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Volume-2 Issue-12

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Volume-2 Issue-12, November 2014, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

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Sanae El Attar, Souhaib Aammou, Az-Eddine Nasseh

Paper Title:

Proposal for an Automatic Identification Model of Learning Styles

Abstract: Hypermedia environments are becoming essential tools to enhance the educational value in teaching. This use is seen by facilitating the coming of the web world that offers us the opportunity to access hypermedia resources on the Web. The implementation of some educational activities in the form of hypermedia, can enhance the learning of cognitive skills in some learners. However, several LMS (Learning Management Systems) offer non-adapted to different types of learners learning activities. Now a Adaptive educational hypermedia system well designed, can generate varied and adapted to each profile educational activities. The consideration of values is very important to get to offer appropriate activities, and produce appropriate feedback. If this system called automatic identification of learning styles which is the subject of this document model, taking into account key factors such as learner preferences, values, characteristics and types of feedback, is to arrive interpret preferences peculiarities that distinguish each user. So we group a set of patterns each with its appropriate weight for each learner, through which one can determine the corresponding values of each characteristics in Learning Style Model. Once this is accomplished, we come to calculate the value of distinct preference for each profile, and the value of the confidence level based on the availability of data on each learner associated to each characteristic. To validate our model, which is still in experimental stage, we stage of implementation of the necessary tools. Once confirmation is made, the model will be used as an analytical tool.

Adaptive hypermedia system, learner model, learning styles.


1.        Bulterman D., Rutledge L., Hardman L. et Van OSSENBRUGGEN J.. Supporting Adaptive and Adaptable Hypermedia Presentation Semantics. The 8th IFIP 2.6 Working Conference on Database Semantics (DS-8): Semantic Issues in Multimedia Systems, 1999.
2.        Halasz F. & Schwartz M.. The Dexter Hypertext Reference Model. Communications of the ACM 37(2), Grønbæk K. and Trigg R. (Eds.), 30-39, 1994.

3.        Beshuizen J.J., Stoutjesdijk E.T. Study Strategies in a Computer Assisted Study Environment. Learning and Instruction, 9, 1999, pp. 281–301.

4.        Keefe J., Ferrell B.. Developing a Defensible Learning Style Paradigm, Educational Leadership, 48 (2), 1990.

5.        Riding R. J., Rayner S.  Cognitive Styles and Learning Strategies: Understanding Style Differences in Learning and Behaviour, David Fulton Publishers, 1998.

6.        James W., Gardner D.. Learning Styles: Implications for Distance Learning. New Directions for Adult and Continuing Education, 1995, 67.

7.        Gregorc A.F. Learning/Teaching Styles: Potent Forces behind Them. Educational Leadership, 1979, 36 (4).

8.        Jonassen D. H., Grabowski B. L.  Handbook of Individual Differences: Learning and Instruction, Hove: LEA, 1993.

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13.     J. Wang, “Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),” IEEE J. Quantum Electron., submitted for publication.
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Sumitra Pundlik, Shwetali Jori, Juilee Kapure, Anisha Gaikwad, ShwetaValunj

Paper Title:

Survey Paper on "Ontology as a Driving Force"

Abstract: Ontology, a branch of artificial intelligence, is a formal representation of concepts of a particular domain and relationships amongst those concepts. Ontology acts as a powerful tool or a driving factor for many real world applications and this paper presents some of those ontology-based approaches. This paper describes how ontology is modeled, implemented and used in Web Semantics, Business Process Networks and Knowledge and Application Engineering.

Ontology, Semantic Web, Ontological Development.


1.        Prof. Ernesto D’Avanzo, Prof. TsviKuflik, Candidato Antonio Lieto, ” Manually vs semiautomatic domain specific ontology building”, Corso di LaureaSpecialistica in Comunicazioned’impresa e pubblica, Tesi di Laurea in Informatica per ilCommercioElettronico, Anno accademico 2007-2008.
2.        Edward H.Y. Lim, Hillman W.K. Tam, Sandy W.K. Wong, James N. K. Liu and Raymond S. T. Lee, ” Collaborative Content and User-based Web Ontology Learning System”,  IEEE 2009.

3.        MadhusudanTherani, ” Ontology Development for Designing and Managing Dynamic Business Process Networks”, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 3, NO. 2, MAY 2007.

4.        QIU Wei School of Computer Science, Jia Ying University,  Meizhou City,  China, “Development and Application of Knowledge Engineering Based on Ontology”, Third International Conference on Knowledge Discovery and Data Mining,2010.






