Full Document Chapter

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CONTENT S.NOTITLE PAGE .NO

Abstract2

Chapter 1Introduction3

Chapter 2Literature Survey6

Chapter 3Theoretical background15

3.1 EXISTING SYSTEM15

3.2 PROPOSEED SYSTEM17

3.3 MODULES19

Chapter 4Result of analytical26

4.1 System architecture26

4.2 Flow Diagram27

4.3 Use case Diagram28

4.4 Class Diagram29

4.5 Sequence Diagram30

4.6 Testing Of Product31

4.7 System Requirements35

Chapter 5Conclusion & Future Work41

5.1 Conclusion41

5.2 Future work42

5.3 Screen shots43

5.5 Reference44

Vessels Classification in Retinal Images by Graph-Based Approach ABSRACT The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. Our method outperforms recent approaches for A/V classification. Normal retinal images vessels are segment using the morphological operations and then using graph trace algorithm for identification the center line of the vessels and trace the pixel values as a feature and use the KNN classifier to classify the feature and assign which is the artery and which is the vein in retinal image. In feature we extract the thickness of the vessels to identify the disease details.

Chapter 1INTRODUCTION Automated detection of retinopathy in eye fundus images using digital image analysis methods has huge potential benefits, allowing the examination of a large number of images in less time, with lower cost and reduced subjectivity than current observer-based techniques. Another advantage is the possibility to perform automated screening for pathological conditions, such as diabetic retinopathy, in order to reduce the workload required of trained manual graders. Retinal vessels are affected by several systemic diseases, namely diabetes, hypertension, and vascular disorders. In diabetic retinopathy, the blood vessels often show abnormalities at early stages as well as vessel diameter alterations. Changes in retinal blood vessels, such as significant dilatation and elongation of main arteries, veins, and their branches, are also frequently associated with hypertension and other cardiovascular pathologies. Several automated techniques have been reported to quantify the changes in morphology of retinal vessels (width, tortuosity) indicative of retinal or cardiovascular diseases. Some of the techniques measure the vessel morphology as an average value representing the entire vessel network, e.g., average tortuosity. However recently, vessel morphology measurement specific to arteries or veins was found to be associated with disease. For example, plus disease in retinopathy of prematurity (ROP) may result in increase in arterial tortuosity relative to that of veins indicating the need for preventative treatment. Arterial narrowing, venous dilatation, and resulting decrease in artery-to-venous width ratio (AVR) may predict the future occurrence of a stroke event or a myocardial infarct. Unfortunately, the detection of minute changes in vessel width or tortuosity specific to arteries or veins may be difficult in a visual evaluation by an ophthalmologist or by a semi-automated method, which is laborious in clinical practice. Therefore, an automated identification and separation of individual vessel trees and the subsequent classification into arteries and veins is required for vessel specific morphology analysis. Several characteristic signs associated with vascular changes are measured, aiming at assessing the stage and severity of some retinal conditions. Generalized arteriolar narrowing, which is inversely related to higher blood pressure levels, is usually expressed by the Arteriolar-to-Venular diameter Ratio (AVR). The Atherosclerosis Risk in Communities (ARIC) study previously showed that a smaller retinal AVR might be an independent predictor of incident stroke in middle aged individuals. The AVR value can also be an indicator of other diseases, like diabetic retinopathy and retinopathy of prematurity. Among other image processing operations, the estimation of AVR requires vessel segmentation, accurate vessel width measurement, and artery/vein (A/V) classification. Therefore, any automatic AVR measurement system must accurately identify which vessels are arteries and which are veins, since slight classification errors can have a large influence on the final value. Several works on vessel classification have been proposed, but automated classification of retinal vessels into arteries and veins has received limited attention, and is still an open task in the retinal image analysis field. In recent years, graphs have emerged as a unified representation for image analysis, and graph-based methods have been used for retinal vessel segmentation, retinal image registration, and retinal vessel classification. In this paper we propose a graph-based method for automatic A/V classification. The graph extracted from the segmented retinal vasculature is analyzed to decide on the type of intersection points (graph nodes), and afterwards one of two labels is assigned to each vessel segment (graph links). Finally, intensity features of the vessel segments are measured for assigning the final artery/vein class. DOMAIN EXPLAIN Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two dimensional signals while applying already set signal processing methods to them.It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too.Image processing basically includes the following three steps. Importing the image with optical scanner or by digital photography. Analyzing and manipulating the image which includes data compression and image enhancement and spotting patterns that are not to human eyes like satellite photographs. Output is the last stage in which result can be altered image or report that is based on image analysis. Purpose of Image processing The purpose of image processing is divided into 5 groups. They are: Visualization - Observe the objects that are not visible. Image sharpening and restoration - To create a better image. Image retrieval - Seek for the image of interest. Measurement of pattern Measures various objects in an image. Image Recognition Distinguish the objects in an image.

