Automatic Detection of Age-related Macular Degeneration from Retinal Images

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1 Automatic Detection of Age-related Macular Degeneration from Retinal Images 1 R. Manjula Sri, 2 Ch.Madhubabu, 3 K.M.M.Rao 1,2 Department of EIE, VNR Vignana Jyothi IET, Hyderabad, India. 3 Department of EEE, BITS-Pilani-Hyderabad campus, Hyderabad, India. 1 rmanjulasri@gmail.com, 2 madhu.ch17@gmail.com, 3 kundammrao@gmail.com Abstract Macular degeneration is one of the most common retinal diseases in India. Usually it is observed in people over 60s and is therefore called age related macular degeneration, ARMD or AMD. The macula is responsible for the sharp central vision needed for detailed activities such as reading, writing, driving, face recognition and ability to see colours. Age related macula degeneration is degeneration of the macula area and the delicate cells of the macula become inactive and stop working. Unfortunately, age-related macula degeneration cannot be completely cured, but if diagnosed at an early stage degeneration laser treatment can help some people to prevent further deterioration of macula. The authors have developed a novel algorithm for automatic detection of AMD using image processing techniques. The algorithm locates disease affected pixels on macula and evaluates the degenerated area on the macula. Lab View software is used to implement the algorithm. After pre-processing particle analysis tool is applied to locate and measure the area of the degenerated region in macula. Keywords Age related macular degeneration, morphology, green plane extraction, particle analysis technique. I. INTRODUCTION Macular degeneration is one of the most common retinal diseases in India. Usually it is observed in people over 60s and is therefore called age related Macular degeneration, ARMD or AMD. The layer of light sensitive tissue at the back of the eye is called the retina. The macula is the central part of the retina where the incident rays of light are focused. The macula is responsible for the sharp central vision needed for detailed activities such as reading, writing, driving, face recognition and ability to see and differentiate colours. Age related macula degeneration is the degeneration of the macular area. Sometimes the delicate cells of the macula become inactive and stop sensing. Images of normal and AMD macula are shown in Figure 1 (a) and (b). With many people the visual cells simply cease to function. In the early stages the central vision may be blurred or distorted, with things appear with unusual size or shape. This may happen quickly or may develop over several months and may cause some discomfort occasionally, but otherwise macula degeneration is not painful. The macula enables us to see finer details and people with the advanced AMD will often notice a blank patch or dark spot in the centre of their sight as in Figure 1 (c) and (d). This makes the activities like reading, writing and recognising small objects or faces very difficult. AMD appears in two forms dry AMD and wet AMD. Dry form is the most usually observed AMD and is less severe. In dry AMD changes occur in the pigmented cells of the macula. Also yellowish deposits called drusens appear on macula. Wet AMD is an advanced form of AMD, which results in abnormal blood vessel bleeding. The increased blood accumulation may lift the macula and cause visual distortions. Unfortunately, age-related macula degeneration cannot be completely cured and there is no treatment available for those who experience a gradual deterioration of their sight. If the sight deteriorates rapidly, or sudden distortion of objects occurs, this can be due to excessive blood vessel growth under the retina. Laser treatment may then be possible and may prevent further deterioration. If AMD is diagnosed at early stage Disciform degeneration laser treatment can help some people to prevent further deterioration of macula. II. CHALLENGES AND SOLUTIONS An ophthalmologist is a medical practitioner that specializes in the identification and treatment of structure, function, and diseases of the human eye, during a clinical examination of the patient and uses this information to diagnose the patient. The most common procedure during an examination is retinal imaging. The retinal image shows the optic nerve, fovea, surrounding vessels, and the retinal layer. The ophthalmologist can then refer to this image for all future findings. But large number of people suffers from eye diseases in rural and semi urban areas in India as well as world over and a large dearth of ophthalmologists exists in these regions. Year after year the number of medical assistants is decreasing, while demand for healthcare is increasing. The tremendous improvement in the Medical imaging techniques and Image Processing techniques simplify the diagnosis of eye diseases, there by assisting ophthalmologists for easy diagnose of the diseases. The authors propose to indigenously develop a low cost hardware gadget loaded with this software, which automatically diagnose the disease. This can be operated by any trained technician who will communicate the same to an expert for advice. The system with internet and mobile enabled network connectivity can help patients in rural and semi urban areas to access well equipped sophisticated hospitals in cities. The work presented in this paper is design and simulation of an algorithm to detect AMD automatically. 887

