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In the recent years, usage of data mining techniques to statistically analyze the performance of candidates in academics or national level examinations is in increase. The development of predictive analytics tools and their applications are also in the rise. This paper reports on the mechanism of the proposed prediction model that predicts the performance of a candidate appearing for national level examinations. The proposed Performance Prediction Model (PPM) is designed as a framework comprising of data classification and ranking of dataset, computation of correlation coefficient that measures the dependency among the variables and prediction using linear regression. The performance of PPM is validated on UGC-NET (2016) dataset. Based on the observed correlation between Paper-II and Paper-III marks, PPM predicts the score of a candidate in Paper-III with reference to the scored marks in Paper-II. The accuracy of the predicted data is recorded as 88 per cent. The illustrative visualizations presented in this article depict the performance analysisof the candidates in Paper-I, Paper-II and Paper-III.

Performance Prediction Model (PPM), Classification, Ranking, Correlation Coefficient, Linear Regression Model.

[1] N. Bhargava, G. Sharma, R. Bhargava, M. Mathuria, “Decision Tree Analysis on J48 Algorithm for Data Mining”. Journal of Advanced Research in Computer Science and Software Engineering, Vol.3, Issue.6, pp. 1114, 1115 June 2013.
[2] M. Gupta, N. Agarwal, “Classification Techniques Analysis”, National Conference on Computational Instrumentation, pp. 128, 129 March, 2010.
[3] S.B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques”, Department of Computer Science and Technology, University of Peloponnese, Greece, pp.249, July 16, 2007.
[4] G. De’ath, K. E. Fabricius, “Classification and Regression Trees: A Powerful Yet Simple Technique for Ecological Data Analysis”, Ecological Society of America, Vol. 81, No. 11, pp. 3178, 2000.
[5] T.S.Korting, "C4.5 algorithm and Multivariate Decision Trees." Image Processing Division, National Institute for Space Research – INPE SaoJose dos Campos – SP, Brazil.
[6] Y.Y. Song and Y. LU, “Decision tree methods: applications for classification and prediction”, Shanghai Archives of Psychiatry, Vol.27, No.2.
[7] I.Jenhani, N. B. Amor, Z. Elouedi, “Decision Tree as probabilistic classifiers”,In the proceedings of the International Journal of Approximate Reasoning, Vol.48, Issue.3, pp.784, August 2008.
[8] Harwati, A. Sudiya, “Application of Decision tree approach to Student Selection Model- A Case Study”. In the proceedings of the IOP Conference Series: Materials Science and Engineering, Vol.105, Conference.1, pp.1, 2016.
[9] V. Bewick, L. Cheek and J. Ball. “Statistics review 7: Correlation and Regression”, Published in Critical care, pp.451, 2003.
[10] W. Medhat, A. Hassan, H. Korashy, “Sentiment analysis algorithm and applications: A survey”, In the proceedings of Ain Shams Engineering Journal, Vol.5, Issue.4, pp.1093-1113, December 2014.
[11] F. M. B. N. Hanif, P. Saptawati, “Correlation analysis of user influence and sentiment on Twitter data” In the proceedings of the International Conference Data and Software Engineering (ICODSE), Bandung, Indonesia, 2014.
[12] V. A. Kharde, S.S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey Techniques”, International Journal of Computer Applications, Vol.139, No.11, pp.5, April 2016.
[13] H.E. Brogden, “On the interpretation of the correlation coefficient as a measure of predictive efficiency”. In the proceedings of the Journal of Educational Psychology, 1946.
[14] G. Sudhamathy, “Credit Risk Analysis and Prediction Modelling of Bank Loans Using R” International Journal of Engineering and Technology, Vol. 8, No.5, pp.1954, 2016.
[15] H. Hamsa, S. Indiradevi, J. J. Kizhakkethottam, “Student academic performance prediction model using decision tree and fuzzy genetic algorithm”, In the proceedings of the Procedia Technology, Vol.25, pp.326, 2016.
[16] E. Seidal, S. Kutieleh, “Using predictive analytics to target and improve first year student attrition”, In the proceedings of Australian Journal of Education, Vol. 61, Issue.2, pp. 200, August 2017.
[17] A. S. Issac, “Statistics”, SCITech Publications,Chapter 6, 2011.

P. Shanmugavadivu, P. Haritha, Ashish Kumar, "Performance Prediction Model for National Level Examinations", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.292-297, 2018.

