INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: firstname.lastname@example.org Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.4 No.10 August 2014
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014
From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. The invention of blue light-emitting diodes that are central to the energy-efficient lights illuminating homes, offices and electronic displays has earned three scientists the 2014 Nobel Prize in physics. Isamu Akasaki of Meijo University and Nagoya University in Japan, Hiroshi Amano of Nagoya University and Shuji Nakamura of the University of California, Santa Barbara will split the roughly $1.1 million prize. “If we look at the landscape of technology, there’s the transistor and the integrated circuit, and then there’s the blue LED,” says Fred Schubert, an electrical engineer at the Rensselaer Polytechnic Institute in Troy, N.Y. The blue LED is the crucial ingredient for white LED lamps, which are rapidly replacing incandescent bulbs. Edison’s classic invention uses a filament that emits light in a range of colors that together look white. But a lot of electricity gets wasted heating the filament rather than generating light. LEDs are far more energy efficient because they use electrons to generate photons. LEDs are made out of layers of semiconductors, materials similar to the ones in computer chips. Some layers have an excess of electrons; others have a deficit, leading to the emergence of positively charged holes where electrons. Combine the electrons and holes in a concentrated area and they emit light. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE
Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. S.Pannirselvam M.Sc., M.Phil., Ph.D Associate Professor & Head, Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. S.Prasath M.Sc., M.Phil., Ph.D Assistant Professor, Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.
Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014 Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE
Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering
Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,RuaItapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences,
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014 Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
Contents Image Segmentation & Performance Evaluation Parameters by Nirmal Patel, Rajiv Kumar GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG.GGGGGGGGG.
Image Segmentation & Performance Evaluation Parameters Nirmal Patel Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, India Gurgaon Institute of Tech. & Mgmt., Gurgaon, India Email: email@example.com Rajiv Kumar GITM Gurgaon Institute of Tech. & Mgmt., Gurgaon, India Email: firstname.lastname@example.org Abstract--- Images are widely used in all walks of life. Image use in daily needs insists upon a robust and result oriented way of analyzing images across all domains. Let it be remote sensing pictures, medical science critical image analysis, biometrics, it has proven to be indispensable to come out with a cut piece of sophisticated algorithms to dispense huge load of image processing requirements. Here we list out some of the effective ways of differentiating image pixels. Image segmentation is the way to carry out segregation of pixels as per desired criteria. Further parameters are figured out to evaluate the performance of these techniques. Keywordsâ€” Segmentation, image processing, evaluation parameters, clustering.
I. INTRODUCTION Image is termed as a two-dimensional representation of pictures consisting of values in numerical form. In digital form, the image comprises of data premised as pixels at the lowest level. Pixels are the smallest individual element in an image, holding finite, discrete, quantized values that represent the brightness, intensity or gray level at any specific point.  Image processing refers to the analysis of the image and obtaining desired results. Medical science image interpretation, remote sensing pictures, face recognition, pattern matching are among useful applications of image processing. Image segmentation, morphological operations, edge detection, image enhancement and restoration are several operations in image processing.  We have categorized the paper in four sections. First section brings in light the concept of image segmentation. Second section presents various segmentation techniques used these days. Third section describes the performance evaluation parameters for the segmentation algorithms. Fourthly, the paper is concluded with a note of revisit. II. IMAGE SEGMENTATION Image segmentation is a primary process of image analysis in any situation. It is a process of subdividing an image into constituents regions or objects so that the minute details of the image are read or analyzed.  Whether one has to classify stained cells in a tissue or dislocation of constituent areas depending upon vegetation in an image of a big city taken from space, image segmentation is the most useful tool available in MATLAB image processing product.