A. Jalali, A. Nalawade, K. Kulkarni, S. Mishra

Paper Title:

Mobile CMS Platform for Android

Abstract: Android is playing a vital role in today's world. Everybody is more interactive with android applications rather than using the Websites. For any enterprise the explosive growth in mobile devices is impossible to ignore. But while many companies would love to extend their e-commerce website to a mobile audience, they're often uncertain about how to proceed. We provide a solution to this problem by providing a platform .Our system focuses particularly on the E-commerce websites which are built with the help of Magento [1] Framework. The web Interface Which we are building will be converting any E- commerce website based on magento framework into an android application. All third party payments will be handled by the system admin and also customer website's database security is kept in mind.

Android, CMS, Magento, Web Interface.


1.        R. Ravensbergen, S. Schoneville, “Magento 2nd Edition Beginner's Guide,”  2nd ed. vol. 3, Published by Packt  Publishing UK. ISBN 978-1-78216-270-4
2.        M.  Kimsal, “PHP architect's Guide to Programming Magento,” First Edition: May 2008 ISBN: 978-0-9738621-7-1

3.        A.  Macgreger, “Magento PHP's Developer Guide,” Published by Packt Publishing, UK. ISBN 978-1-78216-306-0

4.        “Mobile web apps vs mobile native app how to make the right choice,” White Papers: Lionbridge.

5.        M.  Murphy, “Beginning Android 3,” Packt Publishing UK.

6.        H.  Guihot, “Pro Android Apps Performance Optimization,” MGH Publication.

7.        V.  Ghorecha,C. Bhatt, “A Guide for Selecting CMS for Web Application Development,” ISSN:2321-7782

8.        L.  Quinn, H.  Gardner-Madras, “Comparing Open Source Content Management System”.

9.        M. Rouse, “Content management system,” Pearson Publication.

10.     Prof.R. A. Soni, “A Study Paper on Android UI,” ISSN:2230-8849






Dhadiwal Kalpesh Paraskumar, Abhishek Pandey, Dharmendra Kumar, Pankaj Kumar, Deepali Javale

Paper Title:

Home Security System

Abstract: Home Security is an important issue everywhere. Now a days   as the possibilities of intrusion are increasing so home security is required. We propose home security system which focus on monitoring home space to detect intruders and the visitors that are visiting our home.  The Android phone is the advantage of the system as it is carried by everyone and used at any place at any instant as compared to personal computer. The user can monitor the home status using the android phone even when the user is not at home. Internet will be the main communication media between the android phone and the home security system.  

Android phone, IR, Raspberry pi, ZigBee.


1.        Mohd Abdul Samad, M.Veda Chary, “Design of Remote Intelligent Smart Home System Based on Zigbee and GSM Technology,” in IJETT,  vol. 4, sept 2013.
2.        Shiu Kumar, “Ubiquitous Smart Home System using Android Application ”in IJCNC, vol 6,Jan 2014.

3.        Rajeev Piyare, Seong Ro Lee, “Smart Home Control and Monitoring System using Smart Phone”, ICCA 2013, ASTL Vol. 24.

4.        Gowthami.T, Dr. Adiline macriga. G, “ Smart Home Monitoring and Controlling System Using Android Phone” ,in IJETAE, Volume 3, Issue 11, November 2013.

5.        Jayashri Bangali ,Arvind Shaligram, “Design and Implementation of Security Systems for Smart Home based on GSM technology ” in International Journal of Smart Home Vol.7, No.6 (2013).






Rohit Prasad, Tejaswini Kar

Paper Title:

Object Pose Estimation Using Least Non Coplanar Feature Points

Abstract: This paper describes the pose estimation of an object using a calibrated camera. The idea is to first calibrate the camera and then implement the algorithm to find the estimated matrices that describes the three dimensional pose of the object. The camera calibration process includes capturing the images and then processing them to find the intrinsic and extrinsic parameters, which are used to estimate the object pose. And object pose estimation is carried out by first finding the corners with the help of Harris feature extraction and then comparing the image and object matrices in the POSIT algorithm and finally eliminating the errors with the help of iterations. The algorithm estimates the pose with a minimum of four non-coplanar points from the acquired image. Both camera calibration and pose estimation processes were implemented using MATLAB® Ver. (R2011a).

POSIT, pose estimation, camera calibration, intrinsic parameters, non-coplanar feature points.