In this paper, we propose a brain portion extraction method using single seeded region growing technique and morphological operations. Segmentation requires initial seed point selection, which increases computational cost and execution time. To overcome these problems a single seeded region growing method for image segmentation is proposed. Starts by selecting a seed point at center pixel within the image as the initial seed and grow regions to extract brain portion in Magnetic resonance image. Brain portion is iteratively grown by comparing all unallocated neighbouring pixels to the region. Finally Morphological operations erosion, dilation, and holes filling are performed to extract the fine brain. The performance of the method is estimated using the Jaccard and Dice similarity coefficients. Proposed method was tested with IBSR of brain images and had accurately segmented the brain regions which are better than the existing methods such as Brain Extraction Tool (BET), Brain Surface Extractor (BSE).

Brain Extraction,Image Segmentation,Single seeded region growing,Morphological operations

[1] Park G. and Lee C., “Skull Stripping Based on Region Growing for Magnetic Resonance Images,” NeuroImage, vol. 47, no. 4, pp. 13941407, 2009.
[2] Somasundaram K. and Kalavathi P., “Analysis of Imaging Artifacts in MR Brain Images,” Oriental Journal of Computer Science and Technology, vol. 5, no. 1, pp. 135-141, 2012.
[3] Somasundaram K. and Kalavathi P., “A Novel Skull Stripping Technique for T1weighted MRI Human Head Scans,” in Proceeding of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, Mumbai, pp. 1-8, 2012.
[4] Somasundaram K. and Kalavathi P., “Brain Tissue Segmentation in MR Brain Images using Otsu’s Multiple Thresholding Technique,” in Proceeding of International Conference on Computer Science and Education, Colombo, pp. 639-642, 2013.
[5] R.C. Gonzalez, and R.E. Woods, Digital Image Processing, Addison-Wesley Publishing Company, 1992
[6] Rafael C.Gonzalez and Richard E.Woods “Digital Image Processing” 2011,P.762-770.
[7] Atkins M.S. K, Law, B., Orchard, J.J and Rosenbaum, W.L. Difficulties of T1 brain MRI Segmentation techniques.proc.SPIE,2002,4684.1837-1844.
[8] Haacke, E.M., Brown, R.W., Thompson, M.R and Imaging:Physical Principles and Sequence Design, 1999 (John Wiley&Sons, NewYork).
[9] Segonne,F.,Dale,A.M.,Busa,E.,Glessner,M.,Salat,D.,Hahn,H.K and Fischl,B.A hybrid Approach to the skull stripping problem in MRI.NeuroImage,2004,22,1060-1075.
[10] Park, G.J and Lee, C.Skull stripping based on region growing for magnetic resonance Images. NeuroImage, 2009, 47, 1394-1407.
[11] Somasundaram, K.and Kalavathi, P.Contour-based brain segmentation method for Magnetic resonance imaging human head scans J.Comput.Assist.Tomogr,.2013 37,353- 368.
[12] Justice, R.K.,Stokely,E.M.,3-D Segmentation of MR brain images using seeded region Growing ,IEEE International Conference on Bridging Disciplines for Biomedicine.1996.
[13] Om Prakash Verma et al, A Simple Single Seeded Region Growing Algorithm for Colour Image Segmentation using Adaptive Thresholding.2011 International Conference On Communication Systems and Network Technologies.
[14] http://pdfs.semanticscholar.org/e150/02bbcfe0c 3ee5f0202cb6125e2c3e4124.Pdf.
[15] http://www.ijctee.org/files/VOLUME2ISSUE1/ IJCTEE_0212_18.pdf.
[16] Jun Tang, A Color Image Segmentation algorithm based on Region Growing, China School of Electronic Engineering 2010.

K.Somasundaram, J.Helen Mercina, S. Magesh, T. Kalaiselvi, "Brain Portion Extraction Scheme using Region Growing and Morphological Operation from MRI of Human Head Scans", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.298-302, 2018.

In this research article, a novel segmentation technique to extract the brain portion from Magnetic Resonance Image (MRI) of human head scans based on Chan – Vese and Morphological Operations is proposed. First we extracted the rough brain portion using Chan – Vese method and the applying the morphological Operations to segment the fine brain portion. The initial Contour is drawn at the brain image and then propagated to achieve the boundary of the brain image. Then using morphological operation like Erosion and Dilation, the remaining portions were segmented. Comparison of the numerical results obtained from the extracted images, with the standard manual skull stripping gold images and significant results are presented here. The performance of the method is estimated using the Jaccard and Dice similarity Coefficients. The IBSR datasets of brain images are used to evaluate the efficiency of the proposed method and the results shown that which are better than the existing methods such as Brain Surface Extractor (BSE) and Brain Extraction Tool (BET).

