A Composite Architecture for an Automatic Detection of Optic Disc in Retinal Imaging

Transcription

1 A Composite Architecture for an Automatic Detection of Optic Disc in Retinal Imaging LEONARDA CARNIMEO, ANNA CINZIA BENEDETTO Dipartimento di Elettrotecnica ed Elettronica Politecnico di Bari Via E.Orabona, Bari ITALY carnimeo@poliba.it Abstract: - An automatic detection of the position of optic disc (OD) is an important step in a deep analysis of retinal images, due to the fact that information about OD can be used to examine the severity of some ophthalmic pathology via the extraction of a region of interest. Since errors caused by artifacts and bright fringes on retinal images could affect results in image processing, in this work a Composite Architecture for an accurate identification of the centre of OD is presented. More in detail, a Maximum Difference Evaluation Subsystem and a Maximum Gray Level Evaluation Subsystem are combined to analyze retinal images in parallel and find possible values of the centre of optic disc in different ways; locations of resulting pixels are then processed by an OD Centre Positioning Subsystem, which provides a reliable point to be adopted as the centre of the optic disc as effectively as possible. Simulation results are reported on selected fundus oculi images. Key-Words: - Retinal Image Analysis, Optic Disc Detection, System Architecture, Biomedical processing 1 Introduction Retinal imaging has rapidly developed during the last years and is now a mainstay of clinical care and management of patients with retinal diseases as well as systemic ones. Significant improvements in image processing for ophthalmology were introduced [1-10] to document both the health of human eyes and their biometric features [1]. Recent advances in automated diagnostic systems actually enable eye doctors to perform a large number of screening exams for frequent diseases, such as diabetes or glaucoma [1-3]. On this proposal, precise information about the Optic Disk (OD) reveals necessary to examine the severity of some diseases, since changes in the OD often indicate a pathologic progression [4-6]. In fundus imaging the OD is usually brighter than its surrounding area and reveals to be the convergence of the retinal blood vessel network. In fact, OD detection often bases on property of OD, such as high pixel intensity [7-11]. The OD centre is often searched on a floating window along the image, but the presence of bright areas of various cause cannot often be detected. Moreover, it is often needed to mask the OD out, when other bright lesions have to be detected (exudates, cotton-wool spots), because of their similarity in brightness and colour for a damaged retina [3]. Diagnostic systems of analysis are usually based on the detection of an optic disc Region of Interest (ROI), which is a subset of the image domain important for each retinal analysis [3]. In detail, a properly extracted ROI provides a smaller image containing the most of diagnostic information and reveals much less time-consuming when processed [3]. Thus, in retinal imaging the localization of the OD, in terms of position of its centre and radius, has to be as best as possible, being OD viewed as the main reference when analyzing every anatomic/pathologic retinal detail and the detection of its centre C as a key step in automatic extraction of retinal anatomic features [4], [7]. Unfortunately, drawbacks arise for the existence of different determinations of C depending on the evaluation procedure. Moreover, the presence of artifacts and bright fringes on original retinal images can cause significant mistakes. In [6] an interesting geometrical technique for fusing information collected about more candidate centres is presented, but this technique is not adequate in the case of a diseased retina. Taking into account all these considerations, in this work a Composite Architecture for an accurate ISBN:

2 localization of the centre of OD is presented. More in detail, a Maximum Difference Evaluation subsystem and a Maximum Gray Level Evaluation one are combined to analyze retinal images in parallel and find possible values of the centre of optic disc in more ways; then, an OD Centre Positioning subsystem contributes to provide the point to be assumed as the centre C of the optic disc as accurately as possible for successive retinal imaging steps. Simulation results are reported and discussed. 2 The Composite Architecture The optic disc appears as the brightest region in human fundus oculi images. However, also the main vessel crossings are located there. Thus, the maximum variation of gray level values usually occurs within the optic disc. In this work a Composite Architecture formed by more interacting subsystems is presented, with the aim of evaluating maximum gray level values among pixels in human retina in more ways. A Maximum Gray Difference Evaluation Subsystem and a Maximum Gray Level Evaluation Subsystem are therefore developed for analyzing images I g (i,j) of the green channel of RGB retinal images. These elements are both connected to an OD Centre Positioning Subsystem as shown in the block diagram reported in Fig.1. This architecture aims at obtaining one reliable value of the centre of the OD for each subsystem. Then, the OD Centre Positioning Subsystem is inserted to compare results and provide the centre C(i,j) as accurate as possible. 2.1 Maximum Gray Difference Evaluation Subsystem In the Maximum Difference Evaluation Subsystem the maximum difference between gray level values in each retinal image is computed along rows and along columns to select the corresponding pixel as the candidate centre in the Optic Disk. A block diagram of the behaviour of the whole subsystem is reported in Fig.2 and herein described. More in detail, in this system a proper median filter is firstly applied to the original image I g in order to remove non-significant values caused by acquisition noise, obtaining a filtered image I m (i,j), i=1,.., M, j=1,..,n. The research of the centre is performed on the whole considered image I m. Then, gray level values in I m (i,j) are focused in order to find the maximum difference values between gray levels both in each column and in each row. Fig.2: Block diagram of the Maximum Difference Evaluation Subsystem The behaviour of the Maximum Difference Evaluation block is detailed in the following figure. Fig.3: Detailed behaviour of the Maximum Difference Evaluation Subsystem Fig.1: Block Diagram of the Composite Architecture Each subsystem is herein separately described. The filtered image I m is firstly analyzed by considering each column c, thus I m (i, c) for i=1,..,m. The maximum gray level value for each column and the minimum gray level one are stored in a vector c max (j) and in a vector c min (j), respectively, for j =1, ISBN:

