Fig.1.1. Block diagram for image processing system

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1 APPLICATION OF IMAGE PROCESSING SYSTEM-AN INTRODUCTION & PROPOSED SYSTEM Prof. A. Sharmila Prof. P.Mahalakshmi VIT University, Vellore Abstract:-The term digital image refers to processing of a two dimensional picture by a digital computer. In a broader context, it implies digital processing of any two dimensional data. A digital image is an array of real or complex numbers represented by a finite number of bits. An image given in the form of a transparency, slide, photograph or an X-ray is first digitized and stored as a matrix of binary digits in computer memory. This digitized image can then be processed and/or displayed on a high-resolution television monitor. For display, the image is stored in a rapid-access buffer memory, which refreshes the monitor at a rate of 25 frames per second to produce a visually continuous display. The image processing system is described below in the Fig.1.1. THE IMAGE PROCESSING SYSTEM Digitizer Mass Storage Image Processor Digital Computer Operator Console Display Hard Copy Device Fig.1.1. Block diagram for image processing system DIGITIZER: A digitizer converts an image into a numerical representation suitable for input into a digital computer. Some common digitizers are 1.Microdensitometer 2.Flying spot scanner 3.Image dissector 4.Videocon camera 5.Photosensitive solid- state arrays. IMAGE PROCESSOR: An image processor does the functions of image acquisition, storage, preprocessing, segmentation, representation, recognition and interpretation and finally displays or records the resulting image. The following block diagram shown in Fig.1.2.gives the fundamental sequence involved in an image processing system. 1

2 Problem Domain Image Acquisition Segmentation Representation & Description Preprocessing Knowledge Base Recognition & interpretation Result Fig.1.2. Block diagram of fundamental sequence involved in an image processing system As detailed in the diagram, the first step in the process is image acquisition by an imaging sensor in conjunction with a digitizer to digitize the image. The next step is the preprocessing step where the image is improved being fed as an input to the other processes. Pre-processing typically deals with enhancing, removing noise, isolating regions, etc. Segmentation partitions an image into its constituent parts or objects. The output of segmentation is usually raw pixel data, which consists of either the boundary of the region or the pixels in the region themselves. Representation is the process of transforming the raw pixel data into a form useful for subsequent processing by the computer. Description deals with extracting features that are basic in differentiating one class of objects from another. Recognition assigns a label to an object based on the information provided by its descriptors. Interpretation involves assigning meaning to an ensemble of recognized objects. The knowledge about a problem domain is incorporated into the knowledge base. The knowledge base guides the operation of each processing module and also controls the interaction between the modules. Not all modules need be necessarily present for a specific function. The composition of the image processing system depends on its application. The frame rate of the image processor is normally around 25 frames per second. DIGITAL COMPUTER: Mathematical processing of the digitized image such as convolution, averaging, addition, subtraction, etc. are done by the computer. MASS STORAGE: The secondary storage devices normally used are floppy disks, CD ROMs etc. HARD COPY DEVICE: The hard copy device is used to produce a permanent copy of the image and for the storage of the software involved. OPERATOR CONSOLE: The operator console consists of equipment and arrangements for verification of intermediate results and for alterations in the software as and when require. The operator is also capable of checking for any resulting errors and for the entry of requisite data. IMAGE PROCESSING FUNDAMENTAL: Digital image processing refers processing of the image in digital form. Modern cameras may directly take the image in digital form but generally images are originated in optical form. They are captured by video cameras and digitalized. The digitalization process includes sampling, quantization. Then these images are processed by the five fundamental processes, at least any one of them, not necessarily all of them. 2

3 IMAGE PROCESSING TECHNIQUES: This section gives various image processing techniques which is shown in Fig.1.3. Image Enhancement IP Fig.1.3.Image processing techniques IMAGE ENHANCEMENT: Image enhancement operations improve the qualities of an image like improving the image s contrast and brightness characteristics, reducing its noise content, or sharpen the details. This just enhances the image and reveals the same information in more understandable image. It does not add any information to it. IMAGE RESTORATION: Image restoration like enhancement improves the qualities of image but all the operations are mainly based on known, measured, or degradations of the original image. Image restorations are used to restore images with problems such as geometric distortion, improper focus, repetitive noise, and camera motion. It is used to correct images for known degradations. IMAGE ANALYSIS: Image analysis operations produce numerical or graphical information based on characteristics of the original image. They break into objects and then classify them. They depend on the image statistics. Common operations are extraction and description of scene and image features, automated measurements, and object classification. Image analyze are mainly used in machine vision applications. IMAGE COMPRESSION: Image compression and decompression reduce the data content necessary to describe the image. Most of the images contain lot of redundant information, compression removes all the redundancies. Because of the compression the size is reduced, so efficiently stored or transported. The compressed image Image Restoration Image Analysis Image Compression Image Synthesis is decompressed when displayed. Lossless compression preserves the exact data in the original image, but Lossy compression does not represent the original image but provide excellent compression. IMAGE SYNTHESIS: Image synthesis operations create images from other images or non-image data. Image synthesis operations generally create images that are either physically impossible or impractical to acquire. APPLICATIONS OF DIGITAL IMAGE PROCESSING: Digital image processing has a broad spectrum of applications, such as remote sensing via satellites and other spacecrafts, image transmission and storage for business applications, medical processing, radar, sonar and acoustic image processing, robotics and automated inspection of industrial parts. MEDICAL APPLICATIONS: In medical applications, one is concerned with processing of chest X-rays, cineangiograms, projection images of transaxial tomography and other medical images that occur in radiology, nuclear magnetic resonance (NMR) and ultrasonic scanning. These images may be used for patient screening and monitoring or for detection of tumors or other disease in patients. SATELLITE IMAGING: Images acquired by satellites are useful in tracking of earth resources; geographical mapping; prediction of agricultural crops, urban growth and weather; flood and fire control; and many other environmental applications. Space image applications include 3

4 recognition and analysis of objects contained in image obtained from deep space-probe missions. COMMUNICATION: Image transmission and storage applications occur in broadcast television, teleconferencing, and transmission of facsimile images for office automation, communication of computer networks, closedcircuit television based security monitoring systems and in military communications. RADAR IMAGING SYSTEMS: Radar and sonar images are used for detection and recognition of various types of targets or in guidance and maneuvering of aircraft or missile systems. DOCUMENT PROCESSING: It is used in scanning, and transmission for converting paper documents to a digital image form, compressing the image, and storing it on magnetic tape. It is also used in document reading for automatically detecting and recognizing printed characteristics. DEFENSE/INTELLIGENCE: It is used in reconnaissance photointerpretation for automatic interpretation of earth satellite imagery to look for sensitive targets or military threats and target acquisition and guidance for recognizing and tracking targets in real-time smart-bomb and missile-guidance systems. 1.1 OBJECTIVE: In this project first extracts a graph from the vascular tree [1], and afterwards makes a decision on the type of each intersection point (graph node). Based on the node types in each separate sub graph, all vessel segments (graph links) that belong to a particular vessel are identified and then labeled using two distinct labels. Finally, the A/V classes are assigned to the sub graph labels by extracting a set of features and using a linear classifier. 1.2 EXIXTING SYSTEM We use the Gaussian Mixture Model Expectation Maximization (GMM-EM) clustering method which, unlike K-means, does not rely heavily on initialization. The commonly used approach for determining the parameters(mean, Covariance, mixture coefficient) of a Gaussian Mixture Model from a given dataset is to use the maximumlikelihood estimation. The EM algorithm is a general, iterative technique for computing maximum-likelihood. In GMM-EM classifier algorithm, a uniform distribution to the mixture is added to pick up background noise or data points which were not associated to either an artery or a vein cluster. The classifier is run for 10 different initial cluster centers and the parameters corresponding to the best fit (maximum likelihood) was chosen to compute Gaussian mixtures. EXIXTING SYSTEM DISADVANTAGES: The classification is propagated outside this zone, where little or no information is available to discriminate arteries from veins. Low accuracy. Vessel calibers can be affected by diseases; therefore this is not a reliable feature for A/V classification. Arteries Manuallylabeled vessels segmentation LITERATURE SURVEY: 1. Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction by Ana Maria Mendonça, Senior Member, IEEE, and AurélioCampilho, Member, IEEE This paper presents an automated method for thesegmentation of the vascular network in retinal images. Thealgorithm starts with the extraction of vessel centerlines, whichare used as guidelines for the subsequent vessel filling phase. Forthis purpose, the outputs of four directional differential operatorsare processed in order to select connected sets of candidate pointsto be further classified as centerline pixels using vessel derivedfeatures. The final segmentation is obtained using an iterativeregion growing method that integrates the contents of severalbinary images resulting from vessel width dependent morphologicalfilters. Our approach was tested on two publicly availabledatabases and its results are compared with recently publishedmethods. The results demonstrate that our algorithm outperformsother solutions and approximates the average accuracy of ahuman observer without a significant degradation of sensitivityand specificity. 4

5 2. Parallel Thinning with Two- Subiteration Algorithms ZICHENG GUO and RICHARD W. HALL Two parallel thinning algorithms arepresented and evaluated in this article. The two algorithmsuse two-subiteration approaches: (1) alternatively deletingnorth and east and then south and west bounda y pixels and(2) alternately applying a thinning operator to one of twosubfields. Image connectivities are proven to be preservedand the algorithms speed and medial curve thinness arecompared to other two-subiteration approaches and a fullyparallel approach. Both approaches produce very thinmedial curves and the second achieves the fastest overallparallel thinning. 3. Automated Localization of Optic Disc in Retinal Images An efficient detection of optic disc (OD) in colour retinal images is a significant task in an automated retinal image analysis system. Most of the algorithms developed for OD detection are especially applicable to normal and healthy retinal images. It is a challenging task to detect OD in all types of retinal images, that is, normal, healthy images as well as abnormal, that is, images affected due to disease. This paper presents an automated system to locate an OD and its centre in all types of retinal images. The ensemble of steps based on different criteria produces more accurate results. The proposed algorithm gives excellent results and avoids false OD detection. The technique is developed and tested on standard databases provided for researchers on internet, Diaretdb0 (130 images), Diaretdb1 (89 images), Drive (40 images) and local database (194 images). The local database images are collected from ophthalmic clinics. It is able to locate OD and its centre in 98.45% of all tested cases. The results achieved by different algorithms can be compared when algorithms are applied on same standard databases. This comparison is also discussed in this paper which shows that the proposed algorithm is more efficient. 1.3 PROPOSED SYSTEM: The method proposed in this paper follows a graph-based approach, where we mostly focus on a characteristic of the retinal vessel tree that, at least in the region near the optic disc, veins rarely cross veins and arteries rarely cross arteries. Based on this assumption we may define different types of intersection points: bifurcation, crossing, meeting, and connecting points. A bifurcation point is an intersection point where a vessel bifurcates to narrower parts. In a crossing point a vein and an artery cross each other. In a meeting point the two types of vessels meet each other without crossing, while a connecting point connects different parts of the same vessel. The decision on the type of the intersection points are made based on the geometrical analysis of the graph representation of the vascular structure [2]. 5

6 BLOCK DIAGRAM: Stepwise process of proposed system for classifying artery and vein of a retinal image is shown below in the Fig.1.3. INPUT IMAGE VESSEL SEGMENTATION VESSEL CALIBER ESTIMATION FEATURE EXTRACTION VESSEL CENTER LINE EXTRACTION NODE TYPE DICISION LINEAR CLASSIFIER GRAPH EXTRACTION LINKS LABELING A/V CLASSES ASSIGNING GRAPH MODIFICATIO N Graph Analysis A/V Classification Graph Generation CLASSIFICATIO N RESULTS Fig.1.3. Block diagram of the proposed system ADVANTAGES Arteries and veins information extracted from a graph which represents the vascular network. High accuracy. Our method usesadditional information extracted from a graph which representsthe vascular network. REFERENCES [1] S. Vazquez, B. Cancela, N. Barreira, M. Penedo, and M. Saez, On the automatic computation of the arterio-venous ratio in retinal images: Using minimal paths for the artery/vein classification, in Proc. Int. Conf. Digital Image Comput., Tech. Appl., 2010, pp [2] A. S. Neubauer, M. Ludtke, C. Haritoglou, S. Priglinger, and A. Kampik, Retinal vessel 6

7 analysis reproducibility in assessing cardiovascular disease, Optometry Vis. Sci., vol. 85, p , Apr