Brain Extraction,Image Segmentation,Magnetic Ressonance Image (MRI),Chan - Vese,Morphological operations

[1] Eskildsen S., Coupe P., Fonov V., Manjón J., Leung K., Guizard N., Wassef S., Ostergaard L., and Collins D., “BEaST: Brain Extraction Based on Non-Local Segmentation Technique,” NeuroImage, vol. 59, no. 3, pp. 2362-2373, 2012.
[2] Shattuck D., Sandor-Leahy S., Schaper K., Rottenberg D., and Leahy R., “Magnetic Resonance Image Tissue Classification using a Partial Volume Model,” NeuroImage, vol. 13, no. 5, pp. 856-876, 2001.
[3] Smith S., “Fast Robust Automated Brain Extraction,” Human Brain Mapping, vol. 17, no. 3, pp. 143-155, 2002.
[4] Mumford D. and Shah J., “Optimal Approximation by Piecewise Smooth Function,” Communication on Pure and Applied Mathematics, vol. 42, no. 1, pp. 577-685, 1989.
[5] Park G. and Lee C., “Skull Stripping Based on Region Growing for Magnetic Resonance Images,” NeuroImage, vol. 47, no. 4, pp. 13941407, 2009.
[6] Sadananthan A., Zheng W., Chee W., and Zagorodnov V., “Skull Stripping using Graph Cuts,” NeuroImage, vol. 49, no. 1, pp. 225-239, 2010.
[7] Somasundaram K. and Kalavathi P., “Medical Image Contrast Enhancement based on Gamma Correction,” International Journal of Knowledge Management and E-Learning, vol. 3, no. 1, pp. 15-18, 2011.
[8] Somasundaram K. and Kalavathi P., “Medical Image Binarization using Square wave Representation,” Control Computation and Information System, vol. 140, no. 2, pp. 152-158, 2011
[9] Somasundaram K. and Kalavathi P., “Analysis of Imaging Artifacts in MR Brain Images,” Oriental Journal of Computer Science and Technology, vol. 5, no. 1, pp. 135-141, 2012.
[10] Somasundaram K. and Kalavathi P., “ContourBased Brain Segmentation Method for Magnetic Resonance Imaging Human Head Scans,” Journal of Computer Assisted Tomography, vol. 37, no. 3, pp. 353-368, 2013.
[11] Somasundaram K. and Kalavathi P., “A Novel Skull Stripping Technique for T1weighted MRI Human Head Scans,” in Proceeding of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, Mumbai, pp. 1-8, 2012.
[12] Somasundaram K. and Kalavathi P., “Brain Tissue Segmentation in MR Brain Images using Otsu’s Multiple Thresholding Technique,” in Proceeding of International Conference on Computer Science and Education, Colombo, pp. 639-642, 2013.
[13] Somasundaram K. and Kalavathi P., “Skull Stripping of MRI Head Scans based on Chan – Vese Active Contour Model,” in Proceeding International Journal of Knowledge Management & e-Learning, Vol.3 , No. 1 , January-June 2011 , pp. 7-14.
[14] Somasundaram K. and Kalavathi P., “Segmentation of Brain from MRI Head Images Using Modified Chan-Vese Active Contour Model,” in Proceeding The International Arab Journal of Information Technology, Vol. 13, No. 6A, 2016.
[15] R.C. Gonzalez, and R.E. Woods, Digital Image Processing, Addison-Wesley Publishing Company, 1992

K. Somasundaram, R. Yashaswini, S. Magesh, T. Kalaiselvi, "Brain Portion Extraction Scheme Using Chan - Vese and Morphological Operation from MRI of Human Head Scans", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.303-307, 2018.

Image steganography has become a vibrant research area due to increase in digital image transmission over untrusted network. Generally, after recovering the hidden data from a stego image, the host image data cannot be reconstructed perfectly. This is a main challenge for applications demanding lossless host image recovery. In this paper, a new lossless reversible data hiding technique is proposed based on differences of adjacent pixels for embedding data and has more hiding capacity compared to existing methods. The number of hiding bits that can be embedded into an image equals the number of pixels related with the peak point. The performance of the algorithm has been evaluated with hiding capacity and peak signal to noise ratio (dB). The experimental results show that the host image and hidden information can be exactly retrieved from the stego image.

Reversible data hiding, Lossless reconstruction, Information security

[1] I. J. Cox, J. Kilian, T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” in IEEE Trans. on Image Processing, vol. 6. No. 12, pp. 1673-1687, Dec. 1997.
[2] J. Huang and Y. Q. Shi, "An adaptive image watermarking scheme based on visual masking," IEE Electronic Letters, vol. 34, no. 8, pp. 748-750, April 1998.
[3] C. W. Honsinger, P. Jones, M. Rabbani, and J. C. Stoffel, “Lossless recovery of an original image containing embedded data,” US Patent: 6,278,791, 2001.
[4] M. Goljan, J. Fridrich, and R. Du, “Distortion-free data embedding,” Proceedings of 4th Information Hiding Workshop, pp. 27-41, Pittsburgh, PA, April, 2001.
[5] Jun Tian, "Reversible data embedding using a difference expansion," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 8, pp. 890-896, Aug. 2003.
[6] M.MaryShanthi Rani and K.Rosemary Euphrasia, ”Data Security Through QR Code Encryption and Steganography”, Advanced Computing: An International Journal (ACIJ), Vol.7, No.1/2,pp.1-7 March 2016.
[7] M. Mary Shanthi Rani and K.Rosemary Euphrasi, ”A Comparative Study On Video Steganography in Spatial and IWT Domain”, International Conference on Advances in Computer Applications,2016
[8] K. A. Navas, S. Archana Thampy, and M. Sasikumar “EPR Hiding In Medical Images for Telemedicine” International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering Vol: 2, No: 2, 2008.
[9] M.Maryshanthi Rani, S.Lakshmanan, “An Integrated Method of Data Hiding and Compression of Medical Images” International Journal of Advanced Information Technology (IJAIT) Vol. 6, No. 1, February 2016
[10] M.MaryShanthi Rani and S.Lakshmanan, ”Region Based Data Hiding in Medical Images”,International Journal of Advanced Research in Computer Science,vol.8 ,No.3 March – April 2017
[11] M.MaryShanthi Rani, G.Germine Mary and K.RosemaryEuphrasia “Multilevel multimedia security by Integrating Visual Cryptography and Stegnography Techniques”, Computational intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol.412, pp.403-412, December, 2015.
[12] C Nagaraju and S S ParthaSarath, “Embedding ECG and patient information in medical images” IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09-11, 2014, Jaipur, India.
[13] Mohit Gupta, Praveen, Kr. Tripathi, “Data Hiding Using Blind Algorithm of Steganography”, International Journal for Research in Applied Science & Engineering Technology, Vol. 4, Issue IX, Sep 2016.
[14] M.MaryShanthi Rani and G.Germine Mary, “Compression of VC Shares”, International Journal of Computational Science and Information Technology (IJCSITY), Vol.4, Issue.1, pp.57-65, February 2016.
[15] Mary Shanthi Rani, M. Germine Mary, G. and Rosemary Euphrasia,K. “High level Multimedia Security by incorporating Steganography and Visual Cryptography” International Journal of Innovations &Advancement in Computer Science IJIACS - ISSN 2347 – 8616 Volume 4, Special Issue September 2015.
[16] Anupriya Sohaland Dr.Lalita Bhutani, ‘Unique Steganography Technique Using Wavelet Transform and Neural Network”, International Journal of Latest Trends in Engineering and Technology, vol .5,Issue 1,jan 2015.
[17] E Divya and P Raj Kumar, "Steganographic Data Hiding using Modified APSO", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, Issue.7, pp.37-45, 2016.
[18] M. Mary Shanthi Rani and K.Rosemary Euphrasia, “Dynamic Hiding of message in RGB Domain based on Random Channel Indicator”, International Journal of Applied Engineering Research, Vol.10, Issue.76, pp.478-483, 2015.
[19] P.Thiyagarajan, V. Natarajan, G. Aghila, V. PrasannaVenkatesan and R. Anitha, “Pattern Based 3D Image Steganography”, 3D Research, Springer, Vol.4, Issue.1, pp.1-8, 2013
[20] M.Mary Shanthi Rani, S.Lakshmanan and P.Saranya, “Video Steganography using Mid-Prime and Discrete Wavelet Technique”, International Journal of Computer Engineering and Applications, Vol. 11, Issue 8, pp.180-190, August 17.
[21] M.Mary Shanthi Rani, S.Lakshmanan and G.Deepalakshmi, “Video Steganography using Mid-Point Circle Algorithm and Spatial Domain Technique”, International Journal of Engineering and Techniques, Vol. 4 Issue. 1, Jan – Feb 2018.
[22] Z.Ni, Y.-Q.Shi, N. Ansari, W. Su, “Reversible data hiding”, IEEE Trans. Circuits Syst. Video Technol. 16 (2006) 354–362.
[23] Rajkumar Ramaswamy and Vasuki Arumugam, “Lossless Data Hiding Based on Histogram Modification”, Int. Arab J. Inf. Technol. 9 (2012) 445–451.
[24] M.Mary Shanthi Rani and S.Lakshmanan, “Combination of Reversible Data Hiding and Medical Image Compression”, Journal of Global Research in Computer Science, Vol. 9 Issue. 2, Feb 2018.
[25] R.Anushia devi, Padmapriya Praveenkumar, John bosco Balaguru Rayappan and Rengarajan amirtharajan, ‘Reversible Secret Data Hiding Based on Adjacency Pixel Difference, Journal if artificial Intelligence, Vol.10 Issue. 1 pp.22-31, 2017.

S. Lakshmanan, M. Mary Shanthi Rani, "Lossless Data Hiding Based on Adjacency Pixel Differences", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.308-312, 2018.

Most of the remote sensing images are multispectral image where these images are acquired in the form of several bands that constitute a spectral direction. As large amount of data is represented by multispectral image, a lot of memory space is needed for storage and transmission. Hence, there is big need for compression methods for multispectral images. The prime factor of any image compression method is the redundancy as well as correlation on an image. In this way, the multispectral images having high degree of correlation on spatial domain and redundancy on spectral domain. This leads to conception of several compression methods for these multispectral images. Moreover, every tiny information from multispectral image is very important for efficient processing and so the lossless encoding is always preferable. In this paper, we proposed a hybrid lossless method using Lempel-Ziv-Welch (LZW) and Arithmetic Coding for compressing the multispectral Images. The performance of our method is compared with existing lossless compression methods such as Huffman Coding, Run Length Coding (RLE), LZW and Arithmetic Coding.

Lossless Compression, Multispectral Image, Huffman Coding, LZW, Run Length Coding, Arithmetic Coding

[1] M. Rabbani, and P.W Jones, “Digital image compression techniques”, SPIE Press Vol. 7, 1991.
[2] P. Kalavathi and S. Boopathiraja “A wavelet based image compression with RLC encoder” National Conference on Computational Methods, Communication Techniques and Informatics, pp.289–292, 2017.
[3] J. Douglass, Ventana Medical Systems Inc,” Color image compression via spectral decorrelation and elimination of spatial redundancy”. U.S. Patent 6,944,333
[4] A.M. Rufai, G. Anbarjafari, H. Demirel, “Lossy image compression using singular value decomposition and wavelet difference reduction”., Digital signal processing. Vol.1, Issue 24, pp. 117-124, 2014.
[5] The Sunitha Abburu, and Suresh BabuGolla, “Satellite Image Classification Methods and Techniques: A Review”, International Journal of Computer Applications (0975 – 8887), Vol 119 – No.8, 2015.
[6] T. Markas and J. Reif, “Multispectral image compression algorithms”, Data Compression Conference, pp. 391-400, 1993.
[7] ND. Memon, K. Sayood, SS. Magliveras, “Lossless compression of multispectral image data”, IEEE Transactions on Geoscience and Remote Sensing, March 1994.
[8] D. Huffman, "A Method for the Construction of Minimum-Redundancy Codes", Proceedings of the IRE. Vol. 40, No. 9, pp.1098–1101, 1952.
[9] J.H. Pujar, LM. Kadlaskar” A new lossless method of image compression and decompression using Huffman coding techniques”, Journal of Theoretical & Applied Information Technology,2010.
[10] EL. Hauck, inventor; Intelligent Storage Inc, assignee. “Data compression using run length encoding and statistical encoding”. United States patent US 4,626,829. 1986.
[11] P. Kalavathi, and S. Boopathiraja, “A medical image compression technique using 2D-DWT with run length encoding”, Global Journal of Pure and Applied Mathematics (GJPAM), 2017.
[12] Witten, H. Ian, Neal, M. Radford, Cleary, and G. John, "Arithmetic Coding for Data Compression" (PDF). Communications of the ACM. Vol.6, issue.30, pp.520–540, 1987.
[13] J. Ziv, A. Lempel, “Compression of individual sequences via variable-rate coding”. IEEE transactions on Information Theory, Vol .5, issue.24 pp. 530-536,1978.
[14] www. glovis.usgs.gov

S. Boopathiraja, P. Kalavathi, and S. Chokkalingam, "A Hybrid Lossless Encoding Method for Compressing Multispectral Images using LZW and Arithmetic Coding", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.313-318, 2018.

Multispectral images are taken from remote sensing sensors which are used in wide variety of application including earth observation, distortion management and so on. An interpretation of those images for different type of applications needs to enhance for more accurate processing. In this study, we have taken the multispectral image of LANDSAT dataset which has seven bands. The color composite image is derived through combining different bands of this dataset and it is act as single color composite multispectral image. The enhancement of this multispectral color composite images is done through decorrelation stretching and the performance of this method is explained and compared with other methods such as various histogram based methods.

Multispectral Image, LANDSAT, Image Enhancement, Histogram Equalization, Contrast Enhancement.

