Human Recognition System based on Retina Vascular Network CharacteristicsChandrashekhar B.N                               ...
The retinal scanning systems are said to        has reputedly never falsely verified anbe very accurate. For example the  ...
macula, and posterior pole (i.e. the        pixels (e.g. foreground and background)fundus)                                ...
disadvantage of the method is that it is    for an image formation model,indiscriminate. It may increase the         disco...
integer valued filter in horizontal and     For Testing Phase:vertical direction and is therefore                         ...
•   Apply Sobel mask    •   Compare the each resultant pixel        to threshold     • If greater than threshold make     ...
•   Convert to double dimensional       array   •   Apply pre processing methods   •   Apply edge detection   •   Compare ...
given to the administrator via username    2. A. Can, H. Shen, J. N. Turner, J. L.and password.                           ...
given to the administrator via username    2. A. Can, H. Shen, J. N. Turner, J. L.and password.                           ...
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Human recognition system based on retina vascular network characteristics

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Human recognition system based on retina vascular network characteristics

  1. 1. Human Recognition System based on Retina Vascular Network CharacteristicsChandrashekhar B.N Honnaraju .BSr. Lecturer, Dept of ISE Lecturer, Dept of CSENMIT, Bangalore-64 BGS, Mandya eye retina. These are the optic nerve, theAbstract macula and the vascular network. TheThis paper proposes an efficient method reason to the selection was given by thefor Human Recognition System based fact that these characteristics remainon Retina Vascular Network unchanged through years andCharacteristics. Humans recognize each degradations are possible to occur onlyother according to their various because of eye diseases, such ascharacteristics for ages. We recognize glaucoma and retinopathy. Humanothers by their face when we meet them interference on the retina vascularand by their voice as we speak to them. network is not an issue at present.Identity verification (authentication) in Part from the proposed algorithm cancomputer systems has been traditionally also be used to extract informationbased on something that one has (key, about blood vessels network in retinalmagnetic or chip card) or one knows images. This information can be used to(PIN, password). Things like keys or grade disease severity or as a part ofcards, however, tend to get stolen or lost automated diagnosis of diseasesand passwords are often forgotten or (Biomedical Systems).disclosed. Human Recognition System based onHuman Recognition System based on Retina Vascular NetworkRetina Vascular Network Characteristics, This detection systemCharacteristics, This detection system can be used effectively to carry outcan be used effectively to carry out accurate authentication of a person.accurate authentication of a person. Also this detection system can be usedRetina Vascular Network Identification in or suited for environmentsAlgorithm for Human Recognition. requirements requiring maximumThis system authorizes a person based security such as government militaryon his retinal vascular characteristics. and banking.The system takes fundus image of the The iris is the coloured ring of texturedperson as input, performs pre- tissue that surrounds the pupil of theprocessing and produces edge detected eye. Even twins have different irisimage. This resultant image is patterns and everyone’s left and rightcompared with the images stored in the iris is different, too. Research showsdatabase. If the image exists, then the that the matching accuracy of irisperson is authorized, else unauthorized. identification is greater than of the Retina scan is based on the blood vesselKeywords-image analysis, image pattern in the retina of the eye. Retinarecognition, neural network, Image scan technology is older than the irissegmentation, Retina, Optic disc, scan technology that also uses a part ofMacula, Otsu method the eye. The first retinal scanning1. Introduction systems were launched by Eye DentifyThe identification procedure is based on in 1985.three structural elements of the human
  2. 2. The retinal scanning systems are said to has reputedly never falsely verified anbe very accurate. For example the unauthorized user so far. The falseEyeDentify’s retinal scanning systemrejection rate, on the other side, is Image segmentation stage clusters therelatively high as it is not always easy image into two distinct classes andto capture a perfect image of the detection of candidate bifurcation andretina.DNA testing. cross over points is done during the2. Literature review third stage using the MCN and neural“Detection of Vascular Intersection in Retina network technique.Fundus Image Using Modified Cross Point “Locating the Optic Nerve in a Retinal ImageNumber and Neural Network Technique” By Using the Fuzzy Convergence of the BloodM. I. Iqbal, A. M. Aibinu, M. Nilsson, I. B. Vessels” By Adam Hoover , MichaelTijani, and M. J. E. Salami. GoldbaumThis paper talks about the application of In this paper an automated method tothe knowledge of digital image locate the optic nerve in images of theprocessing, fuzzy logic and neural ocular fundus has been given . Theirnetwork technique to detect bifurcation method uses a novel algorithm we calland vein-artery cross-over points in fuzzy convergence to determine thefundus images. The acquired images origination of the blood vessel network.undergo pre-processing stage for They evaluate their method using 30Illumination equalization and noise images of healthy retinas and 51 imagesremoval.[5] Segmentation stage clusters of diseased retinas, containing suchthe image into two distinct classes by diverse symptoms as tortuous vessels,the use of fuzzy c-means technique, choroidal neovascularization, andneural network technique and modified haemorrhages that completely obscurecross-point number (MCN) methods[4] the actual nerve. On this difficult datawere employed for the detection of set, this method achieved 89% correctbifurcation and cross-over points. MCN detection.uses a 5x5 window with 16 The optic nerve is one of the mostneighbouring pixels for efficient important organs in the human retina.detection of bifurcation and cross over The central retinal artery and centralpoints in fundus images. Result retinal vein emanate through the opticobtained from applying this hybrid nerve,[3]supplying the retina withmethod on both real and simulated blood. The optic nerve also serves asvascular points shows that this method the conduit for the flow of informationperform better than the existing simple from the eye to the brain. Most retinalcross-point number (SCN) method, thus pathology is local in its early stages, notan improvement to the vascular point affecting the entire retina, so that visiondetection and a good tool in the impairment is more gradual. Inmonitoring and diagnosis of diabetic contrast, pathology on or near the nerveretinopathy. can have a more severe effect in earlyA three stage bifurcation and cross-over stages, due to the necessity of the nervepoints detection in FI(Fundus Image) is for vision.hereby presented. These stages are: Fundus Image: An image which isimage pre processing, image obtained from a fundus camera issegmentation and bifurcation and cross- referred to as the fundus image[2] Aover point’s detection. The acquired fundus camera or retinal camera is aimage undergoes pre processing stage specialized low power microscope with(A); for colour space conversion, an attached camera designed toillumination equalization and noise photograph the interior surface of thefiltering using a 5x5 median filter. eye, including the retina, optic disc,
  3. 3. macula, and posterior pole (i.e. the pixels (e.g. foreground and background)fundus) then calculates the optimum thresholdA typical fundus image[2]consists of separating those two classes so thatthree important parts the macula, the their combined spread (intra-classoptic nerve and the blood vessels .The variance) is minimal. The extension ofoptic nerve is one from which the blood the original method to multi-levelvessels seems to originate from and is thresholding is referred to as the Multithe brightest part of the retina. Otsu method .The fundus image is taken as the inputfor pre processing and edge detection[6]and based on which comparison is doneand other operations are carried out onit. Fig:Before thresholdingFig:Typical Fundus Image2.1Image Pre-processingImage Pre-processing essentially Fig: After thresholdingcontains two phases. These are image 2.1.2 Histogram Equalizationenhancement and image restoration. This method usually increases theThe idea behind enhancement global contrast of many images,techniques is to bring out detail that is especially when the usable data of theobscured or simply to highlight certain image is represented by close contrastfeatures of interest in an image. Image values. Through this adjustment, therestoration techniques tend to be based intensities can be better distributed onon mathematical or probabilistic models the histogram. This allows for areas ofof image degradation. lower local contrast to gain a higher2.1.1 Thresholding contrast. Histogram equalization accomplishes this by effectivelyThresholding is defined as the process spreading out the most frequentin which individual pixels in an image intensity values.are marked as “object” pixels if their The method is useful in images withvalue is greater than some threshold backgrounds and foregrounds that arevalue (assuming an object to be brighter both bright or both dark. In particular,than the background) and as the method can lead to better views of“background” pixels otherwise. bone structure in x-ray images, and toThe thresholding technique that we better detail in photographs that arehave used is Otsu Thresholding which over or under-exposed. A keyis an automated thresholding technique. advantage of the method is that it is aOtsus method is used to automatically fairly straightforward technique and anperform histogram shape-based image invertible operator. So in theory, if thethresholding, or, the reduction of a histogram equalization function isgraylevel image to a binary image. The known, then the original histogram canalgorithm assumes that the image to be be recovered. The calculation is notthresholded contains two classes of computationally intensive. A
  4. 4. disadvantage of the method is that it is for an image formation model,indiscriminate. It may increase the discontinuities in image brightness arecontrast of background noise, while likely to correspond to:decreasing the usable signal.Histogram equalization is a specificcase of the more general class of • discontinuities in depth,histogram remapping methods. These • discontinuities in surfacemethods seek to adjust the image to orientation,make it easier to analyze or improve • changes in material propertiesvisual quality and • variations in scene illumination. The edge detection techniques used here are sobel edge filtering ,prewitt edge filtering technique which are briefly described below. 2.1.3.1 Sobel Filter The Sobel operator is used in image processing, particularly within edge detection algorithms. Technically, it is a discrete differentiation operator,Fig:Input Image computing an approximation of the gradient of the image intensity function. At each point in the image, the result of the Sobel operator is either the corresponding gradient vector or the norm of this vector. The Sobel operator is based on convolving the image with a small, separable, and integer valued filter in horizontal and vertical direction and is therefore relatively inexpensive in terms of computations. On the other hand, the gradient approximation whichFig: Histogram Equalized Image it produces is relatively crude, in particular for high frequency variations2.1.3Edge Detection in the image.Edge detection is a fundamental tool in 2.1.3.2 Prewitt Filterimage processing and computer The Prewitt operator is used in imagevision[6], particularly in the areas of processing, particularly within edgefeature detection and feature extraction, detection algorithms. Technically, it is awhich aim at identifying points in a discrete differentiation operator,digital image at which the image computing an approximation of thebrightness changes sharply or, more gradient of the image intensity function.formally, has discontinuities. At each point in the image, the result ofThe purpose of detecting sharp changes the Prewitt operator is either thein image brightness is to capture corresponding gradient vector or theimportant events and changes in norm of this vector. The Prewittproperties of the world. It can be shown operator is based on convolving thethat under rather general assumptions image with a small, separable, and
  5. 5. integer valued filter in horizontal and For Testing Phase:vertical direction and is therefore 1. The user has to load his imagerelatively inexpensive in terms of using the Load Image optioncomputations. On the other hand, thegradient approximation which it 2. The user then has to click theproduces is relatively crude, in compare option through whichparticular for high frequency variations the new image is compared within the image images present in the database3 Methodologies 4. Algorithm StepsInitially the whole operation is divided The different modules algorithms areinto two phases the learning phase and as listed below:-the testing phase. In learning phase we 1) Image Pre-processingtry to store images of an authorized • Histogramperson to the database so that it can be Equalization.used for comparison when he comes forauthentication .The learning phase can 2) Edge detectiononly be carried out by the administrator • Sobel maskso he has to login first and then only hecan do further operations. • Prewitt maskThe testing phase is one where an • Robert maskauthorized or an unauthorized personcomes for authentication so he first 4) Image comparisoninputs his image and then comparison 5) Saving Image to the databaseof the image is done with the database 4.1.1 Image Pre-processingto check his authenticity.The main user Image pre-processing is defined as therequirements are for learning and stages before an image is processed intesting phase are:- order to get an enhanced image throughFor Learning Phase: which we can get better outputs. The 1. The user has to login if he image pre-processing steps used here wants to store new images to is:- the database through the Login 1) Histogram Equalization option. Algorithm: • Input the image 2. The user then has to specify the size and the name which is • Convert to double dimensional considered as the name of the array output image that is to be • Compute Cumulative stored in database using Image distributive function Size option. • Compute Probability density function 3. The user then has to load the • Output the image image which he wants to store 4.1.2Edge Detection in the database through the Sobel Mask Load Image option. Algorithm: • Input the image 4. Then the processing of the image is done through the • Convert to double dimensional Process option using which the array output image is obtained. • Specify the threshold • For each pixel in the image,
  6. 6. • Apply Sobel mask • Compare the each resultant pixel to threshold • If greater than threshold make pixel black, else white • Output the imageThe Sobel edge detector or the Sobelfilter can be implemented in threeorientations Sobel X, Sobel Y and Sobel Fig:Implementation results of Prewitt OperatorXY. We have implemented all the threeorientations and found that Sobel XYgave the best result. The output imagesobtained from Sobel are as follows Robert Algorithm • Input the image • Convert to double dimensional array • Specify the thresholdFig: Implementation results of Sobel operator • For each pixel in the image,Prewitt Mask • Apply Robert maskAlgorithm: • Compare the each resultant pixel • Input the image to threshold • Convert to double dimensional • If greater than threshold make array pixel black, else white • Specify the threshold • Output the image • For each pixel in the image, • Apply Prewitt mask • Compare the each resultant pixel to threshold • If greater than threshold make Fig: Implementation results of Robert Operator pixel black, else white 5 Image Comparisons • Output the imageThe Prewitt edge detector or the Prewitt Learning Phase Algorithm:filter can be implemented in three • Input the imageorientations o Prewitt X,Prewitt Y and • Convert to double dimensionalPrewitt XY. We have implemented all arraythe three orientations and found that • Apply pre processing methodsPrewitt XY gave the best result. The • Apply edge detectionoutput images obtained from Prewitt are • Compare the edge detectedas follows image with the images in the database • If the image exists then discard it and print error message • Otherwise save it in the database • End Testing Phase Algorithm: • Input the image
  7. 7. • Convert to double dimensional array • Apply pre processing methods • Apply edge detection • Compare the edge detected image with the images in the database • If the image exists then print authentication success message • Else print authentication failure message • EndSaving Images in databaseAlgorithm: Input the image 6. Conclusion • Convert the image into two In this paper we illustrate a complete dimensional array Retina Vascular Network Identification • Apply pre processing and edge Algorithm for Human Recognition. detection This system authorizes a person based on his retinal vascular characteristics. • Then convert the obtained two The system takes fundus image of the dimensional array into a raw person as input, performs pre- image processing and produces edge detected • Store the raw image with the image. This resultant image is persons name obtained from compared with the images stored in the input database. If the image exists, then the • Then append the name to a file person is authorized, else unauthorized. required for comparison We have also implemented some5. Results fundamental pre-processing techniquesExperiment – A raw fundus image is such as Histogram Equalization andgiven as input and then processing Image thresholding to alter certainoption is clicked which produces the characteristics of the image. For edgefollowing output which is compared detection we have used the variouswith the database if it exists then a masks such as Robert mask, Prewittsuccess message is shown or is the mask and Sobel mask. These filters areimage does not exist in the database valuable in detecting edges of variousthen a failure message message is parts in the image.shown We have provided the administrator an option to add and remove the authorized person to the database. Where as a person other than administrator can only check whether he is authorized or not. Access rights to perform adding and removing a person from database is
  8. 8. given to the administrator via username 2. A. Can, H. Shen, J. N. Turner, J. L.and password. Tanenbaum, and B. Roysam, “Rapid automated tracing and feature extractionReferences from retinal fundus images using direct1A. Pinz, S. Bernogger, P. Datlinger, exploratory algorithms,” IEEEand A. Kruger, “Mapping the human tansactions on Information Technologyretina,” IEEE Transactions on Medical in Biomedicine, vol. 3,no. 2, pp. 125–Imaging, vol. 17, no. 4, pp. 606–619, 137, June 1999.1998. 3. Locating the Optic Nerve in a Retinal Image Using the Fuzzy Convergence of the Blood Vessels By Adam Hoover, Michael Goldbaum4. Detection of Vascular Intersection inRetina Fundus Image Using ModifiedCross Point Number and NeuralNetwork Technique By M. I. Iqbal, A.M. Aibinu, M. Nilsson, I. B. Tijani,and M. J. E. Salami.5. A fuzzy impulse noise detection and reduction method chulte, S.Nachtegael, M.; De Witte, V.; Van der Weken, D.; Kerre, E.E.Dept. of Appl. Math. & Comput. Sci., Ghent Univ., Gent, Belgium6. Local scale control for edgedetection and blur estimation IEEETrans. Pattern Anal. Mach. Intell., 20(7) (1998), pp. 699–7167. An improved Sobel algorithm based on median filter hunxi Ma; Lei ang; Wenshuo Gao; Zhonghui Liu; Digital Media Dept., Commun. Univ. of China, Beijing, China8. design of an image edge detectionfilter using the sobel operator nickkanopoulos, member, ieee,nageshvasanthavada, member, ieee,androbertbaker
  9. 9. given to the administrator via username 2. A. Can, H. Shen, J. N. Turner, J. L.and password. Tanenbaum, and B. Roysam, “Rapid automated tracing and feature extractionReferences from retinal fundus images using direct1A. Pinz, S. Bernogger, P. Datlinger, exploratory algorithms,” IEEEand A. Kruger, “Mapping the human tansactions on Information Technologyretina,” IEEE Transactions on Medical in Biomedicine, vol. 3,no. 2, pp. 125–Imaging, vol. 17, no. 4, pp. 606–619, 137, June 1999.1998. 3. Locating the Optic Nerve in a Retinal Image Using the Fuzzy Convergence of the Blood Vessels By Adam Hoover, Michael Goldbaum4. Detection of Vascular Intersection inRetina Fundus Image Using ModifiedCross Point Number and NeuralNetwork Technique By M. I. Iqbal, A.M. Aibinu, M. Nilsson, I. B. Tijani,and M. J. E. Salami.5. A fuzzy impulse noise detection and reduction method chulte, S.Nachtegael, M.; De Witte, V.; Van der Weken, D.; Kerre, E.E.Dept. of Appl. Math. & Comput. Sci., Ghent Univ., Gent, Belgium6. Local scale control for edgedetection and blur estimation IEEETrans. Pattern Anal. Mach. Intell., 20(7) (1998), pp. 699–7167. An improved Sobel algorithm based on median filter hunxi Ma; Lei ang; Wenshuo Gao; Zhonghui Liu; Digital Media Dept., Commun. Univ. of China, Beijing, China8. design of an image edge detectionfilter using the sobel operator nickkanopoulos, member, ieee,nageshvasanthavada, member, ieee,androbertbaker

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