1.        Ivan E. Sutherland: “Three-Dimensional Data Input by Tablet”, Proceedings of the IEEE, Vol. 62, No. 4, April 1974.
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3.        M.A. Abidi and T. Chandra: “A New Efficient and Direct Solution for Pose Estimation Using Quadrangular Targets: Algorithm and Evaluation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 5, May 1995.

4.        Takeo Kanade and Carlo Tomasi: “Shape and Motion from Image Streams: A Factorization Method Full Report on the Orthographic Case”, Cornell TR 92-1270 and Carnegie Mellon CMU-CS-92-104, March 1992.

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7.        T. A. Clarke and J. G. Fryer: "The development of camera calibration methods and models", Photogrammetric Record, 16(91): 51–66 (April 1998).

8.        Jean- Yves Bouguet: “Complete Camera Calibration Toolbox for Matlab®”, Computer Vision Research Group, Dept. of Electrical Engineering, California Institute of Technology.

9.        Roger Y. Tsai: “A Versatile Camera Calibration Techniaue for High-Accuracy 3D Machine Vision Metrology Using Off-the-shelf TV Cameras and Lenses”, IEEE Journal of Robotics and Automation, Vol. RA-3, No. 4, August 1987.

10.     David A. Forsyth and Jean Ponce: “Computer Vision: A Modern Approach, Second Edition”, Prentice Hall, ISBN-13: 978-0-13-608592-8.

11.     Roger Y. Tsai: “An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision”, IEEE, 1986.

12.     Guo- Qing Wei and Song De Ma: “A Complete Two-plane Camera Calibration Method and Experimental Comparisons*”, IEEE, 1993.

13.     Azra Fetić, Dinko Osmanković and Davor Jurić: “The procedure of a camera calibration using Camera Calibration Toolbox for MATLAB”, MIPRO 2012, May 21-25, 2012, Opatija, Croatia.

14.     Daniel F. DeMenthon and Larry S. Davis: “Model- Based Object Pose in 25 Lines of Code”, Computer Vision Laboratory, Center for Automation Research, University of Maryland.

15.     Urban Simulation Team: “Virtual Los Angeles Project”, University of California, Los Angeles.

16.     Adnan Ansar and Kostas Daniilidis: "Linear Pose Estimation from Points or Lines", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, May 2003.

17.     Omar Tahri, CJean Marc Alexandre and Cristophe Leroux: "Pose Estimation From less than Sic Non Coplanar Points", Proceedings of the 2006 IEEE International Conference on Robotics and Automation Orlando, Florida - May 2006.

18.     Cong ChenRonghua Luo and Huaqing Ming: "Side View Pose Estimation Of Human From Images Using Prior Knowledge", 2011 4th International Congress on Image and Signal Processing.

19.     Guntae Bae, Sooyeong Kwak, Hyeran Byun and Daeyong Park: " Method to improve efficiency of human detection using scalemap", ELECTRONICS LETTERS 13th February 2014 Vol. 50 No. 4 pp. 265–267






Ashwini C. Bolkote, M. B. Tadwalkar

Paper Title:

An Analysis of Psoriasis Skin Images

Abstract: In this study a skin disease diagnosis system was developed and tested. The system was used for diagnosis of psoriases skin disease. Present study relied on both skin color and texture features (features derives from the GLCM) to give a better and more efficient recognition accuracy of skin diseases. In this study feed forward neural networks is used to classify input images to be psoriases infected or non psoriasis infected.

Skin recognition, skin texture, computer aided disease diagnosis, texture analysis, neural networks, Psoriasis.


1.        Anuradha Balasubramaniam, Anbu Selvi, “An Efficient Approach to Segment Scaling in Psoriasis Skin Image,” International Journal of Advanced Research in Computer Engineering * Technology (IJARCET) Vol. 3, Issue 3, March 2014.
2.        Juan Lu*, Ed Kazmierezak, Jonathan H, Manton, and Rodney Sinclair, “Automatic Segmentation of scaling in 2D Psoriasis Skin Images,” IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL, 32, NO.4, APRIL 2013.

3.        K. Busse and M. John Koo, “Residents’ reports: Goeckerman combination therapy with low dose acitretin for HCV-associated psoriasis,” Practical Dermatol., pp. 25–26, Apr. 2010.

4.        C. Paul, P.-A. Gourraud, V. Bronsard, S. Prey, E. Puzenat, S. Aractingi, F. Aubin,M. Bagot, B. Cribier, P. Joly, D. Jullien, M. Le Maitre,M.-A. Richard-Lallemand, and J.-P. Ortonne, “Evidence-based recommendations to assess psoriasis severity: Systematic literature review and expert opinion of a panel of dermatologists,” J. Eur. Acad. Dermatol. Venereol., vol. 24, pp. 2–9, 2010.