[1] N. Hashimoto, Y. Murakami, PA. Bautista, M. Yamaguchi, T. Obi, N. Ohyama, and Y. Kosugi, “Multispectral image enhancement for effective visualization,” Optics express, Vol.9 Issue .19, pp.9315-9329,2011.
[2] Z. Xie, and TG. Stockham, “Toward the unification of three visual laws and two visual models in brightness perception”, IEEE Transactions on Systems, Man, and Cybernetics, vol.19, issue.2, pp.379-387, 1989.
[3] A.k. Bhandari, A. Kumar, and G. K. Singh. "SVD based poor contrast improvement of blurred multispectral remote sensing satellite images.", Third International Conference Computer and Communication Technology (ICCCT), IEEE, pp.156-159. 2012.
[4] R.C. Gonzalez, and Woods,” Digital image processing” 2012.
[5] P. Kalavathi, S. Boopathiraja, and Abinaya, “Despeckling of ultrasound medical images using DW and WP transform techniques”, International Journal of Engineering and Technology (IJET), Vol. 9, issue.3, 2017.
[6] K. Somasundaram, P. Kalavathi, “Medical image contrast enhancement based on gamma correction”, Int J Knowledge Management e-learning. Vol. 3, Issue. 1, pp. 15-18, 2011.
[7] YT. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization”, IEEE transactions on Consumer Electronics, vol.43, issue.1, pp.1-8. 1997.
[8] S. M. Pizer, E. P. Amburn, J. D. Austin, “Adaptive Histogram Equalization and Its Variations”, Computer Vision, Graphics, and Image Processing, vol.39, pp. 355-368, 1977.
[9] G. Adav, S. Maheshwari, and A. Agarwal,” Contrast limited adaptive histogram equalization based enhancement for real time video system”, InAdvances in Computing, Communications and Informatics (ICACCI), pp. 2392-2397, 2014.
[10] AR. Gillespie, and AB. Kahle, RE. Walker, “Color enhancement of highly correlated images. I. Decorrelation and HSI contrast stretches”, Remote Sensing of Environment, vol.20, issue.3, pp.209-35, 1986.
[11] J.M Soha, and A. Schwartz, “Multispectral histogram normalization contrast enhancement Proc”, 5th Canadian Symposium on Remote Sensing, pp. 86-93, 1978.
[12] AR. Gillespie “Enhancement of multispectral thermal infrared images: Decorrelation contrast stretching”, Remote Sensing of Environment. Vol.42, issue.2, pp.147-55, 1992.
[13] www. glovis.usgs.gov

S. Boopathiraja, P. Kalavathi, M. Geethalakshmi, "Performance Analysis of Multispectral Color Composite Image Enhancement Technique using Decorrelation Stretching and Histogram Equalization Methods", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.319-323, 2018.

Breast cancer is reported as the second most dangerous diseases in the world. Early detection of breast cancer is recorded to reduce the mortality rate by 30 to 60 percent. Digital mammography is a widely accepted as a non-invasive modality for the early detection of breast cancer, based on the abnormalities in the form of lesions, tumours and micro calcifications. The computer-based breast cancer screening involves segmentation of the abnormalities which are characterized by abrupt change in the pixel intensity, against the neighbourhood pixels. This paper presents a computationally simple and efficient enhancement technique that uses the principle of Otsu thresholding. The thresholds value for segmentation is chosen from the histogram bins of the input mammogram. The insignificant segmented partitions are discarded using morphological operations. The proposed method based on Histogram Bins based Otsu Thresholding (HBOT) is proved to segment the suspicious region accurately, as evidenced in the visual perception.

Breast Cancer Detection ,Microcalcifications ,Mammogram Segmentation, Histogram Bins ,Otsu Thresholding

[1] Zaheeruddin,Z.A.Jaffery and Laxman Singh, “Detection and Shape Feature Extraction of Breast Tumor in Mammograms” , Proceedings of the World Congress on Engineering 2012 Volume 2 WCE 2012, July 4 - 6,pp 231-236,2012.
[2] .P.Shanmugavadivu, Lakshmi Narayanan, S.G., “Detection of Microcalcifications in Mammogram using Statistical Measures based Region Growing”, SPIE, Proceedings of the International Conference on Communication and Electronics System Design ICESD 2013,vol. 8760, p.no.87601M-1 -6, 2013.
[3] P.Shanmugavadivu, P., Lakshmi Narayanan, S.G., “Segmentation of Microcalcifications in Mammogram Images using Intensity - Directed Region Growing”, Proceedings of the International Conference on Computer Communication and Informatics ICCCI 2013.
[4] Shanmugavadivu, P., Lakshmi Narayanan, S.G.: Segmentation of Micro calcification Regions in Digital Mammograms using Self-Guided Region Growing. In: Proceedings of the International Conference on Emerging Trends in Science Engineering and Technology INCOSET 2012, pp. 274–279 (2012).
[5] Y. Ireaneus Anna Rejani and Dr.S.Thamarai Selvi, “Breast Cancer Detection Using Multilevel Thresholding”, International Journal of Computer Science and Information Security, Volume. 6, No.1, 2009.
[6] Snehal A. Mane and Dr. K. V. Kulhalli, “ Mammogram Image Features Extraction and Classification for Breast Cancer Detection”, International Research Journal of Engineering and Technology, e-ISSN: 2395 -0056 Volume: 02 Issue: 07, P-ISSN: 2395-0072, Oct 2015.
[7] P.Shanmugavadivu, V.Sivakumar, “Segmentation of Masses in Digital Mammograms using Fractal-Bound Computing Technique for Breast Cancer Prognosis”, International Journal of Applied Engineering Research, ISSN 0973-4562, pp. 23187 – 23192, Vol. 10 No.31, 2015.
[8] P.Shanmugavadivu, V.Sivakumar, (2013) “Segmentation of pectoral muscle in mammograms using Fractal method”, ICCCI-2013, IEEE Xplore, ISBN: 978-1-4673-2906-4, pp. 1-6.
[9] Pradeep N, Girisha H, Sreepathi B and Karibasappa K, “Feature Extraction Of Mammograms”, International Journal of Bioinformatics Research, ISSN: 0975–3087 & E-ISSN: 0975–9115, Volume 4, Issue 1, pp.-241-244, 2012.
[10] Moumena Al-Bayati and Ali El-Zaart, “Mammogram Images Thresholding for Breast Cancer Detection Using Different Thresholding Methods”, Advances in Breast Cancer Research, 2, 72-77, 2013.
[11] M.Vasantha,Dr.V.Subbiah Bharathi and R.Dhamodharan, “Medical Image Feature, Extraction, Selection and Classification”, International Journal of Engineering Science and Technology, Volume 2(6), 2071-2076, 2010.
[12] Anu Appukuttan and Sindhu L., “Breast Cancer-Early Detection and Classification Techniques: A Survey”, International Journal of Computer Applications (0975 – 8887) Volume 132 – No.11, December2015.
[13] K.Vennila, k.Sivakami and R.Padmapriya, “Detection of Mass in Digital Mammograms”, International Journal of Computer Applications (0975 – 8887) Volume 104 – No.5, October 2014.
[14] Inam ul Islam Wani, M. C Hanumantharaju and M. T Gopalakrishna ,“Review of Mammogram Enhancement Techniques for Detecting Breast Cancer”, International Journal of Computer Applications (0975 – 8887), International Conference on Information and Communication Technologies – 2014.
[15] Belal K. Elfarra and Ibrahim S. I. Abuhaiba, “New Feature Extraction Method for Mammogram Computer Aided Diagnosis”, International Journal of Signal Processing, Image Processing and Pattern Recognition Volume 6, No. 1, February, 2013.

P. Shanmugavadivu, A. Thilshat Barveen, Ashish Kumar, "Breast Cancer Detection in Digital Mammograms using Histogram Bins based Otsu Thresholding", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.324-327, 2018.

Genomics has become a hot research area in medical field for diagnosis of monogenetic disorder identification, pharmaco genetics, targeted therapy, genome editing and personalized medicine. Each human genome consists of 3 billion pairs which are to be effectively stored and transmitted for analysis. This process necessitates the development of novel genomic data compression algorithms. In this paper a referential based method for compressing genomes has been proposed. The input and reference genomes are compared for dissimilarities and further entropy coded to achieve high compression ratio.

FASTA file, Genomic data compression, R-Programming, Huffman coding, BIG DATA.

[1] S. D. Kahn. “On the future of genomic data. Science (Washington)”,vol.331,pp.728–729, 2011.
[2] J. K. Bonfield and M. V. Mahoney, “Compression of FASTQ and SAM format sequencing data”, PLoS ONE,vol.8, issue.3, 2013.
[3] S. Deorowicz, A. Danek, and M. Niemiec. Gdc , “Compression of large collections of genomes”, arXiv preprint arXiv:1503.01624, 2015.
[4] Y. Zhang, L. Li, Y. Yang, X. Yang, S. He, and Z. Zhu. “Light-weight reference-based compression of FASTQ data”, BMC bioinformatics, vol.16, issue.1, pp.188, 2015.
[5] E. S. Lander, et al., “Initial sequencing and analysis of the human genome”, Nature, vol. 409, pp. 860-921, 2001.
[6] S. Kuruppu, S. J. Puglisi and J. Zobel, “Optimized relative Lempel-Ziv compression of genomes”, Proceeding of ACSC 2011.
[7] P.SubrahmanyaandT.Berger, “Asliding window Lempel-Zivalgorithm for differential layer encoding in progressive transmission”, Proc. IEEE Int. Symp. Inf. Theory, Whistler, BC, Canada, pp. 266,995, 1995.
[8] Kwang Su Jung, Nam Hee Yu, Seung Jung Shin, Keun Ho Ryu, “A Compressing Method for Genome Sequence Cluster using Sequence Alignment”, 2008
[9] M.Mary Shanthi Rani, “A New Referential Method for Compressing Genomes” International Journal of Computational Bioinformatics and In Silico Modeling, Research Article Open Access, Vol. 4, issue.1, pp.592-596 2015.
[10] Biji CL and AchuthsankarS.Nair, “Benchmark dataset for Whole Genome sequence compression”, pp.1545-5963, 2016.
[11] RabiaArshad,AdeelSaleem and Danista Khan, “ Performance Comparison of Huffman Coding and Double Huffman Coding ”,978-1-pp.5090-2000,2016.
[12] Komal Sharma, Kunal Gupta, “Losseless Data Comperssion Techniques and Their Performance”, ieee, 2017.
[13] Kakoli Banerjee and A.Prasad, “Reference Based Inter Chromosomal Similarity based DNA sequence Compression algorithm”, ,ieee, 2017