Fig. 1. An example of image segmentation of an image of a stained tissue (a) original image , (b),(c),(d) are segmented images
III. SEGMENTATION TECHNIQUES There exist many different types of segmentation techniques in literature but there is no particular method which can be applied on different types of images which would generate same result. Algorithm development for one class of images may not always be applied to other class of images . There is a constant challenge to develop a general segmentation algorithm that could address a large section of issues concerned with edges, clustering of pixels, region similarities, pixel classification depending on desired criteria. Here we present some of the most widely used methods for segmentation. A. Thresholding Thresholding method proposes the use of a threshold value set by the administrator. If a pixel value lies is equal to or greater than the threshold value the pixel could be picked up else left. It implies the picked pixel constitutes the foreground and the left ones form the background part of the image. This operation could be classified as local, global and adaptive application. The local thresholding refers to use of threshold values over small region. Global thresholding is considered when intensity variation of object and background is
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014 conviniently distinct. Further if the threshold value is depending upon the spatial co-ordinates, adaptive thresholding could be used. Histogram shape-based, entropy-based, spatial methods and local methods are some algorithms based on thresholding. B. Edge Based The pixels reflecting abrupt change in intensity are known as edge pixels. These pixels define different regions in the image. For detecting these pixels two techniques are used namely, Gray Histogram Technique and Gradient Based Method. These methods require a balance between detecting accuracy and noise immunity in practice. In the former method, segmentation is done on the basis of a threshold value. Firstly depending upon the color or intensity a histogram is calculated from the entire pixel in the image, and then edges are located on the basis of contours and valleys in image are located . While in the latter one, convolving gradient operators with the image is applied. Gradient is defined as change in magnitude in the image while traversing from one end to another. If the gradient magnitude is high, then there is a possibility of rapid transition from one region to another. Then these are pixels which form edges and linking of these edges is done to form closed boundaries to result regions. In these methods commonly used edge detection operators used in gradient based method are Sobel, Prewitt and Roberts. Thus, edge detection algorithms are suitable for images that are simple and noise-free as well often produce missing edges or extra edges on complex and noisy images. C. Region Based Region based segmentation refers to the way of forming regions in the image based on some seed pixels. Seed pixels are the main pixels to which other pixels belonging to the same region must adhere to in terms of gray level within a certain threshold range. 1.Region Growing: Region growing means starting from the seed pixels outright towards surrounding ones in order to include or exclude them on the basis of some membership condition. These conditions could instantiate as pixel strength, gray level surface or color. Since the regions are grown up on the basis of the principle, the image information itself is significant. For example, if the principle were a pixel intensity threshold value, information of the histogram of the image would be of use, as one could use it to regulate a suitable threshold value for the region membership condition. The regions need the distance in the entire image because each point has to fit to one region or another. To get regions at all, one must define a property that will be accurate for each region that is defined. The property selected for the region should be unique in order to differentiate between any two regions. So that pixels of two regions don't get mixed and could easily be segregated. If these principles are met, then the image is correctly segmented into regions. Region growing segmentation can take course either of the two - by mean or by max-min variance. Blocks of 2X2 pixels size could be taken for region growing mechanism. Taking the max-min instance, the maximum, minimum intensity variation is taken into consideration and adjacent regions whose max-min variance is within an acceptance of the seed blocks or seed pixels are
