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Applying a Set of Gabor Filter to 2D-Retinal Fundus Image to Detect

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Applying a Set of Gabor Filter to 2D-Retinal Fundus Image to Detect OriginalOriginal Article Applying a Set of Gabor Filter to 2D-Retinal Fundus Image to Detect the Optic Nerve Head (ONH) Rached Belgacem1,2*, Hédi Trabelsi2, Ines Malek3, Imed Jabri1 1Higher National School of engineering of Tunis, ENSIT, Laboratory LATICE (Information Technology and Communication and Electrical Engineering LR11ESO4), University of Tunis EL Manar. Adress: ENSIT 5, Avenue Taha Hussein, B. P. : 56, Bab Menara, 1008 Tunis; 2University of Tunis El-Manar, Tunis with expertise in Mechanic, Optics, Biophysics, Conference Master ISTMT, Laboratory of Research in Biophysics and Medical Technologies LRBTM Higher Institute of Medical Technologies of Tunis ISTMT, University of Tunis El Manar Address: 9, Rue Docteur Zouheïr Safi – 1006; 3Faculty of Medicine of Tunis; Address: 15 Rue Djebel Lakhdhar. La Rabta. 1007, Tunis - Tunisia Corresponding author: Abstract Rached Belgacem, High Institute of Medical Technologies Background: The detection of edge of the cup (excavation), disc, and the optic nerves head of Tunis, ISTMT, and High National which converges into the center of the excavation in the papilla of the retina is a crucial School Engineering of Tunis, procedure in computer vision object detection processing and analysis. Purpose: We propose Information Technology and Communication Technology and a method and approach for cup and disc detection based on 2-D Gabor filter with the addition Electrical Engineering, University of of some constraints. This paper approves that edge magnitude and edge phase are useful for Tunis El-Manar, ENSIT 5, Avenue Taha extracting the disc and the cup and to overcome the lack contrast between the cup-disc in order Hussein, B. P.: 56, Bab Menara, 1008 Tunis, Tunisia, to discriminate one from the other and to extract the different optical vessels along several Tel: 9419010363; directions theta. These methods are based on space image and phase image after searching the E-mail: [email protected] favorable parameters (Scale, sigma, a & b, r ...) to properly adjust the results of the application of kernels Gabor on the fundus image of the retina to calculate the magnitude image and the phase image which each bring additional information on the excavation of the of the optic nerve head ONH to help to early glaucoma disease screening. Results: The proposed method was applied on several retinal images issued from a set of images of Tunisian glaucomatous database. A total of 60 images were used. For an input image, several features were determined, such as edge of the disc, edge of the cup, boundaries of the papilla and calculate magnitude image and phase’s image. Fitting sigma, scale, spatial frequency 0F and (a, b) parameters were adjusted to aggregate the Gaussian kernel function modulated by a sinusoidal plane wave, ωr (x, y) and C (x, y) a complex sinusoid, respectively. The Signal-to-Noise Ratio SNR and the mean square error MSE were used to identify the optimum fitting parameters instigated in the Gabor function and convolved with the retinal image to detect the magnitude and phase image over different orientations for the segmentation of cup and the disc and the optic nerve head ONH. Conclusions: 2D Gabor filters are analyzed and the apply a set of Gabor filter to 2D- fundus image of retina is efficacious to detect the optic nerve head ONH and the Gabor filter banks for texture classification of the papilla and pattern analysis of the cup and the Disc. The results given in this paper can be useful by an ophthalmologist to diagnosis a glaucomatous case. Segmentation of the cup and the disc through the bank Gabor filter proved functional for glaucomatous diagnosis and screening the pathology. Keywords: Excavation; Glaucoma; Segmentation; Optic Cup (OC) and Optic Disc (OD); Set of Gabor filter; Texture Classification and pattern analysis Introduction cells of human beings [4] and cats. [5-7] However, not all current psychophysical and physiological researchers agree with this Glaucoma is a disease that is asymptomatic until advanced model. [8] stages, in this paper we use Gabor filters for 2D-image analysis have been extensively used as convolution filters, [1,2] motivated 2D-Gabor filters were considered an important instrument for by search results in Biological vision systems [3] to detect this a variety of image pattern processing and recognition problems disease and screening it until total blindness. The 2D-Gabor (e.g., image enhancement, [9] Compression, [10] texture analysis, filters occupy an irreducible volume in a four-dimensional [11] edge and line detection, [12] biometrics Recognition, [13] object hyper-space of information whose axes can be interpreted as 2D [4] visual space, Orientation and spatial frequency. Even if the This is an open access article distributed under the terms of the Creative Commons properties of the 2D-Gabor filters (e.g. orientation selectivity Attribution‑NonCommercial‑ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non‑commercially, as long as the author is credited and the compromise between spatial and frequency resolutions) and the new creations are licensed under the identical terms. correspond to the first psychophysical and physiological How to Cite this Article: Belgacem R, et al. Applying a Set of Gabor Filter results, they were finally confirmed for the modeling of simple to 2D- Retinal Fundus Image to Detect the Optic Nerve Head (ONH). Ann receptive fields thanks to a series of 2D experiments on simple Med Health Sci Res. 2018; 8: 48-58 48 © 2018 Annals of Medical and Health Sciences Research Belgacem R, et al.: Applying a Set of Gabor Filter to 2D-Retinal Fundus Image to Detect the Optic Nerve Head (ONH) detection [14] and segmentation [15]. In order to maximize the others and analyze psychophysical masking data and find that in performance of the systems in terms of accuracy, researchers many cases receptive-field functions other than Gabor functions used optimization algorithms to adjust the Gabor parameters fit better. We conclude that there are insufficient theoretical [16] and to increase the filtering speed; they proposed orientable demonstrations and experimental data to favor Gabor functions Gabor type filters, [17] a Gabor wavelet [18] and a Gabor filtering over any of a number of other plausible receptive-field functions. system. [19] A novel peripheral processing method is proposed to segment To answer the current research, 2D Gabor filters are analyzed. total field strain distributions from interferometric deformation The results [Figure 1] given in this paper are the segmentation patterns by use of Gabor filters. This novel strategy is and extraction of patterns: specifically proposed for strain measurement with a Gabor filter used as a set of wavelets. [23] To increase computational speed as well as for selection of contour intervals, judicious design of the filter bank, based on the border pattern and the necessities of the user, is crucial in this methodology. A filter design strategy is developed and, based on the proposed filter design scheme; properly designed filter banks are generated and applied for strain contouring in low-strain and strain concentration regions. This scheme allows one to measure engineering strains within regions of interest and hence provides the design engineer great flexibility of monitoring, testing, or analysis. [24] Figure 1: (a) The real part and the imaginary part of a Gabor filter. (b) Texture segmentation involves subdividing an image into The real and imaginary parts of a complex sinusoid. (c) The response differently textured regions. Many texture segmentation of the convolution of the retinal image and the real imaginary parts of a complex Gabor function in space domain (Gabor magnitude response). schemes are based on a filter-bank model, where the filters, (d) Gabor phase responses. The images are ‹1000 × 1054 × 3 unit 8 › called Gabor filters, are derived from Gabor elementary pixels. The parameters are as follows: x0=y0=0, a=1/50 pixels, b= 1/40 functions. The aim is to transform texture differences into pixels, θ=(-π/8; π/8), F =√2/80 cycles/pixel, ω =45 degrees, φ=0 degree. 0 0 detectable filter-output discontinuities at texture boundaries. Papilla, disc, cup and vessel’s detection are along several By locating these discontinuities, one can segment the image orientations. A 2D Gabor filter in a spatial domain is defined into differently textured regions. Distinct discontinuities bellow and the results of the convolution of the Gabor filter occur, however, only if the Gabor filter parameters are suitably bank with the input imaging shows its robustness of segmenting chosen. Some previous analysis has shown how to design filters the papilla and separating the cup from the disc thus visualizing for discriminating simple textures. Manipulative filters by the the optical nerves (vessels) that converge towards the head optimum parameters; for more general natural textures, though, of the optic nerve following different orientations theta [20] to has largely been done. We have devised a more rigorously based distinguish those that blow from necrosis to increase intraocular method for designing Gabor filters by the fitting parameters who controlled the convolution of the kernel Gabor filters with pression IOP and which is a factor generates glaucoma and its the retinal image to obtain finest texture describe all schemes early screening using machine learning can help avoid total (vessels, papilla, disc, cup and the optic nerve head ONH) blindness before this pathology reaches an advanced stage to confined in this regions. It assumes that an image contains two reduce glaucoma severity and vision loss. different textures and that prototype samples of the textures Segmentation of the optic nerve head are given a priori. We claim that Gabor filter outputs can be modeled as Rician random variables [25] and develop a decision- Several recent theoretical models for human spatial vision theoretic algorithm for selecting optimal filter parameters.
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