5.        E. Puzenat, V. Bronsard, S. Prey, P. Gourraud, S. Aractingi, M. Bagot, B. Cribier, P. Joly, D. Jullien, M. Le Maitre, C. Paul,M. Richard-Lallemand, J. Ortonne, and F. Aubin, “What are the best outcome measures for assessing plaque psoriasis severity? A systematic review of the literature,” J. Eur. Acad. Dermatol. Venereol., vol. 2, pp. 10–16, Apr. 2010.

6.        M.Meier and P.B. Sheth, “Clinical spectrumand severity of psoriasis,” Curr. Probl. Dermatol., vol. 38, pp. 1–20, 2009.

7.        R. Achanta, F. J. Estrada, P. Wils, and S. Süsstrunk, “Salient region detection and segmentation,” in Proc. Int. Conf. Comput. Vis. Syst., 2008, pp. 66–75.

8.        L. Ma and R. C. Staunton, “Optimum Gabor filter design and local binary patterns for texture segmentation,” Pattern Recognit. Lett., vol. 29, pp. 664–672, 2008.

9.        L. Naldi and D. Gambini, “The clinical spectrum of psoriasis,” Clin. Dermatol., vol. 25, no. 6, pp. 510–518, 2007.

10.     P. V. de Kerkhof and K. Kragballe, “Psoriasis: Severity assessment in clinical practice. Conclusions from workshop discussions and a prospective multicentre survey of psoriasis severity,” Eur. J. Dermatol., vol. 16, no. 2, pp. 167–171, Mar. 2006.

11.     J. Taur, G. Lee, C. Tao, C. Chen, and C. Yang, “Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques,” IEEE Trans. Syst.,Man, Cybernet., Part B: Cybernet., vol. 36, no. 2, pp. 390–402, Apr. 2006.

12.     Z. Kato and T. chuen Pong, “A Markov random field image segmentation model for color textured images,” Image Vis. Comput., vol. 24, pp. 1103–1114, 2006.

13.     D. D.Gómez, B. K. Ersbøll, and J.M.Carstensen, “S.H.A.R.P: A smart hierarchical algorithm to register psoriasis,” in Int.Wkshp Syst., Signals Image Process., Sep. 2004, pp. 43–46.

14.     S. E. Grigorescu, N. Petkov, and P. Kruizinga, “Comparison of texture features based on Gabor filters,” IEEE Trans. Image Process., vol. 11, no. 10, pp. 1160–1167, Oct. 2002.

15.     M.-C. Su and C.-H. Chou, “A modified version of the k-means algorithm with a distance based on cluster symmetry,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 674–680, Jun. 2001.

16.     J. Röing, R. Jacques, and J. Kontinen, “Area assessment of psoriatic lesions based on variable thresholding and subimage classification,” in Vis. Interface ’99, May 1999, pp. 303–311.

17.     M. Ahmed, S. Yamany, N. Mohamed, A. Farag, and T. Moriarty, “A modified fuzzy -means algorithm for bias field estimation and segmentation of MRI data,” , IEEE Trans. Med. Imag., vol. 21, no. 3, pp. 193–199, Mar. 2002.

18.     L. Zhang, “Hierarchical block-based disparity estimation using mean absolute difference and dynamic programming,” in Proc. Int. Wkshp Very Low Bitrate Video Coding, 2001, pp. 114–118.

19.     T. Malisiewicz and A. A. Efros, “Improving spatial support for objects via multiple segmentations,” in Br. Mach. Vis. Conf., Warwick, U.K., Sep. 2007, pp. 55.1–55.10.






Sangita V. Darandale, M. B. Tadwalkar

Paper Title:

EEG Signal Sorting & ANN Principal Features Analysis for Brain Disease Diagnosis

Abstract: Automatic support system for EEG signal classification for brain diseases diagnosis is proposed in this study. The artificial neural network is used to diagnosis disease like epilepsy by classifying the EEG Signal. The manual analysis of the signal require more time.  Also there is a requirement of intensive trained person, to minimize the diagnostics errors. Back Propagation Network with data processing techniques was employed. Decision is based on two stages: feature extraction using Principal Component Analysis and the classification using Back Propagation Network (BPN).The training performance as well as classification accuracy is evaluated for Back Propagation classifier performance. Back Propagation Network classifier is used for high speed and accuracy.

EEG signals, Classification algorithms, Back propagation network, epilepsy disease.


1.        K. Sivasankari† and K. Thanushkodi “An Improved EEG Signal Classification Using   Neural Network with the Consequence of ICA and STFT”VOL-9,1060-1071,JEET 2014
2.        Neelam Rout “Analysis and Classification Technique     Based On     ANN for EEG Signals” International Journal of Computer Science and Information Technologies, Vol.5, , 2014

3.        Kottaimalai R, EEG “Signal Classification using Principal Component Analysis with   Neural Network in Brain Computer Interface Applications ”  2013 IEEE International Conference on EmergingTrends in Computing, Communication and Nanotechnology (ICECCN 2013)

4.        Sharan reddy, P.K. Kulkarni, “EEG signal classification for Epilepsy  Seizure Detection using Improved  approximate Entropy”   International Journal of Public Health Science (IJPHS) Vol. 2, No.     1, March 2013.

5.        Baha Sen,Musa Peker, “Novel approaches for automated epileptic   diagnosis using FCBF  selection and classification   algorithms”, Turkish journal of Electrical Engineering and  Computer  Science,2013

6.        Zarita Zainuddin1, Lai Kee Huong1, Ong Pauline1,2 “Reliable   epileptic seizure detection using an improved wavelet  neural   network ”  Australasian Medical Journal [AMJ 2013]

7.        Satyanarayana Vollala & Karnakar Gulla]“Automatic detection of  epilepsy EEG using  Neural Networks” International Journal of  Internet Computing ISSN No: 2231 – 6965, Vol.1, ISS- 3 2012

8.        Kavita Mahajan, M. R. Vargantwar, Sangita M. Rajput “Classification of EEG using PCA, ICA and Neural Network ” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958,      Volume-1, Issue-1, October 2011

9.        S. Aydn,” Determination of auto agressive model orders for seizure detection.”Turkish   Journal of Electrical Engineering & Computer Science,Vol.18,pp.23-30,2010

10.     T. Fathima, M. Bedeeuzzaman, O. Farooq, U.K. Yusuf, “Wavelet based features for epileptic seizure detection", MES Journal of Technology and Management, pp. 108-112, 2010

11.     Forrest Sheng Bao , Donald Yu-Chun Lie, and Yuanlin Zhang “ A New Approach to Automated  Epileptic Diagnosis Using EEG and Probabilistic Neural Network”CSAI,2008

12.     N. Kannathal, M. Choo, U. Acharya, P. Sadasivan, Entropies for detection of epilepsy in EEG", Computer Methods and Programs in Biomedicine, Vol. 80, pp. 187,94, 2005.

13.     V. Srinivasan, C. Eswaran, N. Sriraam, Artical neural network based epileptic detection using time domain and  frequency domain features", Journal of Medical systems, Vol. 29, pp. 647/660, 2005.

14.     A. Subaşı, A. Alkan, E. Köklükaya, “Wavelet neural network classification of EEG signals”, Teknoloji, Vol. 7, pp. 71-80, 2004 (in Turkish).

15.     M. Akın, M.A. Arserim, M.K. Kıymık, İ. Türkoğlu, “A new approach for diagnosing epilepsy by using wavelet transform and neural networks”, Proceedings of the 23rd Annual EMBS International Conference, İstanbul, pp. 1596-1599, 2001






Harsha Jain, Ashwini Andurkar, Vandana Koli

Paper Title:

Doppler Spectrogram Calculation Using DSP Processor & MATLAB

Abstract: Doppler echocardiography is a method for detecting the direction and velocity of moving blood vessels within the heart. It uses Doppler’s shift principle that there is change in frequency of ultrasonic waves relative to the motion of moving blood cells. The change in frequency is proportional to the velocity of blood cells. Most of the Doppler ultrasound systems employ quadrature demodulation technique at the detection stage. The information concerning flow direction encoded in the phase relationship between in-phase and quadrature phase channels is not obvious at this stage. A method based on the complex fast Fourier transform (CFFT) and complex wavelet transform (CWT) has been described. It eliminates the intermediate processing stages by mapping directional information in frequency and scale domain respectively. These methods are implemented in real time using commercially available digital signal processors TMS320C6713DSK along with Code Composer Studio 3.1 and also used MATLAB 7.4.0(R2007a) software. This system has been designed as open research platform, which can be programmable with variety of novel algorithms for studying improved and resolved spectrograms to obtain accurate diagnostic details in the future. 

Directional Doppler, Doppler Echocardiography, CFFT, CWT, TMS320C6713DSK.


