Thickness related textural properties of retinal nerve fiber layer in color fundus images

Odstrčilík, Jan; Kolář, Radim; Tornow, Ralf P.; Jan, Jirí; Budai, Attila; Mayer, Markus A.; Vodakova, Martina; Laemmer, Robert; Lamoš, Martin; Kuna, Zdenek; Gazárek, Jirí; Kuběna, Tomáš; Cernosek, P; Ronzhina, Marina

doi:10.1016/j.compmedimag.2014.05.005

Cited by 29 publications

(16 citation statements)

References 33 publications

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“…Among these parameters, mean and standard deviation are computed using intensity probability distribution (IPD), which is calculated from histogram of ROI. The detailed explanations of these parameters can be found elsewhere [1,2]. The equations for parameters used in this paper are given in Table-1.…”

Section: Algorithm For Box Counting Methodsmentioning

confidence: 99%

“…Later it was learnt that even in healthy retina, the variations of RNFL thickness anatomically was present which depended on the angular position around the ONH. These findings encouraged the authors to carry out analysis of RNFL variations through advanced image processing methods [2]. Hence we conclude that fundus camera images provide useful and easy-to-access information about RNFL which helps us to assess glaucoma through GRI or C/D ratio.…”

Section: Background Workmentioning

confidence: 99%

“…A linear classifier was used to classify the defected RNFL and normal object with a classification error of below 4%. [2]. Fractal descriptions can also be a measure to detect RNFL loss.…”

Section: Background Workmentioning

confidence: 99%

See 2 more Smart Citations

Early Detection of Glaucoma Through Retinal Nerve Fiber Layer Analysis Using Fractal Dimension and Texture Feature

Lamani¹,

Manjunath²,

Mahesh³

et al. 2014

IJRET

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Section: Algorithm For Box Counting Methodsmentioning

confidence: 99%

Section: Background Workmentioning

confidence: 99%

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Early Detection of Glaucoma Through Retinal Nerve Fiber Layer Analysis Using Fractal Dimension and Texture Feature

Lamani¹,

Manjunath²,

Mahesh³

et al. 2014

IJRET

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“…In 2007 Bock et al has been implemented feature extraction by combining the shape, texture and color features using intensity value of pixel, Gabor Filter, coefficient, Fourier Transform (FFT) and model histogram method [16], then in 2010 he modified his research by different method: intensity of line pixel, FFT and B-spline [17]. In order to extract the feature of the RNFL, the previous researches applied Gaussian Markov random fields and local binary pattern method [18], fractal dimension [19][20]. Kolar and Jan (2008) used simple box counting, maximum likelihood estimators and spectral-based method [19], while Kim et al (2013) used box-counting and multifractional Brownian motion [20].…”

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confidence: 99%

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

Septiarini

Hamdani

Khairina

2016

IJECE

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<p>Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%.</p>

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Section: Introductionmentioning

confidence: 99%

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

Septiarini¹,

Hamdani²,

Khairina³

2016

IJECE

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Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%. Keyword: Contour features Cup Fundus image Glaucoma Morphology Copyright © 2016 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Anindita Septiarini, Faculty of Computer Science and Information Technology, Universitas Mulawarman, Samarinda-75123, Indonesia. Email: anindita@unmul.ac.id INTRODUCTIONGlaucoma is an eye disease that can cause vision problems. This disease is the second cause of blindness in the world [1], therefore the detection of glaucoma is required. The detection of glaucoma by an expert carried out by observing the structure of the retina using a fundus image, to determine the eyes of the patient are normal and glaucoma. Currently, research on automatic glaucoma detection techniques have been developed. Most of the research using fundus images as input data and several feature extraction methods have been applied.Several previous researches related to automatic glaucoma detection generated by applying nonmorphological feature extraction method (image-based features). The feature referred to color, shape or texture on the optic nerve head (ONH), retinal nerve fiber layer (RNFL) or blood vessel as part of the structure of the retina [2]. These parts undergo changes in patients with glaucoma [3]. ONH (also called disc) is the part area with round shape. Inside of the disc, there is a smaller area with round shape called a cup and a neuroretinal rim which is an area that lies between the disc and the cup. RNFL is the area outside of the disk where the structure of thickness of the RNFL is the one which distinguishes whether a patient's eye is normal or glaucoma. The illustration of ONH and RNFL structure are shown in Figure 1.In the features extraction of the ONH applied the process of blood vessel inpainting as the previous phase in order to remove or blurring the blood vessel that presences as noise. This process is required the detection to determine the presence of the blood vessel. Several methods have been proposed for the detection of the blood vessel. Thresholding with the low pixel values in the image followed by median filtering of the size 41x41 [4], using threshold probing [5] and normalized mathematical morphology (used to enhance the vessel...

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Thickness related textural properties of retinal nerve fiber layer in color fundus images

Cited by 29 publications

References 33 publications

Early Detection of Glaucoma Through Retinal Nerve Fiber Layer Analysis Using Fractal Dimension and Texture Feature

Early Detection of Glaucoma Through Retinal Nerve Fiber Layer Analysis Using Fractal Dimension and Texture Feature

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

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