2 Colour Retinal image Cropping Re-sampling (a) (b) Extracting green channel image Morphology Subtraction (c ) (d) Figure 1: (a) Normal Macula (b) Degenerated Macula (c) Normal vision (d) AMD vision The algorithm is tested with drive dataset as well as several clinical retinal images. III. RELATED WORK A large number of image processing techniques and algorithms have been published to detect AMD. Marryam Mubbhashar et.al, proposed an algorithm to detect macula from distance estimation of Optical disc centre and blood vessels [1]. OD centre is detected with Hough transform [2] and blood vessels are enhanced using 2D Gabor wavelet transform. Luca Giancardo et.al, proposed a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation [3]. P.Burlina et.al used a multi-resolution locally-adaptive scheme that identified both normal and anomalous regions within the retina by using a hybrid parametric/non-parametric characterization of the support of the probability distribution of normal retinal tissue in colour and intensity feature space [4]. Cemal Kose et.al, proposed a simple inverse segmentation method to exploit the homogeneity of healthy areas of the macula rather than unhealthy areas [5]. This method first extracts healthy areas of the macula by employing a simple region growing method. Then, blood vessels are also extracted and classified as healthy regions. In order to produce the final segmented image, the inverse image of the segmented image is generated as unhealthy region of the macula. Rapantzikos et.al, proposed a novel segmentation algorithm for the automatic detection and mapping of drusen in retina images for the diagnosis of AMD [6]. Meindert Niemeijer et.al, implemented a fast method to detect the position of the optic disc and the fovea in retinal images [7]. Sopharak et.al, implemented detection of OD based on entropy filter [8]. Rashid Jalal Qureshi et.al, Detect particles (Connected regions or groups of non-zero pixels) Measurement of number of particles, Area of pixels in the total number of particles. Figure 2: Flowchart for the proposed algorithm. implemented a combination of the above algorithms for the detection of OD and Macula [9]. IV. METHODOLOGY The proposed algorithm by the authors detects AMD by identifying damaged pixels in the macula. The algorithm detects groups of damaged pixels in the macula region and evaluates the total damaged area in the macula from the colour retinal images. The flow chart for the algorithm is shown in figure2. The original retina image is first preprocessed and then particle analysis is performed..during pre-processing the image is cropped and re-sampled to perform the image processing operations in the region of interest (ROI). The macula is dark pattern and the gray level variations in this region are higher than in any other part of the image. Hence a shade correction operator is used to remove the slow background variations. The green component of the image shows a good variation between macula and background. Hence green plane is extracted and a series of morphological opening operations are applied. The output image is subtracted from the green plane for the calculation of shade correction operator. To detect the degenerated region (dark pixels) particle analysis is performed [10]. NI Vision Particle analysis tool is used to detect connected regions or groups of pixels in an image and then to measure selected parameters of those regions. These regions are commonly referred to as particles. A particle is a contiguous region of nonzero pixels. These particles are extracted from a gray scale image by thresholding the image into background and foreground states. Zero valued pixels are in the background state, and all nonzero valued pixels are in the foreground. Particle analysis consists of a series of processing operations and analysis functions that yield 888

3 information about particles in an image. In addition to making conventional pixel measurements, NI Vision particle analysis functions use calibration information attached to an image to make measurements in calibrated real-world units. To make shape measurements particle measurements tool is used on particles in a binary image. In addition to make conventional pixel measurements, NI Vision particle analysis functions use calibration information attached to an image to make measurements in calibrated real-world units. In applications that do not require the display of corrected images, the calibration information attached to the image is used to make realworld measurements directly without using timeconsuming image correction. In pixel measurements, a pixel is considered to have an area of one square unit, located entirely at the centre of the pixel. In calibrated measurements, the pixel is a polygon with corners defined as plus or minus one half a unit from the centre of the pixel. A pixel at (2, 5) is a square with corners at (1.5, 4.5), (2.5, 4.5), (2.5, 5.5) and (1.5, 5.5). To make real-world measurements, the four corner coordinates are transformed from pixel coordinates into real-world coordinates. Thus from the particle analysis tool, the total number of particles, total number of pixels in the particle and total particle area in sq. mm. is obtained. Based on the degenerated area of macula, early stage of AMD can be identified. V. IMPLEMENTATION The proposed algorithm is simulated in Lab VIEW 8.0.To employ particle analysis, first binary image is to be created. An enhanced binary image is created by subtracting from green plane of the RGB image, the resulting image after series of morphological opening operators on the green plane image. Then measurements are made on the image. Area of degenerated macula in sq.mm is evaluated. Figure 3 below shows the Block diagram of the Lab View program and Figure 4 shows its Front panel. In order to highlight the damaged area as bright, the output image is inverted. Figure 3: Block diagram of the labview program to detect AMD. Figure 4: Front panel of the Lab VIEW program to detect AMD. 889

4 VI RESULTS The algorithm is tested with 25 clinical retinal images of AMD patients and retinal images from DRIVE data set. The results are tested with ground truth. Table 1 shows the number of particles and the total degenerated area in square mm for seven AMD images and normal image. The original image (a) resultant images after cropping (b), green plane extraction(c), opening operation(d) and shade correction (subtraction of A and B)(e) during simulation are shown in figure 5. Table1: Number of particles and total degenerated area for the images in Figure 4. case Particle analysis (Number of Particles) Measured area (Total diseased area) (sq. mm.) Case1 0 0 Case Case Case Case Case Case Case VII. CONCLUSIONS The algorithm has been tested with DRIVE data set and clinical images. 90% of the images tested matched with the ground truth. The objective of developing this simple algorithm is to develop an embedded system which detects AMD automatically, and with network connectivity, any trained technician can communicate with expert for advice. [4] P. Burlina, D.E. Freund, B. Dupas, and N. Bressler, 2011, Automatic Screening of Age-Related Macular Degeneration and Retinal Abnormalities, proceedings of 33rd Annual International Conference of the IEEE EMBS,Boston, 2011,pp [5] C. Kose, U. Sevik, C. Ikibas and H. Erdol, 2008, Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images, Computers in Biology and Medicine 38 (2008) [6] K. Rapantzikos, M. Zervakis and K. Balas Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-relatedmaculardegeneration, 2003, Medical Image Analysis,Vol.7 (2003),pp [7] M. Niemeijer, M. D. Abràmoff and B. V. Ginneken Fast Detection Of The Optic Disc And Fovea In Color Fundus Photographs, 2009, Medical Image Analysis 13 (2009), pp [8] A. Sopharak, B. Uyyanonvara, S. Barman and T. H. Williamson, 2009, Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods, 2009, in Computerized Medical Imaging and Graphics,Vol. 32 (2009) pp [9] R. J. Qureshi et al. Combining algorithms for automatic detection of optic disc and macula in fundus images Computer Vision and Image Understanding,Vol. 116 (2012),pp [10] NI Vision Concepts Manual, National instruments, November ACKNOWLEDGEMENTS The authors thank doctors at Maxivision Eye Hospital and Padmavathy Netralaya, Hyderabad, for providing medical images and facilities for testing and for their valuable suggestions. Thank Dr.S.Rajaratnam for his valuable suggestions. Thank Principal, VNRVJIET- Hyderabad for encouraging the research work and providing the facilities. REFERENCES [1] M. Mubbashar, A. Usman and M. U. Akram, 2011, Automated System for Macula Detection in Digital Retinal Images, in the proceedings of 33rd Annual International Conference of the IEEE EMBS,Boston, pp-, 2011 [2] S. Sekhar,W. Al-Nuaimy and A. K. Nandi, 2008, Automated Localisation Of Retinal Optic Disk Using Hough Transform, 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp [3] L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y.Li, S. Garg, K. W. Tobin Jr and E. Chaum, 2012, Exudate- Based Diabetic Macular Edema Detection In Fundus Images Using Publicly Available Datasets Medical Image Analysis 16, 2012 pp

5 Case1 (Norma l) Case2 Case3 Case4 Case5 Case6 Case7 Case8 (a) (b) ( c) (d) (e) Figure 5: (a) original image,(b)cropped image, (c) Green plane of the image(a), (d)enhanced image(morphological open) (B) and (e)shade correction(subtraction) (A-B) 891