Chapter 2 LITERATURE SURVEYTITTLE: Exploratory dijkstra forest based automatic vessel segmentation (2012)AUTHOR NAME: R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, We present a methodology for extracting the vascular network in the human retina using Dijkstras shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal video indirect ophthalmoscopy (VIO) image database from pediatric patients and compared the segmentations achieved by our method and state-of-the-art approaches to a human-drawn gold standard. Our experimental results show that our algorithm outperforms prior state-of-the-art methods, for both single VIO frames and automatically generated, large field-of-view enhanced mosaics. We have made the corresponding dataset and source code freely available online. Accurate segmentation and evaluation of the anatomical and pathological features of retinal vessels are critical for the diagnosis and study of many ocular diseases. These include retinopathy of prematurity (ROP). ROP is a disorder of the retinal blood vessels that is a major cause of vision loss in premature neonates. Important features of the disease include increased diameter (dilation) as well as increased tortuosity (wiggliness) of the retinal blood vessels in the portion of the retina centered on the optic nerve (the posterior pole). Increased dilation and tortuosity of the blood vessels in the posterior pole (called pre-plus in intermediate, and plus in severe circumstances) is an important indicator of ROP severity.

ADVANTAGE This exploratory strategy has two advantages: it eliminates the need for selecting a destination point manually, and it finds vessels as tree-like image regions, thereby accounting for vessel branching naturally and efficiently Existing methods in both categories have been developed primarily for use on high quality retinal fundus images, such as those obtained with the Ret Cam imaging system (Clarity Medical Systems, Inc., Pleasanton.DISADVANTAGE This lower sensitivity reduces both the problem of leakage, in which a segmentation goes beyond the correct vessel boundary and the problem of stopping too soon. For our experiments. Both versions of Dijkstras algorithm have the same computational complexity of where indicates the cardinality or size of a set. This complexity is achievable with a heap-based priority queue implementation.

TITLE: Automatic classification of retinal vessels into arteries and veins (2009)AUTHOR NAME: M. Niemeijer, B. van Ginneken, and M. D. Abramoff Abnormalities of retinal vasculatures can indicate health conditions in the body, such as the high blood pressure and diabetes. Providing automatically determined width ratio of arteries and veins (A/V ratio) on retinal fundus images may help physicians in the diagnosis of hypertensive retinopathy, which may cause blindness. The purpose of this study was to detect major retinal vessels and classify them into arteries and veins for the determination of A/V ratio. Images used in this study were obtained from DRIVE database, which consists of 20 cases each for training and testing vessel detection algorithms. Starting with the reference standard of vasculature e segmentation provided in the database, major arteries and veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We applied the black top-hat transformation and double-ring filter to detect retinal blood vessels. From the extracted vessels, large vessels extending from the optic disc to temporal regions were selected as target vessels for calculation of A/V ratio. Image features were extracted from the vessel segments from quarter-disc to one disc diameter from the edge of optic discs. The target segments in the training cases were classified into arteries and veins by using the linear discriminant analysis, and the selected parameters were applied to those in the test cases. Out of 40 pairs, 30 pairs (75%) of arteries and veins in the 20 test cases were correctly classified. The result can be used for the automated calculation of A/V ratio.