amalgamated. The new region is now the seed and the process goes on. Another round of repetition of checking adjacent regions and comparing them with the acceptable range is carried out. And thus, region growing segmentation is implemented across the entire image. While considering the mean instance, region growing is performed by comparing the mean values of the blocks with that of the seed blocks. 2. Split and Merge: For the merge and split process blocks of 16X16 pixels size are taken. If the max-min difference of a block is near by the max-min difference of its neighbors, then the blocks are merged . A threshold value is required for this purpose. This threshold defines which blocks can be merged into a particular block and which others could be split into smaller blocks based on the difference between the maximum and minimum intensities in every block. A block is split in partial if the max-min difference of the block outstands the threshold. The process is pulled over recursively until, no blocks satisfy the conditions to be split or merged. Thus a block whose max- min variance exceeds the threshold will continue to be split until the max-min variance of the subsequent block(s) are within the threshold. There needs to be the lower check on upto what extent the blocks to be splitted. In case this is specified the algorithm remains consistent and doesn't take a chance to retire into unwanted situation producing awkward results. This could be done by specifying the smallest block dimensions that could be generated through splitting. D. Clustering Clustering is the way of grouping a set of objects in such a way that objects in the same group called a cluster, are more similar in some sense or another to each other than to those in other groups or clusters. A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. An image can be grouped based on keyword (metadata) or its content (description). A variety of clustering techniques have been introduced to make the segmentation more effective. 1) K-Means Clustering: In K-means algorithm data vectors are grouped into predefined number of clusters. At the beginning the centroids of the predefined clusters is initialized randomly. The dimensions of the centroids are same as the dimension of the data vectors. Each pixel is assigned to the cluster based on the closeness, which is determined by the Euclidian distance measure. After all the pixels are clustered, the mean of each cluster is recalculated. This process is repeated until no significant changes result for each cluster mean or for some fixed number of iterations. . Steps: • K clusters are formed by partitioning the dataset and the data points are randomly assigned to the clusters resulting in clusters that have roughly the same number of data points. • For all data point the distance from the data point to each cluster is calculated. • Leave the data point where it is only if it closes to its own cluster. If the data point is not close to its own cluster, shift it into the closest cluster.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014 Repeat the above step until a complete pass through all the data points‘ results in no data point moving from one cluster to another cluster. On this point the clusters are stable and the clustering process ends. 2) Fuzzy C-Means Clustering: The fuzzy clustering method is devised to confront the real situations when some issues may erupt due to partial spatial resolution, intensity of overlapping, poor contrast, noise and intensity in homogeneities. Taking into consideration the two main clustering strategies - the hard and the fuzzy clustering, fuzzy clustering scheme is a soft segmentation method and has been generally studied and successfully applied in image clustering and segmentation. Due to robust characteristics for ambiguity and can retain much more information than hard segmentation methods. Fuzzy c-means (FCM) algorithm is most popularly used than other fuzzy clustering techniques. Steps: • Set values for c, m and e. Where 'c' is number of clusters, 'm' is fuzzy factor and 'e' is stopping condition. • Do initialization of fuzzy partition matrix. • Set the loop counter b. • Calculate the c cluster centers. • Calculate the membership matrix. • Set b= b+1 and go to step 4. •
and in different sets in Y. (c), the number of pairs of elements in S that are in the same set in X and in d ifferent sets in Y.( d), the number of pairs of elements in S that are in different sets in X and in the same set in Y The Rand index I is,
Where, a + b is the number of agreements between X and Y and c + d is the number of disagreements between X and Y. The Rand index has a value between 0 and 1, with 0 indicating that the two data clusters do not agree on any pair of points and 1 indicating that the data clusters are exactly the same. F. Variation of Information (VOI)) The Variation of Information (VOI) metric defines the distance between two segmentations as average conditional entropy of one segmentation given the other, and thus measures the amount of randomness in one segmentation which cannot be explained by the other . Suppose we have two clustering (a division of a set into several subsets) X and Y where is: Then the variation of information between two clustering Where, H(X) is entropy of X and I(X, Y) is mutual information between X and Y. The mutual information of two clustering is the loss of uncertainty of one clustering if the other is given. Thus, mutual information is positive and bounded by
Fig.2.Image segmentation methods