3 , N. In a similar way, the maximum gray level value for each row and the minimum gray level one are stored in a vector r max (i) and in a vector r min (i), respectively, for i =1,, M. Then, a vector d c (j) = c max (j) - c min (j) for j =1,, N is derived together with its maximum value d cmax. In an analogous way, every image I m is to be analyzed also by considering each row r, thus I m (r, j) for j=1,.:., N. The maximum gray level value for each row and the minimum gray level one have to be stored in a vector r max (i) and in a vector r min (i), respectively, for i =1,, M. Then, a vector d r (i) = r max (i) - r min (i) for i =1,, M has to be evaluated with its maximum value d rmax. The next step aims at determining the (i,j) coordinates of the centre C A. For this purpose, the j- th coordinate of each element of d c (j), which has its gray level value equal to d cmax, has to be stored in a vector x(k), k<n. Finally, the value i of the abscissa of the center C A (i,j) will be given by the average value i = k x( k) K k = 1,,K<N 2.2 Maximum Gray Level Pixel Subsystem Although the optic disc is usually the brightest area in a retinal image I g, it could happen that the pixel with the highest gray level value is not located inside the OD, such as in a diseased retina. The candidate pixel could be positioned in a noisy area of image I g, or it could be inside other small bright regions, if a retinopathy is present. In order to smooth out these distractors, in this subsystem each image I g (i,j) is FFT-transformed into the frequency domain as I gf (f 1,f 2 ) and then filtered by a Gaussian low-pass filter with transfer function H(f 1,f 2 ) analytically defined as H ( f 1 2 d ( f1, f, f 2 ) = exp 2 2 f 0 2 ) where (f 1,f 2 ) are coordinates in the frequency domain, d(f 1,f 2 ) is the euclidean distance between the point (f 1,f 2 ) and the origin of the frequency plane, and f 0 = 25 Hz is the cut-off frequency. The required centre C B (i,j) is the maximum gray-level pixel in the filtered image I gflp (f 1,f 2 ) after returning into the image spatial domain. In other words, the centre C B (i,j) is the pixel with maximum gray-level value v max in a low-pass filtered image I glp (i,j). The block diagram of the subsystem is shown in Fig. 5. as shown in the following figure. d cmax d rmax Pixel location columnmapping x(k) Abscissa Average Value i Pixel location row-mapping y(h) Ordinate Average Value j C A (i,j) Fig.4: Block diagram of Pixel Location Mapping In a similar way, the i-th coordinate of each element of d r (i), which has its gray level value equal to d rmax will be stored in a vector y(h), h<m. The value j of the ordinate of the center C A (i,j) will be given by the average value y( h) j h =1,,H<M H = h Summarizing, the candidate centre C A (i,j) in image I g (i,j) will be the pixel having coordinates given by the computed average values (i,j) in image I m. Fig.5: Block diagram of the Maximum Gray Evaluation Subsystem ISBN:

4 2.3 Centre Positioning Subsystem Two points C A (i,j) and C B (i,j) for every analyzed image in terms of two couples of coordinates, are provided to this subsystem by previous described ones. The centre C(i,j) is estimated as the average point between them given by the geometric coordinates taken inside each investigated retinal image. 3 Numerical Results The accuracy of the proposed architecture has been investigated on several retinal images, both healthy and diseased ones, belonging to different databases. For experimental reasons, the method has been tested on 80 retinal images. A number of (565x584)-sized images were taken from the publicly available DRIVE database [12], which consists of a total of 40 color fundus photographs used for making actual clinical diagnoses, where 33 photographs do not show any sign of diabetic retinopathy and 7 show signs of mild early diabetic retinopathy. Other 40 considered (768x576)-sized images are from a database collected by eye doctors of a local Ophthalmology Hospital. Both sets of images in the two databases contain bright symptoms of diabetic retinopathies, such as exudates or cotton wool spots. The detected location of the centre has been considered correct if falling within 60 pixels of a manually identified OD centre, as proposed in [13-15], where the center of the OD is identified as the point from which the main retinal vessels emerge. Performance of the Composite Architecture expressed in terms of detection success rate is reported in Table I. TABLE I: Performance of the Composite Architecture in Detection Success Rate Retina Database DRIVE Ophthalm. Hospital Tot Healthy Diseased All Detection Success rate (%) 97,5% 100% 98,75% For the sake of a better comprehension, the proposed method has been applied to a healthy retina taken by the free database named DRIVE. In this case, the previously described architecture give C A =C A (288,465) and C B =C B (288,443) as in Fig.6. Fig.6: A healthy retina with centres C A (i,j), C B (i,j) and the OD Centre Positioning Subsystem provides C(i,j)=C(288,454) as shown in Fig.7. Fig.7: Zoomed image of the OD Centre Positioning for the previously selected healthy retina The proposed method has been also applied to a deseased retina in the presence of exudates, as shown in Fig.8, where C A = C A (323,176) and C B =C B (311,182) Fig.8: A selected deseased retina with centres C A (i,j) and C B (i,j) In this case the Centre Positioning Subsystem provides C=C (317,179) as shown in Fig.9. Fig.9: Centre Positioning of C(i,j) ISBN:

5 4 Conclusion In this work a Composite Architecture for the automatic detection of the centre of OD in retinal imaging has been presented which involves different subsystems for guaranteeing correctness in determining OD centres. Computed pixels have been combined through a Centre Positioning Subsystem in order to obtain a point to be considered the centre of OD as accurately as possible. Obtained results reveal interesting and show that the proposed architecture can offer a reliable approach to reduce possible errors. References: [1] M.D.Abramoff, M.K.Garvin, M.Sonka, Retinal imaging and image analysis, IEEE Reviews in biomedical eng., Vol. 3, 2010, pp [2] V. Bevilacqua, L. Carnimeo, G. Mastronardi, V. Santarcangelo and R. Scaramuzzi, On the Comparison of NN-Based Architectures for Diabetic Damage Detection in Retinal Images, Journal of Circuits, Systems & Computers, Vol.18, No.8, 2009, pp [3] Z.Zhang, B.Lee, J.Liu, D.Wong, N.Tan, J.Lim, F.Yin, W.Huang, H.Li, T.Wong, Optic disc region of interest localization in fundus image for glaucoma detection in ARGALI, 5th IEEE Conf. on Industrial Electronics and appl. 2010, June 2010, pp [4] S.Sekhar, W.Al-Nuaimy, A.K.Nandi, Automated localization of retinal optic disk using Hough transform, 5 th IEEE Intern. Sym. on Biomedical Imaging: from Nano to macro 2008, May 2008, pp [5] L. Carnimeo, V. Bevilacqua, L. Cariello, G. Mastronardi, Retinal Vessel Extraction by a Combined Neural Network Wavelet Enhancement Method, Lect. Notes on Artificial Intelligence, Vol. 5755, 2009, pp , Springer Verlag. [6] B.Harangi, R. J.Qureshi, A.Csutak, T.Peto, A.Hajadu, Automatic detection of the optic disc using majority voting in a collection of optic disc detectors, 2010 IEEE Int. Symp. on Biom. Imaging from Nano to Macro, April 2010, pp [7] C.Sinthanayothin, J.F.Boyce, H.L.Cook, T.H.Williamson, Automated localization of the optic disc, fovea, and retinal blood vessel from digital colour fundus images, British J. of Ophthalmology 1999, No.83, pp [8] H.Li and O.Chutatape, Automatic location of optic disk in retinal images, Proc. of IEEE Int. Conf. on Image Processing, Vol.2, 2001, pp [9] H.Li and O.Chutatape, A model-based approach for automated feature extraction in fundus images, Proc. of IEEE Int. Conf. on Computer Vision, Vol.1, 2003, pp [10] A. Osareh, M.Mirmehdi, B.Thomas and R.Markham, Comparison of color spaces for optic disc localization in retinal images, Proc. of Int. Conf. on Pattern Recognition, Vol.1, 2002, pp [11] A.Mahfouz, A.Fahmy, Fast localization of the optic disc using projection of image features, IEEE Trans. on Image Processing, Vol.19, No.12, 2010, pp [12] M. Niemeijer, J.J. Staal, B. van Ginneken, M. Loog, M.D. Abramoff, DRIVE Retinal Database from Comparative study of retinal vessel segmentation methods on a new publicly available database, 2002, [13] G. A. Hoover and M. Goldbaum, Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels, IEEE Trans. Med. Imag., Vol.22, No.8, 2003, pp [14] M. Foracchia, E. Grisan, and A. Ruggeri, Detection of optic disc in retinal images by means of a geometrical model of vessel structure, IEEE Trans. Med. Imag., Vol.23, No.10, 2004, pp [15] A. Youssif, A. Ghalwash, and A. Ghoneim, Optic disc detection from normalized digital fundus images by means of a vessels direction matched filter, IEEE Trans. Med. Imag., Vol.27, No. 1, 2008, pp ISBN: