AUTOMATED DIAGNOSIS OF GLAUCOMA USING EMPIRICAL WAVELET TRANSFORM AND CORRENTROPY FEATURES EXTRACTED FROM FUNDUS IMAGES

AUTOMATED DIAGNOSIS OF GLAUCOMA USING EMPIRICAL WAVELET TRANSFORM AND CORRENTROPY FEATURES EXTRACTED FROM FUNDUS IMAGES

AUTOMATED DIAGNOSIS OF GLAUCOMA USING EMPIRICAL WAVELET TRANSFORM AND CORRENTROPY FEATURES EXTRACTED FROM FUNDUS IMAGES. Glaucoma is the second leading cause of blindness worldwide. The available scanning methods are Heidelberg Retinal Tomography (HRT), Scanning Laser Polarimetry (SLP) and Optical Coherence Tomography (OCT). These methods are expensive and require experienced clinicians to use them.

Minimum distance, random forest, and naïve Bayesian (NB) classifiers for classification.Diagnosis of glaucoma is mainly based on the Intra Ocular Pressure (IOP), medical history of patient’s family [4], and change in optic disc structure . Glaucoma suspect will have IOP more than 21 mmHg. Other methods of monitoring glaucoma involve Optical Nerve Hypoplasia Stereo Photographs (ONHSPs). Advanced imaging technology such as Optical Coherence Tomography (OCT), Scanning Laser Polarimetry (SLP). Confocal Scanning Laser Ophthalmoscopy (CSLO) to generate reference images to study the eye and its internal structure.

Glaucoma is a diffuse disease: it is the first cause of unemendable visual disability and blindness worldwide and a recent epidemiological review concludes that 1 in 40 adults over 40 years of age suffers from glaucoma with visual loss.

 

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AUTOMATED DIAGNOSIS OF GLAUCOMA USING EMPIRICAL WAVELET TRANSFORM AND CORRENTROPY FEATURES EXTRACTED FROM FUNDUS IMAGES

Approximately 120 000 are blind from glaucoma, thus accounting for 9%–12% of all cases of blindness in the U.S. About 2% of the population between 40–50 years old and 8% over 70 years old has elevated IOP, which increases their risk of significant vision loss and even blindness..

 

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