IV. EVALUATION PARAMETERS Having segmentation evaluation measures is an efficient way to analyze the performance of existing and future algorithms. Segmentation evaluation metrics can be divided into boundary –based and region –based methods. Before one gets to know the performance of an algorithm, knowing comprehensively the definitions of these metrices is inevitable. Various performance parameters used for evaluation of image segmentation are as follows. E. The Rand index (RI) Rand index counts the fraction of pairs of pixels who’s labeling are consistent between the computed segmentation and the ground truth averaging across multiple ground truth segmentation . The Rand index or Rand measure is a measure of the similarity between two data clusters. Given a set of n elements and two partitions of S to compare, we define the following(a), the number of pairs of elements in S that are in the same set in X and in the same set in Y. (b), the number of pairs of elements in S that are in different sets in X
G. Global Consistency Error (GCE)) The Global Consistency Error (GCE) measures the extent to which one segmentation can be viewed as a refinement of the other . Segmentations which are related are considered to be consistent, since they could represent the same image segmented at different scales. Segmentation is simply a division of the pixels of an image into sets. The segments are sets of pixels. If one segment is a proper subset of the other, then the pixel lies in an area of refinement, and the error should be zero. If there is no Subset relationship, then the two regions overlaps in an inconsistent manner. The formula for GCE is as follows,
Where, segmentation error measure takes two segmentations S1 and S2 as input, and produces a real valued output in the range [0::1] where zero signifies no error. For a given pixel pi consider the segments in S1 and S2 that contain that pixel. It measures the extent to which one segmentation can be viewed as a refinement of the other. Segmentations which are related in this manner are considered to be consistent, since they could represent the same natural image segmented at different scales.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.10 OCTOBER 2014 H. Boundary Displacement Error (BDE) The Boundary Displacement Error (BDE) measures the average displacement error of one boundary pixels and the closest boundary pixels in the other segmentation . Particularly, it defines the error of one boundary pixel as the distance between the pixel and the closest pixel in the other boundary image.
I. Mean absolute error (MAE) Mean absolute error is the average of the difference between predicted and actual value in all test cases; it is the average prediction error. MAE indicates that higher the values of MAE mean the image is of poor quality. Mean absolute error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. J. Peak signal to noise ratio (PSNR) It gives quality of image in decibels (db).and is given as
.Jay Acharya, Sohil Gadhiya and Kapil Raviya, “Segmentation Techniques for Image Analysis: A Review”, International Journal of Computer Science and Management Research, Vol 2 Issue 1, January 2013, Pg. 1218-1221. . Mehmet Sezgin and Bulent Sankur, “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging 13(1), 146–165 (January 2004). .Vishal B. Langote and Dr. D. S. Chaudhari, “Segmentation Techniques for Image Analysis”, International Journal of Advanced Engineering Research and Studies (IJAERS)/Vol. I/ Issue II/January-March, 2012. .Vijay Kumar Chinnadurai, Gharpure Damayanti Chandrashekhar,” Improvised levelset method for segmentation and grading of brain tumors in dynamic contrast susceptibility and apparent diffusion coefficient magnetic resonance images”, International journal of engineering science and technology , vol.2(5), 2010, 1461-1472. . S.L.A Lee, A.Z.Kouzani, E.J.Hu,” Empirical Evaluation of segmentation algorithms for lung modeling”, 2008 International conferences on systems, man and cybernetics (SMC 2008). . Allan Hanbury, Julian Stottinger,” On segmentation evaluation metrics and region count”. . Monika Xess and S.Akila Agnes”, Survey On Clustering Based Color Image Segmentation And Novel Approaches To Fcm Algorithm”, IJRET: International Journal of Research in Engineering and Technology eISSN: 23191163 pISSN:2321-7308.  Yong Yang,Image Segmentation by Fuzzy C-Means Clustering Algorithm with a novel penalty term, School of Information Management Jiangxi University of Finance and Economics Nanchang 330013, P.R. China.
Fig. 2. Various Segmentation Parameters
V. CONCLUSION Towards the end we want to recollect that we stated the concept of image segmentation. We drove through various segmentation methods prevailing these days. Also we concluded with the performance parameters useful in judging an algorithm's utility. A useful lay of knowledge lastly is hopeful in extending the reader's repository of intellect. ACKNOWLEDGMENT Authors wish to thank the MATLAB workshop team for organising a spectacular event at the MD University, Rohtak, India. REFERENCES . P. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson Education, Asia. .http://www.mathworks.in/help/pdf_doc/images/images_tb. pdf
International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711) October 2014 Issue Vol.4 No.8