ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING
SEPTEMBER 2017 VOL-7 NO-09
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.09 SEPTEMBER 2017, IMPACT FACTOR: 1.04
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.7 NO.09 SEPTEMBER 2017, IMPACT FACTOR: 1.04
International Journal of Innovative Technology & Creative Engineering Vol.7 No.09 September 2017
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.09 SEPTEMBER 2017, IMPACT FACTOR: 1.04
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. Teaching methods for students in the classroom have to remember facts, but they also have to apply them. Some research efforts to enhance learning zero in on methods to strengthen memory and recall, while others bolster students’ abilities to stay on task, think more fluidly and mentally track and juggle information . The science behind student learning is so far based on carefully controlled studies, primarily with college students. Moving from the lab to a classroom, with all its disruptions and distractions, is key for pinning down what works, under what conditions and for whom. In the process of tweaking some of the most promising tools and strategies for classroom use, educators hope to find ways to help low-performing students gain skills that already pay off for their more successful peers. The efforts described here draw on new, innovative training methods to boost learning in K-12 classrooms. Higgins calls them “great examples” of the work under way. The trick for younger learners found to provide cues to help recall, without making the task too easy. 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. 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. 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 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 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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.7 NO.09 SEPTEMBER 2017, IMPACT FACTOR: 1.04 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 School,RuaItapeva, 474 (8° andar)01332-000, São 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, 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 Human Skin Texture Analysis using Image Processing Techniques G.Balakrishnan & A.vennila .…………………………………….
Human Skin Texture Analysis using Image Processing Techniques G.Balakrishnan Associate Professor & Head, Department of Computer Science, Navarasam Arts and Science College for Women, Erode,Tamil Nadu, India. A.Vennila M.Phil Research Scholar, Department of Computer Science, Navarasam Arts and Science College for Women, Erode,Tamil Nadu, India. Abstract- The skin properties like skin dryness, fungus and allergic symptoms i.e. etching kind of problem correlation with skin texture profile is discussed in the proposed work. In the existing scenario, the skin images are analyzed in frequency domain. However, it is observed that the skin color in texture images does not vary over a wide range. Hence, the histogram profile of the skin texture remains almost flat. In the proposed work, we have shifted the skin texture analysis towards the gray level profile analysis. The gray color profile of the skin texture may give fair idea about the skin sensitivity and is a new emerging skin texture analysis tool. In the proposed work, skin gray color profile has been taken as the input parameter in order to ascertain the skin profile. In the proposed work, Gray Level Co-occurrence Matrix of the skin image is computed. The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix. Further, the image entropy and energies are also computed in order to correlate the skin symptoms to the skin texture image. Keywords-GLCM, Image, Texture.
1. INTRODUCTION According to dermatologist, the skin texture has close relation with the individual’s diet, hormones, hydration and any allergic symptoms. Therefore, by analyzing the skin texture by acquiring the skin texture image by exposing the human skin to imaging devices, the skin’s health may be defined. Texture analysis in image processing is an important tool in analyzing the image of textural nature. The skin texture is the appearance of the skin smooth surface. To the features of this texture, many factors are occurring, for instance diet and hydration, amount of collagen and hormones, and, of course, skin care. As skin ages, it becomes thinner and more easily damaged, with the appearance of wrinkles. The deterioration is also accompanied by a darkening of skin color for an over absorption of the natural coloring pigment, melanin, by the top most cell layer in skin. The skin texture also depends on its body location. In the case of image processing, we have to consider the fact that texture appearance is changing with image recording parameters, that are camera, illumination and direction of view, a problem common to any real surface. The task to have a quantitative evaluation of the skin features is quite complex, as in all the cases where image analysis must be applied to surfaces with irregular non-periodic patterns.
In the digital image processing, several methods have been developed to classify images and define statistical distances among them, with the aim to decide whether, in a set of many images, there exist some which are close to any arbitrary image previously encountered. The texture discrimination can be obtained by choosing a set of attributes, the texture features, which account for the spatial organization of the image. 2. RELATED WORKS A practical skin color and texture analysis/synthesis technique is introduced for this E-cosmetic function. Shading on the face is removed by a simple color vector analysis in the optical density domain as an inverse lighting technique. The image without shading is analyzed by a previously introduced technique that extracts hemoglobin and melanin components by independent component analysis. The comparison shows an excellent match between the synthesized and actual images of changes due to tanning and alcohol consumption. Grain size and anisotropy are evaluated with proper diagrams. The possibility to determine the presence of pattern defects is also discussed. The skin color image is decomposed to the four texture components by multi- resolution analysis using wavelet transform. A variety of skin images with different conditions of skin color and texture are created in a linear combination of the texture components. Experimental results show good separation of skin textures by wavelet analysis and realistic synthesized images. To make improvement in this regard, we propose a new texture analysis synthesis framework that combines two main ideas. Firstly, in material space we decompose the texture contents into units with "basic shape" and 'feature vector ". Based on this, the space spanned by a set of sampled textons is constructed to help introduce additional changes upon textons. Secondly, in pattern space, using the idea of 'feature texture “acquired from texture swatch for different properties especially for distribution rules of textons, we may capture and manipulate the global structure flexibly. Texture refers to visual patterns or spatial arrangement of pixels that regional intensity or color alone cannot sufficiently describe. Researchers have proposed numerous methodologies to automatically analyze and recognize textures, from deriving texture energy measures using a set of simple masks to using Gabor filters, for several image analysis applications, including texture classification and segmentation.
iv. Histogram Computation of the enhanced image The filtering method introduced here is applied to dermoscopic skin image in a non-linear manner and allows v. Computation of GLCM Matrix of skin texture selective image filtering. This feature is highly desirable due to image the fact that in most cases of computer aided diagnostic, input vi. Computation of Contrast images need to be pre-processed (e.g. for brightness vii. Computation of Entropy normalization, histogram equalization, contrast enhancement, viii. Computation of Energy color normalization) and this can results in unwanted ix. Computation of Homogeneity artifacts or simply may require human verification. x. Correlation with Skin Symptoms Introduced method was developed specially to recognize one of the differential structures (pigmented network texture) used for calculating the Total Dermoscopy Score (TDS) of the ABCD rule. A method is proposed that tracks the skin’s recovery optically from an initial strain made using a mechanical indentor, diffuse side -lighting and a CCD videocapture device. Using the blue color plane of the image it is possible to examine the surface topography only, and track the decay of the imprint over time. Two algorithms are discussed for the extraction of information on the skin’s displacement and are analyzed in terms of reliability and reproducibility. The disease conditions are recognized by analyzing skin texture images using a set of normalized Original Image Histogram Equalized Images symmetrical Grey Level Co-occurrence Matrices (GLCM). Fig.1 Fig. 2 GLCM defines the probability of grey level I occurring in the neighborhood of another grey level j at a distance d in direction è. The system is tested using 180 images pertaining to three dermatological skin conditions viz. Dermatitis, Eczema, Urticaria. An accuracy of 96.6% is obtained using a multilayer perceptron (MLP) as a classifier. The geometric, random field, fractal, and signal processing models of texture are presented. The major Fig.3 Fig. 4 classes of texture processing problems such as segmentation, classification, and shape from texture are discussed. The possible application areas of texture such as automated inspection, document processing, and remote sensing are summarized. A bibliography is provided at the end for further reading. The relation between the nonlinear results by Monte Carlo simulation (MCS) and the modified Lambert Beer’s law (MLB) is also clarified, emphasizing the importance of the 4. IMAGE ACQUISITION AND PREPROCESSING absolute values of skin pigments and their influence on the Skin images are acquired using the UV camera in mean path- length used in MLB. Images of oxygenated order to get the deep skin images. The acquired image is in hemoglobin with a newly-developed four wavelength camera jpeg format and is read in matlab using the command imread(). are presented to demonstrate the advantages of a multi The image is now converted to gray image using rgb2gray() wavelength system. function. The gray image is enhanced using the histogram equalization algorithm. Following figure show the result of 3. METHODOLOGY image preprocessing operations: A statistical method of examining texture that considers the spatial relationship of pixels is the gray-level co5. GLCM EXTRACTION occurrence matrix (GLCM), also known as the gray-level A statistical method of examining texture that spatial dependence matrix. The GLCM functions considers the spatial relationship of pixels is the gray-level cocharacterize the texture of an image by calculating how often occurrence matrix (GLCM), also known as the gray-level pairs of pixel with specific values and in a specified spatial spatial dependence matrix. The GLCM functions relationship occur in an image, creating a GLCM, and then characterize the texture of an image by calculating how often extracting statistical measures from this matrix. The work is pairs of pixel with specific values and in a specified spatial divided into following stages: relationship occur in an image, creating a GLCM, and then i. Image Acquisition extracting statistical measures from this matrix. The number of ii. Conversion to Gray Scale Image gray levels in the image determines the size of the GLCM. The iii. Image Enhancement using Histogram gray-level co-occurrence matrix can reveal certain properties Equalization 435
about the spatial distribution of the gray levels in the texture image. For example, if most of the entries in the GLCM are concentrated along the diagonal, the texture is coarse with respect to the specified offset. To create a GLCM, use the graycomatrix function. The graycomatrix function creates a gray-level cooccurrence matrix (GLCM) by calculating how often a pixel with the intensity (gray-level) value i occurs in a specific spatial relationship to a pixel with the value j. By default, the spatial relationship is defined as the pixel of interest and the pixel to its immediate right (horizontally adjacent. After you create the GLCMs, image contrast, energy, coorelation and homogeneity can be computed as: Contrast Measures the local variations in the graylevel co-occurrence matrix. Contrast is 0 for a constant image. Correlation Measures the joint probability occurrence of the specified pixel pairs. Correlation is 1 or -1 for a perfectly positively or negatively correlated image. Correlation is NaN for a constant image. Energy Provides the sum of squared elements in the GLCM. Also known as uniformity or the angular second moment. Energy is 1 for a constant image Homogeneity Measures the closeness of the distribution of elements in the GLCM to the GLCM. 6. COMPUTATION OF ENTROPY The expression of the information entropy of an image is given by:
Where L denotes the number of gray level, pi equals the ratio between the number of pixels whose gray value equals i(0 i L _ 1) and the total pixel number contained in an image. The information entropy measures the richness of information in an image. If pi is the const for an arbitrary gray level, it can be proved that the entropy will reach its maximum.
8. CONCLUSION The main focus of this paper is on analyzing the texture of skin thereby using it to diagnose the skin diseases. Various skin diseases can be analyzed based on the combination of feature vector set of contrast, correlation, energy and homogeneity. From the experimental results discussed above, we infer that the multi-class classification can serve as an effective tool in identifying skin diseases. The future work will be based on developing algorithms to identify various other skin diseases, to improve the overall efficiency and also to further reduce the computational time.
 7. RESULTS For texture characterization, we consider a set of features derived from GLCM matrix: contrast (C),  homogeneity (H), mean (M), energy (N), and variance (V). Images are obtained from Dermnet Skin disease atlas. Dermnet is the largest independent photo dermatology source.  Dermnet provides information on a wide variety of skin conditions. The proposed algorithm produce a skin map of a  given image and highlights patches of skin like pixels. The function reads an image file given using Matlab command imread. A skin map overlayed onto the image with skin pixels marked in blue color is generated by using the GLCM matrix. Following figures shows the output of the algorithm. Once the skin pixels are extracted,, than it is easier to analyze the skin diseases.
REFERENCES Norimichi Tsumura, Nobutoshi Ojima, Kayoko Sato, Mitsuhiro Shiraishi, Hideto Shimizu, Hirohide Nabeshima Syuuichi Akazaki Kimihiko Hori† Yoichi Miyake, “Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin”, IEEE-2012 Motonori Doi, Shoji Tominaga, “Image Analysis and Synthesis of Skin Color Textures by Waveletransform”,1-4244-0069-4/06©2006 IEEE. Yuanting Gu and Enhua Wu, “Feature Analysis and Texture Synthesis”, 978 -1-4244-1579-3/07/2007, IEEE. Ranjan Parekh , “Using Texture Analysis for Medical Diagnosis”, Jadavpur University, India Neil T. Clancya, Martin J. Leahya, Gert E. Nilssonb, Chris Andersonc, “Analysis of skin recovery from mechanical indentation using diffuse lighting and digital imaging”, Proc. of SPIE-OSA Biomedical Optics, SPIE Vol. 6629, 66291G, © 2007.
International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711)