M. Mary Shanthi Rani , S. Jegatheesh Chandra Bose , "Reference Based Genomic Data Compression Using R Programming", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.328-331, 2018.

Image compression is a technique which reduces the storage requirements of an actual image with fewer bits. Especially, the high dimensional images need a lot because of the exponential rate of its contained information. As multispectral images are represented in the form of different bands, it is three-dimensional in nature and demands larger memory spaces. There are several lossy and lossless compression methods are available for these types of images and the lossless one is more preferable. But, the problem is that the lossless methods on multispectral images yields better quality images but lack in the compression performance. Hence, there is need for optimal compression method that incorporate both the quality and compression performance. In this paper, we proposed a near lossless compression method for multispectral images. Three-Dimensional Discrete Wavelet Transform is used for decomposition and the Huffman coding followed by thresholding is used for encoding. The results of our proposed method for the multispectral LANDSAT images are discussed and compared with other existing methods in terms of PSNR and SSIM.

Near Lossless Compression, Multispectral Image, LANDSAT, 3D-DWT, Huffman Coding

[1] Ashok K. Rao, “Multispectral Data Compression Using Interband Prediction”, United States Patent, Patent Number: 5,513,12., 1996.
[2] M. Schroder, H. Rehrauer, K Seidel, M. Datcu, “Interactive learning and probabilistic retrieval in remote sensing image archives”, IEEE Trans. Geosci. Remote Sensing., Vol: 38, No. 5, pp. 2288–2298, 2000.
[3] S. Gupta, A. Gersho, “Feature predictive vector quantization of multispectral”, IEEE Trans. Geosci. Remote Sensing., Vol. 30, No.1, pp.491–501, 1992
[4] G.R. Canta, G. Poggi, “Compression of multispectral images by address-predictive vector quantization”, Signal Process Image Commun., Vol. 11, pp147–159, 1997.
[5] M. J. Ryan and J.F. Arnold, “The lossless compression of AVIRIS images by vector quantization” IEEE Trans. Geoscience and Remote Sensing, Vol. 35, 1997.
[6] G.R. Canta, and G. Poggi, “Kronecker-product gain-shape vector quantization for multispectral and hyperspectral image coding”, IEEE Trans. Image Process., Vol. 7, pp. 668–678, 1998.
[7] S. Nichols, H. Kim,A., A.Humos, and H. J. Cho, “A performance evaluation on DCT and wavelet-based compression methods for remote sensing images based on image content,” in Proceedings of the 17th International Conference on Geoinformatics, vol. 1, pp. 454–458, 2009.
[8] Chang and Lena, “Multispectral image compression using
[9] Jungwoo Lee, “Optimized quadtree for Karhunen–Loeve ransform in multispectral image coding”, IEEE Trans. Image Process. Vol. 8 No. 4, pp. 453–461, 1999.
[10] J.M. Shapiro and M. Czigler, “Multispectral KLT-wavelet data compression for Landsat thematic mapper images”, Proceedings of the Data Compression Conference, Snowbird, Utah, pp. 200–208, 1992.
[11] P.L. Dragotti, G. Poggi, K. Seidel, and R.P. Ragozini, “Compression of multispectral images by three-dimensional SPIHT algorithm”, IEEE Trans. Geosci. Remote Sensing Vol. 38 No. 1, pp. 416–428, 2000.
[12] P. Kalavathi, and S. Boopathiraja, “A medical image compression technique using 2D-DWT with run length encoding”, Global Journal of Pure and Applied Mathematics (GJPAM), 2017.
[13] P. Kalavathi and S. Boopathiraja “A wavelet based image compression with RLC encoder” National Conference on Computational Methods, Communication Techniques and Informatics, pp.289–292, 2017.
[14] M. T. