A Simple and Fast Algorithm for the Automatic Localization of Optic Disc in Digital Fundus Retinal Images S.Balasubramanian, Anantha Vidya Sagar, V.Chandrasekaran, SMIEEE
Abstract— Locating the Optic Disc (OD) is of critical importance in a retinal analysis system. This paper presents a fast algorithm for the automatic localization of OD in digital fundus retinal images. OD localization follows the Blood Vessel Detection (BVD). A morphology based approach is proposed for BVD. OD is localized by exploiting the property that it is a bright region and the density of blood vessels is high in this region. The algorithm has been tested on 100 normal and 480 diseased retinal images achieving success rates 100% and 93.75% with average execution times of 9 seconds and 17 seconds respectively, thereby demonstrating the speed, robustness and accuracy of the proposed method. Index Terms— Optic Disc, Blood Vessel Detection, Diabetic Retinopathy.
I. INTRODUCTION Diabetic Retinopathy (DR) is a common ailment and the leading cause of blindness in the working age population. The loss of sight due to DR can be moderated if it is detected early enough for treatment. Digital fundus retinal images are widely used for the diagnosis of DR. Ability to automatically and quickly process a large number of retinal images helps ophthalmologists in improving their productivity and efficiency. Optic Disc (OD) is the entrance and exit region of blood vessels and optic nerves to the retina and its localization and segmentation is an important task in an automated retinal imaging system. OD localization is required as a prerequisite for the identification of anatomical structures and pathologies in retinal images. Localizing the OD assumes utmost importance in the automatic detection of lesions of DR since the OD has characteristics similar to exudates thereby being S. Balasubramanian is with the Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Prashanthinilayam, Puttaparthy, India. (e-mail:
[email protected] ). Anantha Vidya Sagar is with the Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Prashanthinilayam,Puttaparthy,India. (e-mail:
[email protected]). V.Chandrasekaran is with the Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Prashanthinilayam,Puttaparthy,India. (e-mail:
[email protected]).
IMECS 2007
more vulnerable to get targeted as a false positive. Hence the main focus of this endeavor is to propose a novel technique that will automatically detect the location of OD. Edge detection followed by circular Hough transform is the method of localization of OD in [1]. In [2] OD is located by finding the area with highest variation of intensity of adjacent pixels. Pyramidal decomposition followed by Hausforff-based template matching is used for detecting the OD in [3]. OD localization and segmentation is performed in [4] using morphological filtering techniques and watershed transformation. These methods perform well on normal retinal images where OD is the brightest. However, they often fail on pathological images, where bright lesions similar to the OD are present. Also, these techniques have not been tested on a large database of images. The minimum distance between the original retinal image and its projection on to the principle component basis images identifies the centre of OD in [5] and [6]. While this approach reports a good success rate, it is time consuming and has been tested only on 89 images. Foracchia et al. [7] build a geometric model of blood vessels and localize the OD by simulated annealing, which is again a highly time consuming operation. In [8] a fuzzy convergence algorithm is proposed to locate the OD as the focal point of the blood vessel network. In this paper, we propose a simple and fast algorithm for the automatic detection of OD. Our approach exploits the following two properties: 1. OD is a bright region. 2. The density of blood vessels is very high in the OD region. II.
PROPOSED METHOD
The proposed method is carried out in the following stages Detection of bright regions. Preprocessing: Fast Gray Level Grouping (FGLG) applied on the green channel of the image. BVD using Morphology. OD localization. Figure 1 shows the schematic diagram of the proposed method. A. Detection of Bright Regions
1965
It is noted that the intensity of OD is much higher than the surrounding retinal background. Therefore a common method to localize the OD is to find the largest cluster of pixels with the highest gray level. This will not always succeed because the manifestations of DR in the form of exudates have brightness similar to OD. Hence, firstly we determine all the bright regions in the image. The green channel of the RGB image is chosen for this purpose as the contrast of the image maintained well in this channel. Smoothing operation is performed to remove noise. Then large size median filtering is applied to the smoothed image. Subtracting the median filtered image from the green channel of the original image highlights the bright regions. This difference image is thresholded to obtain the bright regions. Figure 2 shows result on a sample image.
(a)
(b)
Retinal Image
Detection of Bright Regions
FGLG on Green Component
(c)
(d)
Fig. 2. Detection of bright regions. (a) the green channel of the test image (b) median filtered image (c) difference map of the images in (a) and (b). (d) Detection of bright regions.
The basic procedure for Gray Level Grouping is as follows. Blood Vessel Detection
OD localization
Fig. 1. Schematic diagram of the proposed method.
B. Gray Level Grouping for Contrast Enhancement There is degradation in quality of contrast as we move from the centre towards the boundary of fundus image. Also, Blood vessels usually have poor local contrast. So, a preprocessing method is applied for contrast enhancement. The green channel of the RGB space is chosen for this purpose because blood vessels appear most contrasted in this channel. Conventionally, methods such as histogram equalization, adaptive histogram equalization and histogram specification are used for contrast enhancement. For our purpose, we make use of Gray Level Grouping (GLG) [9] as it is an automatic method unlike histogram specification, and shows improved performance over existing methods.
Firstly, the histogram components of the input image are grouped in to a proper number of gray level bins according to their amplitudes. These groups of histogram components are redistributed over the grayscale, so that each group occupies a grayscale segment of the same size as the other groups, and the concentrated histogram components spread out and image contrast is increased. Ungrouping the previously grouped gray-levels and mapping the gray-level values of the pixels in the input image to the desired values in the output image is performed. A variation of GLG called Fast Gray Level Grouping (FGLG) [9] has been applied on the images. Figure 3 shows a sample of results.
(a)
(b)
Fig. 3. FGLG for Contrast Enhancement: (a) green channel of a test image. (b) image after Contrast Enhancement.
1966
IMECS 2007
misclassified as blood vessels are removed using connected component labeling choosing size as criteria. Figure 5 shows a sample of results of our approach.
C. Blood Vessel Detection (BVD). Our method for BVD is based on morphology and is applied on the contrast enhanced images. Some basic morphological operations [4] are defined below. 2
Let D f ⊆ Z and T= { t min ,…, t max } be an ordered set of gray levels. A gray level image f can be defined as a 2 function f : D f ⊆ Z → T= { t min ,…, t max }. Let B be a 2 subset of Z and s ∈ N a scaling factor, the morphological operations are defined as follows. Erosion: [ε
( sB )
Dilation: [δ Opening:
γ
( f )]( x) = min b∈sB f ( x + b).
( sB )
D. OD Localization. Since the diameter of the OD is in the range of 65-100 pixels in the retinal image of size 565x584, our aim in this step is to find a square of size 110x110 pixels that contains the OD. We work on the green channel of the RGB image for our purpose. Figure 6 shows the green channel of two retinal images. As it can be observed the OD is a bright region with a high density of blood vessels. There are other regions such as exudates which are also bright, but the density of blood vessels in these regions is low. We exploit this property for OD localization.
( f )]( x) = max b∈sB f ( x + b).
( sB )
( f ) = δ ( sB ) [ε ( sB ) ( f )]. φ ( sB ) ( f ) = ε ( sB ) [δ ( sB ) ( f )].
Closing: Our method for blood vessel detection is as follows. Let g represent the green channel of the test image after contrast enhancement. Firstly, blood vessels are eliminated by a closing operation. A square structuring element s1 B is used for the purpose. s1 is chosen such that s1 B is larger than the maximal width of blood vessels.
h1 = φ ( s1B ) ( g ).
(1)
(a)
(b)
Where h1 is the closed image. Figure 4 shows a sample result of closing operation.
(c)
(d)
Fig.5. Results of BVD: (a) test image1 (b) Result of BVD on test image 1. (c) test image 2 (d) Result of BVD on test image 2
Fig.4. Results of Closing: (a) Input image (b) Result of morphological closing.
Then image subtraction is performed, where the image g is subtracted from the closed image h1 .
h2 = h1 − g . (2) As blood vessels are relatively darker compared to the background, the values at locations of vessel pixels in h2 will be higher compared to the values at locations of non-vessel pixels. Hence thresholding h2 at level t1 , where t1 is chosen appropriately gives rise to BVD. Post processing is performed for reduction of noise. The noise caused by bright regions whose size matches that of blood vessels is removed using the result of section II A. Small isolated regions of pixels
IMECS 2007
(a)
(b)
Fig.6 Test Images. (a) Test image 1 (b) Test image 2.
Firstly, candidate regions for OD localization are determined. The bright pixels found during section II.A are clustered and clusters which contain more than 20 pixels are considered as
1967
candidate regions. Then, for each bright pixel which belongs to a candidate region a 110x110 square with the pixel as centre is considered and the number of blood vessel pixels in the square is counted. The result of BVD from section II.C is used for this purpose. Let the original image be denoted by A. Then a count matrix C whose size equals that of A is created such that: 1. For all non candidate pixels C (i, j) equals zero. 2. For all candidate pixels C (i, j) equals the number of blood vessel pixels in the 110x110 square with A (i, j) as the centre. Since the density of blood vessel pixels is high in the OD region, it is observed that the count C (i, j) is large for all the bright pixels in the OD. We propose that the 110x110 square S for which the sum of C (i, j)’s of pixels is maximum contains the OD. Our method is justified as follows. In case of normal images OD is the only bright region, which justifies the approach. In case of diseased images bright lesions such as exudates also get detected as candidate regions. As can be observed in figures 6 and 7 the density of blood vessels in these lesions is less compared to that of OD region. Because blood vessels converge at the OD, the bright pixels in the OD are divided in to number of clusters rather than a single cluster. This can be noticed in figure 2. It can be noted that the count C(i,j) is large for each pixel in the OD region as compared to pixels in other areas of the image and all the clusters of OD region can fit in to a square of size 110x110. Therefore the proposed approach leads to OD localization even on diseased images.
III. EXPERIMENTAL SETUP, RESULTS AND DISCUSSION The proposed algorithm is implemented in MATLAB (version 7) on a P4 machine (3.00 GHz). Our database included 100 normal and 480 diseased images of resolution 565x584. For the detection of bright regions we used a median filter of size 100x100, and the threshold is set at 40. Using FGLG the original gray level bins are grouped in to default number of bins 100. For morphological closing a square structuring element of size 11 is chosen. The threshold t1 is set to 20. We tested our algorithm on 100 normal images, achieving 100% success rate, with average run time of 9 sec. When tested on 480 diseased images our algorithm achieved a success rate of 93.75%, with an average run time of 17 seconds. Figure 7 shows results of our algorithm on some images. The proposed algorithm is simple yet robust, reliable and fast. The fuzzy convergence approach to OD localization [8] reports only 80% success rate on diseased retinal images. The PCA based method in [5] has been tested on the same diseased images and it achieves only success rate of 60%. Also, the average execution time of this algorithm is 8 minutes compared
1968
to 17 seconds for our algorithm. We chose a large database of 580 images for testing to prove the robustness of the algorithm. Figure 8 shows images on which our method failed to detect the OD. The proposed algorithm failed in these images because OD is not bright in these images. Presence of illumination imbalance could pose problems for OD localization as can be seen in figure 8(b). But with the advancement of technology and availability of superior quality fundus cameras, without any loss in generality we may assume that such images may not occur in practical situations.
IV. CONCLUSION A simple and fast algorithm for the automatic localization of OD has been proposed in this paper. This algorithm has been demonstrated to be fast, robust and reliable with (100% on normal images with execution time of 9 sec and 93.75% on diseased images with execution time of 17 sec) improved performance over the previously reported techniques. This method contributed to our overall goal of developing a system for the automated screening of DR in medical camps. Our future work will focus on the prediction of DR in patients based on the changes in the caliber of blood vessels.
(a)
(b)
(c)
(d)
(e)
(f)
IMECS 2007
[6]
(g)
(h)
Fig.7. Results of OD localization. The black square represents the localized OD. (a)-(b) results on normal images (c)-(h) results on diseased images.
(a)
H. Li, O.Chutatape. “Automated feature extraction in color retinal images by a model based approach,” IEEE Trans. Biomedical engineering, vol. 51, No. 2, Feb. 2004. [7] Foracchia M., Grisan E. and Ruggeri A., “Detection of optic disc in retinal images by means of a geometric model of vessel structure,” IEEE Trans. Med. Imag., vol. 23, no.10 pp.1189-1194. 2004. [8] Hoover A. and Goldbaum M., “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Trans. Med. Imag., vol. 22, pp.951-958, 2003. [9] Zhi Yu Chin, Besma R. Abidi, David L.Page, Mongi A. Abidi, “ Gray-Level Grouping (GLG) : An Automatic Method for Optimized Image Contrast Enhancement – Part I : The Basic Method,” IEEE Trans. Image Process., vol. 15, no.8, pp. 2290-2302, Aug. 2006. [10] http://cmm.ensmp.fr/~walter/
(b)
Fig.8. Images on which our method failed to detect OD.
ACKNOWLEDGMENT We dedicate this work to Our Chancellor Bhagawan Sri Sathya Sai Baba. We acknowledge the database support for DRIVE+ database www.isi.uu.nl/Research/Databases/DRIVE, which was part of our test database. We also acknowledge the use of images provided by [10]. Our thanks are due to SSSIHMS( Puttaparthi,INDIA), Dr.Rema Mohan (MVDSC,Chennai, INDIA), Dr.Rajiv Raman (Shankara Netralaya,Chennai, INDIA), Dr.Puvana Chandra(UK), Prof. Panchanathan (ASU, USA),Dr. Bhujanga Shetty and Dr.VenkataSubramanian from Narayana Netralaya, Bangalore,INDIA for providing us images and valuable information on DR. REFERENCES [1]
[2]
[3]
[4]
[5]
Z. Liu, O. Chutatape and S. M. Krishnan, “Automatic image analysis of fundus photograph”, Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2, 1997, pp. 524-525. C. Sinthanayothin, J. Boyce, H. Cook, T.Williamson, “Automatic localization of optic disc, fovea, and retinal blood vessel from digital colour fundus images,” Br. J. Opthalmol., 1999, 83, pp.902-910. L. Gagnon, M. Lalonde, M. Beaulieu, M.-C. Boucher, “Procedure to detect anatomical structures in optical fundus images”, Proc. SPIE Med. Imaging: Image Processing, vol.. 4322, pp.1218-1225, 2001. T. Walter, J. C. Klein, P.Massin, and A.Erginay, “A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy –Detection of Exudates in Color Fundus Images of the Human Retina,” IEEE Trans. Med. Imag., vol.21, no. 7, pp. 1010-1019, Jul.2001. Li Huiqi and C.Opas, “Automatic location of optic disc in retinal images,” In Proceedings of the International Conference on Image Processing, pages: 837-840, 2001.
IMECS 2007
1969