ADVANTAGE The true positive fraction is defined as the ratio of the number of pixels that were segmented correctly to the number of pixels in the gold standard vessels. For extracting the blood vessel regions, the methods using the black top-hat transformation and double ring filter have advantages.

DISADVANTAGE The major vessels used for the A/V ratio measurement usually run from an optic disc to the upper and lower temporal regions. In order to select such vessels, a vessel-range mask was superimposed to the images for including vessels that were inside this mask. Starting with the reference standard of vasculature segmentation provided in the database, major arteries and veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We applied the black top-hat transformation and double-ring filter to detect retinal blood vessels

TITTLE: Retinal microvascular abnormalities and cognitive decline (2009)AUTHOR NAME: S. R. Lesage, T. H. Mosley, T. Y. Wong, M. Szklo, D. Knopman, D. J. Catellier, S. R. Cole, R. Klein, J. Coresh, L. H. Coker, and A. R. Sharrett A substantial proportion of elderly persons do not experience normal cognitive aging and develop cognitive impairment or dementia (estimated prevalence in US adults >65 years 17% and 10%, respectively).1 Cardiovascular disease (CVD) is known to impact cognitive function in later years,2 and vascular and metabolic risk factors including high blood pressure, overweight and obesity, diabetes and stroke have been shown to be inversely associated with cognitive function among middle-aged and older adults. Some studies have found an association between atherosclerosis (ie large vessel disease) and reduced cognitive function, while others have reported weak or null associations. The vast majority of brain blood vessels are small blood vessels, ie arterioles 50 years. A total of 48 subjects without systemic hypertension or any other vascular disease and 54 subjects with confirmed hypertension were enrolled. Analysis was performed on retinal photographs taken by a retinal thickness analyzer (Talia Technology, Israel). The arteriovenous ratio (AVR) was calculated by a semi-automated vessel tracking VSL software (Talia Technology). Reproducibility was determined for software tracking, intra-, and intergrade selection as well as intra- and internist for 20 subjects. The effects of image quality degradation and decent ration were investigated.Results. Validation showed an excellent agreement between semi-automated software and manual vessel measurements. In the 102 subjects analyzed, retinal AVR was only correlated with established systemic hypertension (p = 0.01) and gender (p = 0.01). There was no effect of age on AVR. Other risk factors such as diabetes, smoking, body mass index, and current blood pressure showed some trends on multifactorial analysis. When limiting the number of vessels selected, software tracking induced no variability.

ADVANTAGE Overall, the reproducibility obtained for semi-automated vessel tracking and AVR is good and exceeds the one reported for the original ARIC study method (intergrade correlation 0.84, intergrade correlation That reduced image resolutions still yielded good ICC of, whereas significant noise, especially blurring, caused a marked reduction of ICC and respectively.DISADVANTAGE Such large variability in AVR imposes problems when making longitudinal individual comparisons. The vessel trunk was measured, thus avoiding the more complex calculations for branching.

Chapter 3THEORETICAL BACKGROUND

3.1 EXISTING SYSTEM This method uses existing vessel segmentation results, and some manually labeled Starting vessel segments. Grison et al. developed a tracking A/V classification technique that classifies the vessels only in a well-defined concentric zone around the optic disc. Then, by using the vessel structure reconstructed by tracking, the classification is propagated outside this zone, where little or no information is available to discriminate arteries from veins. Vazquez et al. Described a method which combines a color-based clustering algorithm with a vessel tracking method. First the clustering approach divides the retinal image into four quadrants, then it classifies separately the vessels detected in each quadrant, and finally it combines the results. Then, a tracking strategy based on a minimal path approach is applied to join the vessel segments located at different radii in order to support the classification by voting. Kondermann et al. described two feature extraction methods and two classification methods, based on support vector machines and neural networks, to classify retinal vessels. One of the feature extraction methods is profile-based, while the other is based on the definition of a region of interest (ROI) around each centerline point. To reduce the dimensionality of the feature vectors, they used a multiclass principal component analysis (PCA). Niemeijer et al. proposed an automatic method for classifying retinal vessels into arteries and veins using image features and a classifier. A set of centerline features is extracted and a soft label is assigned to each centerline, indicating the likelihood of its being a vein pixel. Then the average of the soft labels of connected centerline pixels is assigned to each centerline pixel. They tested different classifiers and found that the k-nearest neighbor (kNN) classifier provides the best overall performance. In, the classification method was enhanced as a step in calculating the AVR value.

DISADVANTAGE For addressing this problem we define an adaptive parameter, the threshold Tns, which is used as the criterion for merging two neighborhood nodes. Automated classification of retinal vessels into arteries and veins has received limited attention, and is still an open task in the retinal image analysis field. In order to reduce the complexity of the subsequent graph analysis, all endpoints with very short links are removed. The propagation of AV classification inside of ROI to the periphery may be complex due to the factors such as the AV crossings where artery and vein may run parallel to each other. Discuss variety of such vascular interactions due to which the propagation of AV classification to the outside of the ROI becomes complex and requires a rule-based approach. The proposed method provides the separation of vessel trees into arteries and veins as well as into primary vessels, and their branches, which may reduce the intertwining complexity of the retinal vessel structure that normally prevents the accurate measurement of individual vessel properties.

3.2 PROPOSEED SYSTEM The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). The link labeling process starts by locating the optic disc center (ODC) using the automatic method based on the entropy of vascular directions, proposed by Mendona et al. In order to make the classifier more robust, each image is processed using the method proposed by M. Foracchia et al. The following subsections present the results of applying the proposed A/V classification method on the images of these data bases. For evaluating the proposed method, which is the combination of graph-based classification with LDA, we have calculated the accuracy both for centerline pixel classification and for vessel pixel classification. Table VI shows the accuracy values for centerline and vessel pixels and accuracy value of 98.0% was obtained, thus demonstrating that the proposed methodology for A/V classification is reliable for use in an automated procedure for AVR calculation. Furthermore, we compared the performance of our approach with other recently proposed methods, and we con The promising results of the proposed A/V classification method on the images of three different databases demonstrate the independence of this method in A/V classification of retinal images with different properties, such as differences in size, quality, and camera angle. On the other hand, the high accuracy achieved by our method, especially for the largest arteries and veins, confirm that this A/V classification methodology is reliable for the calculation of several characteristic signs associated with vascular alterations. Clued that we are achieving better results.

ADVANTAGE Another advantage is the possibility to perform automated screening for pathological conditions, such as diabetic retinopathy, in order to reduce the workload required of trained manual graders. The high accuracy of the semiautomatic approach is a good indication that the structural information embedded in the graph-based method is important per se for link labeling. Such as differences in size, quality, and camera angle. On the other hand, the high accuracy achieved by our method, especially for the largest arteries and veins. Inversely related to higher blood pressure levels, is usually expressed by the Arteriolar-to-Venular diameter Ratio (AVR). The method proposed by Mendona et al. Was used for segmenting the retinal vasculature, after being adapted for the segmentation of high resolution images. As a vein. For each pair of labels in each sub graph, the label with higher artery probability will be assigned as an artery class, and the other as a vein class.

3.3 MODULES Input image Convert to gray Morphological operation Center line Pixel Extraction Extract the Features Classify the features Output imageMODULES DESCRIPTIONINPUT IMAGERETINAL COLOR IMAGE The health of the retina deteriorates with age in some people due to the appearance of drusens. Drusens are accumulation of lipid and other waste material from different layers of the retina. These are markers of age-related macular de Generation (ARMD) as their increasing number generally indicates risk for RMD, a leading cause of blindness in people above the age of 50. Morphological information of drusens is also crucial in determining the risk factor for ARMD. Color retinal images are used presently to visually identify the presence of druses. Automated detection and analysis can provide vital information about the quantity and quality of the drusens. In this paper, we report on two methods that we have developed to reliably detect and count drusens. The methods exploit the morphological characteristics of the drusens such as texture and their 3D profiles. We compare the results of using these two methods and make recommendations for automated drusen analysis.CONVERT TO GRAY1. The process of retinal image is convert to gray for done the further implementation in images2. In the convert process the RGB was remove in color image.3. In proposed the RGB in not completely removed but any one color like Red, Green or blue will remove. 4. So the other colors are there in retinal color image, so easy to segment the vessels in that gray image.MORPHOLOGICAL OPERATIONMorphological operations are affecting the form, structure or shape of an object. Applied on binary images (black & white images Images with only 2 colors black And white). They are used in preorpost processing (filtering, thinning, and runing) or for getting a representation or description of the shape of objects/regions (boundaries, skeletons convex hulls.The two principal morphological operations are: Dilation and operation. Dilation allows objects to expand, thus potentially filling in small holes and connecting disjoint objects. Erosion shrinks objects by etching away (eroding) their boundaries. These operations can be customized for an application by the proper selection of the structuring element, which determines exactly how the objects will be dilated or eroded.EXTRACT THE FEATUREThe feature extraction process is important for classification the feature extraction process the image pixel information is stored as vector that values will help to identify the vein and artery. That image quality features wasi. Intensity of the Red, Green and Blue of center pixels,ii. Hue, Saturation and Intensity of center pixels,iii. Mean of Red, Green, Blue of center line pixels,iv. Mean of Hue, Saturation, and Intensity in the vessel ,v. Stranded Deviation of Red, Green, and Blue intensities in the vessels,vi. Stranded Deviation of Hue, Saturation, and Intensity intensities in the vessels,vii. Maximum and minimum of Red and Green intensities in the vessel.Intensity of pixels Pretty pictures are nice, but many times we need to turn our images into quantifiable data. Image is useful for getting information from images, including pixel intensity. There are a number of different ways to get intensity information from images using the base package of Image (no plugins required.You can simply hover the cursor over a given area in the image and read out the pixel intensity at that pixel on the toolbar. For RGB images, there will be three numbers, red, green and blue.HUE The first step in many techniques for processing intensity and saturation in color images keeping hue unaltered is the transformation of the image data from RGB space to other color spaces such as LHS, HSI, YIQ, HSV, etc. Transforming from one space to another and processing in these spaces usually generate gamut problem, i.e., the values of the variables may not be in their respective intervals. Enhancement techniques for color images are studied here theoretically in a generalized setup. A principle is suggested to make the transformations gamut problem free in this regard. Using the same principle a class of hue preserving contrast enhancement transformations are proposed, which generalize the existing grey scale contrast intensification techniques to color images. These transformations are also seen to bypass the above mentioned color coordinate transformations for image enhancement. The developed principle is used to generalize the histogram equalization scheme for grey scale images to color images.INTENSITIES The quality or condition of being intense. Great energy, strength, concentration, vehemence, etc., as of activity, thought, or feeling: He went at the job with great intensity. A high or extreme degree, as of cold or heat. The degree or extent to which something is intense. A high degree of emotional excitement; depth of feeling: The poem lacked intensity and left me unmoved.STANDARED DEVIATIONTo further describe data sets, measures of spread or dispersion are used. One of the most commonly used measures is standard deviation. This value gives information on how the values of the data set are varying, or deviating, from the mean of the data set. Deviations are calculated by subtracting the mean,X, from each of the sample values,X, i.e. deviation.As some values are less than the mean, negative deviations will result, and for values greater than the mean positive deviations will be obtained. By simply adding the values of the deviations from the mean, the positive and negative values will cancel to result in a value of zero. By squaring each of the deviations, the problem of positive and negative values is avoided. To calculate the standard deviation, the deviations are squared. These values are summed, divided by the appropriate number of values and then finally the square root is taken of this result, to counteract the initial squaring of the deviation.The property of the each center line pixels are called features, that is 30 features are extracted.CLASSIFICATIONS1. Use that feature will help to set some thresh hold for identify the retinal vessels in which type (Artery or Vein)2. That classification done by using KNN classifier The k- Nearest-Neighbors (kNN) is a non-parametric classification method, which is simple but effective in many cases. For a data record t to be classified, its nearest neighbors are retrieved, and this forms a neighborhood of t. Majority voting among the data records in the neighborhood is usually used to decide the classification for t with or without consideration of distance-based weighting. However, to apply KNN we need to choose an appropriable value for k, and the success of classification is very much dependent on this value. In a sense, the kNN method is biased by k. There are many ways of choosing the k value, but a simple one is to run the algorithm many times with different k values and choose the one with the best performance.OUTPUT IMAGEThe output image is only show the vessels in two types like Artery and vein and the performance graph will draw and shows in proposed what is accuracy of segmentation result.

GRAPH BASED APPROACH This paper proposes an algorithm to measure the width of retinal vessels in fundus photographs using graph-based algorithm to segment both vessel edges simultaneously. First, the simultaneous two-boundary segmentation problem is modeled as a two-slice, 3-D surface segmentation problem, which is further converted into the problem of computing a minimum closed set in a node-weighted graph. An initial segmentation is generated from a vessel probability image. We use the REVIEW database to evaluate diameter measurement performance. The algorithm is robust and estimates the vessel width with sub pixel accuracy. Next, matching moles across images is modeled as a graph matching problem and algebraic relations between nodes and edges in the graphs are induced in the matching cost function, which contains terms reflecting proximity regularization, angular agreement between mole pairs, and agreement between the moles normalized coordinates calculated in the un warped back template. We propose and discuss alternative approaches for evaluating the goodness of matching. We evaluate our method on a large set of synthetic data (hundreds of pairs) as well as 56 pairs of real dermatological images. Our proposed method compares favorably with the state-of-the-art. To the best of our knowledge, there exists limited previous work on skin mole or lesion matching. In [9], Huang and Bergstresser proposed to utilize the area of the voronoi cells surrounding moles in the similarity term for mole matching. Then, a dynamic programming approach was used to find corresponding moles.

Chapter 4Result of analytical

4.1 SYSTEM DESIGNSYSTEM ARCHITECTURE

4.2 FLOW DIAGRAM Convert to graySegment the vesselsRetinal imageExtract the Graph Group Extract the feature Classification

Result

4.3 UML DIAGRAMUSE CASE DIAGRAM

4.4 CLASS DIAGRAM

4.5 SEQUENTAL DIAGRM

4.6 TESTING OF PRODUCT

SYSTEM TESTINGThe purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub-assemblies, assemblies and/or a finished product. It is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. There are various types of test. Each test type addresses a specific testing requirement.TYPES OF TESTSUnit testing Unit testing involves the design of test cases that validate that the internal program logic is functioning properly, and that program inputs produce valid outputs. All decision branches and internal code flow should be validated. It is the testing of individual software units of the application .it is done after the completion of an individual unit before integration. This is a structural testing, that relies on knowledge of its construction and is invasive. Unit tests perform basic tests at component level and test a specific business process, application, and/or system configuration. Unit tests ensure that each unique path of a business process performs accurately to the documented specifications and contains clearly defined inputs and expected results.

Functional testFunctional tests provide systematic demonstrations that functions tested are available as specified by the business and technical requirements, system documentation, and user manuals.Functional testing is centered on the following items:Valid Input:Identified classes of valid input must be Accepted.

Invalid Input:Identified classes of invalid input must be Rejected.

Functions:Identified functions must be exercised.Output:Identified classes of application outputs.Systems/Procedures: interfacing systems or procedures must be invoked.Organization and preparation of functional tests is focused on requirements, key functions, or special test cases. In addition, systematic coverage pertaining to identify Business process flows; data fields, predefined processes, and successive processes must be considered for testing. Before functional testing is complete, additional tests are identified and the effective value of current tests is determined.System TestSystem testing ensures that the entire integrated software system meets requirement. It tests a configuration to ensure known and predictable results. An example of system testing is the configuration oriented system integration test. System testing is based on process descriptions and flows, emphasizing pre-driven process links and integration points.

White Box TestingWhite Box Testing is a testing in which in which the software tester has knowledge of the inner workings, structure and language of the software, or at least its purpose. It is purpose. It is used to test areas that cannot be reached from a black box level.

Black Box Testing

Black Box Testing is testing the software without any knowledge of the inner workings, structure or language of the module being tested. Black box tests, as most other kinds of tests, must be written from a definitive source document, such as specification or requirements document, such as specification or requirements document. Test objectives All field entries must work properly. Pages must be activated from the identified link. The entry screen, messages and responses must not be delayed.

Integration TestingSoftware integration testing is the incremental integration testing of two or more integrated software components on a single platform to produce failures caused by interface defects. The task of the integration test is to check that components or software applications, e.g. components in a software system or one step up software applications at the company level interact without error.

Acceptance TestingUser Acceptance Testing is a critical phase of any project and requires significant participation by the end user. It also ensures that the system meets the functional requirements.Test Results: All the test cases mentioned above passed successfully. No defects encountered.

4.7 SYSTEM REQUIREMENTSSOFTWARE REQUIREMENTS: OS : Windows Software : Mat lab

HARDWARE REQUIREMENTS: Processor: Intel Pentium. RAM: 2GB SOFTWARE DESCRIPTION:MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical computation. Using MATLAB, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and FORTRAN.Mat lab is a data analysis and visualization tool which has been designed with powerful support for matrices and matrix operations. As well as this, Mat lab has excellent graphics capabilities, and its own powerful programming language. One of the reasons that Mat lab has become such an important tool is through the use of sets of Mat lab programs designed to support a particular task. These sets of programs are called toolboxes, and the particular toolbox of interest to us is the image processing toolbox. Rather than give a description of all of Mat lab's capabilities, we shall restrict ourselves to just those aspects concerned with handling of images. We shall introduce functions, commands and techniques as required. A Mat lab function is a keyword which accepts various parameters, and produces some sort of output: for example a matrix, a string, a graph. Examples of such functions are sin, imread, imclose. There are many functions in Mat lab, and as we shall see, it is very easy (and sometimes necessary) to write our own.Mat lab's standard data type is the matrix all data are considered to be matrices of some sort. Images, of course, are matrices whose elements are the grey values (or possibly the RGB values) of its pixels. Single values are considered by Mat lab to be matrices, while a string is merely a matrix of characters; being the string's length. In this chapter we will look at the more generic Mat lab commands, and discuss images in further chapters.

When you start up Mat lab, you have a blank window called the Command Window_ in which you enter commands. Given the vast number of Mat lab's functions, and the different parameters they can take, a command line style interface is in fact much more efficient than a complex sequence of pull-down menus.You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement financial modeling and analysis. Add-on toolboxes (collections of special-purpose MATLAB functions) extend the MATLAB environment to solve particular classes of problems in these application areas.MATLAB provides a number of features for documenting and sharing your work. You can integrate your MATLAB code with other languages and applications, and distribute your MATLAB algorithms and applications.When working with images in Mat lab, there are many things to keep in mind such as loading an image, using the right format, saving the data as different data types, how to display an image, conversion between different image formats. Image Processing Toolbox provides a comprehensive set of reference-standard algorithms and graphical tools for image processing, analysis, visualization, and algorithm development. You can perform image enhancement, image deploring, feature detection, noise reduction, image segmentation, spatial transformations, and image registration. Many functions in the toolbox are multithreaded to take advantage of multicore and multiprocessor computers.

MATLAB and imagesThe help in MATLAB is very good, use it!An image in MATLAB is treated as a matrixEvery pixel is a matrix elementAll the operators in MATLAB defined onMatrices can be used on images: +, -, *, /, ^, sqrt, sin, cos etc.MATLAB can import/export several image formatsBMP (Microsoft Windows Bitmap)

GIF (Graphics Interchange Files)HDF (Hierarchical Data Format)JPEG (Joint Photographic Experts Group)PCX (Paintbrush)PNG (Portable Network Graphics)TIFF (Tagged Image File Format)XWD (X Window Dump)MATLAB can also load raw-data or other types of image data

Data types in MATLABDouble (64-bit double-precision floating point)Single (32-bit single-precision floating point)Int32 (32-bit signed integer)Int16 (16-bit signed integer)Int8 (8-bit signed integer)Uint32 (32-bit unsigned integer)Uint16 (16-bit unsigned integer)Uint8 (8-bit unsigned integer)Images in MATLABBinary images: {0, 1} Intensity images: [0, 1] or uint8, double etc. RGB images: m-by-n-by-3 Indexed images: m-by-3 color map Multidimensional images m-by-n-by-p (p is the number of layers)IMAGE TYPES IN MATLABOutside Mat lab images may be of three types i.e. black & white, grey scale and colored. In Mat lab, however, there are four types of images. Black & White images are called binary images, containing 1 for white and 0 for black. Grey scale images are called intensity images, containing numbers in the range of 0 to 255 or 0 to 1. Colored images may be represented as RGB Image or Indexed Image.In RGB Images there exist three indexed images. First image contains all the red portion of the image, second green and third contains the blue portion. So for a 640 x 480 sized image the matrix will be 640 x 480 x 3. An alternate method of colored image representation is Indexed Image. It actually exist of two matrices namely image matrix and map matrix. Each color in the image is given an index number and in image matrix each color is represented as an index number. Map matrix contains the database of which index number belongs to which color.

IMAGE TYPE CONVERSIONRGB Image to Intensity Image (rgb2gray)RGB Image to Indexed Image (rgb2ind)RGB Image to Binary Image (im2bw)Indexed Image to RGB Image (ind2rgb)Indexed Image to Intensity Image (ind2gray)Indexed Image to Binary Image (im2bw)Intensity Image to Indexed Image (gray2ind)Intensity Image to Binary Image (im2bw)Intensity Image to RGB Image (gray2ind, ind2rgb)

Key FeaturesHigh-level language for technical computingDevelopment environment for managing code, files, and dataInteractive tools for iterative exploration, design, and problem solvingMathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, and numerical integration2-D and 3-D graphics functions for visualizing dataTools for building custom graphical user interfacesFunctions for integrating MATLAB based algorithms with external applications and languages, such as C, C++, Fortran, Java, COM, and Microsoft Excel. Chapter 55.1 CONCLUSION The classification of arteries and veins in retinal images is essential for the automated assessment of vascular changes. In previous sections, we have described a new automatic methodology to classify retinal vessels into arteries and veins which is distinct from prior solutions. One major difference is the fact that our method is able to classify the whole vascular tree and does not restrict the classification to specific regions of interest, normally around the optic disc. While most of the previous methods mainly use intensity features for discriminating between arteries and veins, our method uses additional information extracted from a graph which represents the vascular network. The information about node degree, the orientation of each link, the angles between links, and the vessel caliber related to each link are used for analyzing the graph, and then decisions on type of nodes are made (bifurcation, crossing, or meeting points). Next, based on the node types, the links that belong to a particular vessel are detected, and finally A/V classes are assigned to each one of these vessels using a classifier supported by a set of intensity features. The graph-based method with LDA outperforms the accuracy of the LDA classifier using intensity features, which shows the relevance of using structural information for A/V classification. Furthermore, we compared the performance of our approach with other recently proposed methods, and we conclude that we are achieving better results.

5.2 FUTURE ENHANCEMENT

The disease like Diabetes and some another disease affect the retinal vessels. In feature we used that artery and vein feature to classify the retinal is normal and up normal We use some feature extraction method to train normal and up normal dates. Use robust classifier to classify that feature to find the retinal is normal are abnormal

5.3 Screen shots

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