1.        D.Balasubramaniam, D.Nedumaran, “Doppler Spectrogram Calculation Using CFFT Algorithm In A Digital Signal Processor Based System”, 2009 Third Asia International Conference on Modeling & Simulation.
2.        D.C.Reddy, “Biomedical Signal Processing Principles & Techniques”, the Tata McGraw-Hill Publication New Delhi, 2006.

3.        J. Solano, M.Fuentes, A. Villar, J. Prohias,, F.Garcia-Nocetti, “Doppler Ultrasound Blood Flow Measurement System for Assessing Coronary Revascularization”, Universidad Nacional Autonoma de Mexico, IIMAS Mexico D.F.04510.

4.        Joseph A. Kisslo, MD and David B. Adams, RDCS, “Principles of Doppler Echocardiography and The Doppler Examination #1”,  pdf document.

5.        “MATLAB Wavelet Toolbox”, pdf document.

6.        MATLAB Help.

7.        N. Aydin, and D.H.Evans, “Implementation of Directional Doppler Techniques using a Digital Signal Processor”, MBEC, Electrocardiography, Myocardial Contraction and Blood Flow Supplement, 1993.

8.        Nizamettin Aydin, Lingke Fan and David H Evans, “Quadrature-to-Directional Format Conversion of Doppler signals Using Digital Methods”, Physiol. Meas. 15(1994) 181-199. Printed in the UK.

9.        Nizamettein Aydin, IEEE member, and Hugh S. Markus, “Directional Wavelet Transform in the Context of Complex Quadrature Doppler Signals”, IEEE Signal Processing Letters, VOL.7, No.10, October 2000.

10.     Rulph Chassing, Donald Ray, “Digital Signal Processing and Applications with TMS320C6713 & TMS320C6416 DSK”, 2nd edition, Wiley India Edition.

11.     R.S.Khandpur, “Handbook of Biomedical Instrumentation”, 2nd edition.

12.     http:// www.youtube.com/doppler echo signal
13.     http://www.wikipedia.com/Doppler effect- Wikipedia, the free encyclopedia.html






Vandana Y. Koli, Ashwini G. Andurkar, Harsha S. Jain

Paper Title:

Automatic Blood Vessel Segmentation in Retinal Image Based on Mathematical Morphology

Abstract: Retinal blood vessels detection or segmentation is important according to ophthalmologist. To diagnose the retinal disease or to avoid the vision loss, regular checkup of retinal blood vessels is necessary. This regular checkup provides the information about the changes of blood vessels. This changes are like swelling, narrowing of blood vessels etc. The automatic segmentation of blood vessels helps in the diagnosis of retinal diseases. In this work two approaches are used for vessel segmentation. First one is segmentation using morphology with Thresholding and second is segmentation using morphology with Fuzzy-C-Means clustering. Both approaches are unsupervised methods. The segmentation result of these methods is approximately same but there is one difference. The first one technique provides better result for major vessel while second provides good result for minor vessels. This system designed to resolve the problem of ophthalmologist by developing two algorithms.  

 Retinal Blood Vessels, Fuzzy-C-Means, Mathematical Morphology, Thresholding.


1.       Alaudin Bhuiyan, Baikunth Nath, Joselite Chua and Ramamohanarao Kotagiri “Blood vessel segmentation from color retinal images using Unsupervised texture classification”,IEEE transaction, ICIP 2007.
2.       B . Sindhu, J. B. Jeeva, “Automated Retinal Vessel Segmentation using Morphological operation and Threshold”, IJSE,Vol. 4, issue 5, may 2013 ISSN 2229-5518

3.       James C  Bezdek, Robert Ehrlich, William Full “FCM: The Fuzzy C Means clustering algorithm ” Computers and Geosciences vol. 10 No. 2-3, pp. 191-203, 1984.

4.       Image Database, http://www.isi.uu.nl/Research.

5.       Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, Digital Image Processing Using MATLAB, Mc Graw Hill, kundli 131 028, Haryana.

6.       “MATLAB Image Processing  Toolbox”, pdf document.

7.       Uyen T. V. Nguyen, Kotagiri Ramamohanrao, “A Quantitative Measure for Retinal Blood Vessel Segmentation evaluation”, IJCVSP,1(1), 1-8(2012)

8.       Vuda S. Rao, Dr. S Vidyavathi “comparative investigations and performance analysis of fcm and mfpcm algorithms on Iris data” Indian Journal of Computer Science and Engineering Vol 1 No 2, 145-151