Hybrid Gabor Filter Bank with Trilateral Based Filtering Image Segmentation Technique for Exudate Extraction

Image segmentation is the technique of dividing an electronic digital image into a number of sub-images. The aim is always to simplify and/or change the representation of a graphic into something more meaningful and easier to analyze. The majorreason behind blindness and vision problems in working people is a serious public health issue. Identifying diabetic retinopathy lesions in retinal images can help in diagnosing a problem early. Lack of accuracy in these techniques can lead to fatal results because of incorrect treatment. So, there is a great need for automation techniques with high accuracy for retinal disease identification. Several automation techniques have been proposed for retinal image analysis. This paper proposes new unsupervised technique which gives improved results but it is suffers from the problem of the noise. So to remove this problem a hybrid Gabor filter bank with trilateral based filtering technique is proposed. Keywords— Image segmentation, Exudates segmentation, Gabor filter, Fundus image INTRODUCTION Image processing aims at performing some operations on the image to get an improved image with an aim to extract useful information from the image. Image processing is a kind of signal processing in which takes the image as input and produces some characteristics of the image as the output. These days, image processing is a widely growing technology and is one of the main areas of research. Image processing basically includes the following three steps: 1. Using image acquisition tools to import the image 2. Analysis and manipulation of the image. 3. Altering or modifying the Output Basically, two approaches, analogue and digital image processing are majorly used for image processing. Analogue technique is generally used with hard copies of the image like printouts and photographs. Digital image processing deals with manipulating digital images with help of a computer. The data undergoes three steps, namely pre-processing, image enhancement, and information extraction. Fig 1: Image processing[9] A digital image is a way to represent a 2-D image in form of image pixels as a finite set of digital values. The values of pixels depict characteristics such as gray levels, colors, heights, opacities etc. Digital Image Processing is the technique of manipulation of digital binary images with the help of a digital computer. DIP deals with the computer-based processing of digital images. The input to the image processing system is a binary image which is processed using efficient algorithms, and output is in form of a processed image. Adobe Photoshop is a commonly used application for processing digital images. Main tasks of Digital image processing are1. Improving the images for better human understanding. Paper Title:Hybrid Gabor Filter Bank with Trilateral Based Filtering Image Segmentation Technique for Exudate Extraction ISSN:-2349-3585 |www.ijrdt. org 153 2. Processing the digital images for the use of transmission, storage and representation for autonomous machine perception. 1. SEGMENTATION PROCESS Image segmentation is a technique which divides a binary image into numerous sub-images which is a group of pixels. These pixels have similarity depending upon some homogeneity criterion like color, intensity and texture of the image. This is helpful in identifying the object of interest from the images. In good segmentation approach:1. Pixels having similar grayscale value and forming a connected region belong to the same category. 2. Pixels belonging to different categories and having dissimilar grayscale values are neighboring pixels. The choice Image segmentation techniques dependent on following:1. Detecting DiscontinuitiesThis approach divides an image into sub-images depending upon an abrupt change in the intensity value. Example: edge detection approach. 2. Detecting SimilaritiesThis approach divides an image into sub-images depending upon some predefined similarity criterion. Example: thresholding, Region growing, Region splitting and merging approaches. Fig 2: Segmentation Techniques 2. GABOR FILTER Dennis Gabor proposed the Gabor filtering technique which was later on used by Daugman to model the response of cells in the visual cortex of some mammals. Gabor filter is a linear filtering technique which is widely used for edge detection. Gabor filters have an advantage of having frequency and orientation representations identical to the human visual system, and so it is very helpful in segmenting retinal vessel structures. Gabor filters are basically bandpass filters and are mainly used in feature extraction and texture analysis for processing images. This filtering technique makes use of multi-scale Gabor filtercharacterized by one free parameter that determines the scale to identify the pixels called keypoints using the scale invariant feature transform (SIFT). Filterresponse vectors derived from keypoints are used to initialize a clustering algorithm which generates textonswhich are eventually used for the classification of vessel pixels or nonvessel pixels in the input images. Then a process called model selection is used to identify keypoints from a small training set (N = 10) of retinal images. Then using these keypoints, a textondictionary is built. In this second training stage, a k-NN clustering algorithm is initialized with seeds found by matching keypoints identified by the model selection stage to those in the training set Leung and Malik characterize textons by its response to a filter bank (F1 ,F2 ,. . .,Fn). R = F1× I (x, y) , F2× I (x, y) , . , Fn×(x, y) If the filter bank convolved with an input image I(x,y) the response at each pixel will depend on specific local structures and on the design of the filter bank. 3. RELATED WORK Zhang et al. [2015] [6] presented an algorithm for segmenting retinal vessel structures which makes use of a texton dictionary to categorize vessel pixels or non-vessel pixels. This algorithm also uses Key points which are a small set of image features to derive the parameters for filtering. A Gabor filter bank presented in this paper has main aim of extracting key points from an image which represent significant information regarding vessel features with the help of SIFT algorithm. Pereira et al. [2015] [7] segmented exudates from the retinal images on the basis of the ant colony optimization technique. The efficiency of the method was evaluated with an online database and the experimental results proved that the technique is better in performance as compared to traditional Kirsch filter for exudates detection from the retinal images. Arraign et al. [2014] [8] presented a technique to identify glaucoma by measuring the displacement of blood vessels in the OD (optic disc) in fundus images due to increase in size of the excavation or cup. This technique deals with segmenting the area of the vascular bundle in an optic disc to set a reference point in the temporal side of the cup. Roychowdhury Paper Title:Hybrid Gabor Filter Bank with Trilateral Based Filtering Image Segmentation Technique for Exudate Extraction ISSN:-2349-3585 |www.ijrdt. org 154 et al. [2014] [9] presented a new three-step vessel segmentation technique using fundus images. Initially, high-pass filtering is used to extract a binary image by preprocessing the green plane of fundus images, and the other binary image is extracted from the reconstructed enhanced image.Akram et al. [2013] [10] gave a methodology to accurately detect drusen in the fundus images. This method makes use of a filter which extracts all possible drusen regions from fundus images and also removes false pixels from the fundus image which may appear due to the resemblance of drusen with optic disc. This methodology represents each area using several characteristics and then classifies areas as drusen region and non-drusen region by applying support vector machine.Bhuiyan et al. [2013] [11] have discussed that the Age-related macular degeneration (AMD) is the main reasons for vision defect among the aged people and identification of persons with first stages of AMD is vital while considering the preventative strategies for late AMD. They proposed a technique for drusen detection from standard color retinal images which enables quick, accurate and automated method to determine characteristics of drusen. Kambojet al. [2013] [12] explained that enhancing a corrupted image by removing noise is an important step in digital image processing for better perception. Various filters are used for removing various types of the noise from the images. Numerous noise models and filtering algorithms are explained. Odstrcilik et al. [2013] [13] improved the concept of matched filtering, and proposed a technique for segmenting retinal vessels from the fundus images. This proposed method aims to segment blood vessels which have different vessel from color retinal images.Krishnan et al. [2012] [14] used Attanassov intuitionist fuzzy histon (A-IFSH) based segmentation technique to propose an algorithm to extract the optic disc from digital retinal images. To find and locate the optic disc region, Optic disc pixel intensity and column wise neighborhood techniques are used in this paper. The strategy was tested on 100 images containing of 30 normal images, 39 images with glaucoma and 31 DR images.Rozlan et al. [2012] [15] developed a Graphical User Interface (GUI) which enhances the process of segmentation of blood vessels in retinal images, It can be helpful for the ophthalmologist to improve the morphological processes by analyzing key characteristics in processed images for faster diagnosis. Shanmugavadivu et al. [2012] [16] gave a filtering technique for the purpose of restoration of the retinal images which have been corrupted with a fixed-value impulse noise. This technique is simpler as compared to other techniques, so the rate of restoration rate is faster.Yin et al. [2012] [17] gave a method to segment the optic disc and opt