014, Ophthalmology.

[8]  Dinesh Kumar,et al.  Validating retinal fundus image analysis algorithms: issues and a proposal. , 2013, Investigative ophthalmology & visual science.

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Xu Sun,et al.  Feature Extraction from Optic Disc and Cup Boundary Lines in Fundus Images Based on ISNT Rule for Glaucoma Diagnosis , 2015 .

[11]  U. Rajendra Acharya,et al.  Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index , 2016, Comput. Biol. Medicine.

[12]  Marcelo Gattass,et al.  Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images , 2004, Pattern Analysis and Applications.

[13]  Flávio H. D. Araújo,et al.  Automatic Detection of Glaucoma Using Disc Optic Segmentation and Feature Extraction , 2015, 2015 Latin American Computing Conference (CLEI).

[14]  Kevin Noronha,et al.  Decision support system for the glaucoma using Gabor transformation , 2015, Biomed. Signal Process. Control..

[15]  Anselmo Cardoso de Paiva,et al.  Computational methodology for automatic detection of strabismus in digital images through Hirschberg test , 2012, Comput. Biol. Medicine.

[16]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[17]  M. Greenwood.  An Introduction to Medical Statistics , 1932, Nature.

[18]  U. Rajendra Acharya,et al.  Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features , 2012, Knowl. Based Syst..

[19]  Syed Muhammad Anwar,et al.  Autonomous Glaucoma detection from fundus image using cup to disc ratio and hybrid features , 2015, 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[20]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

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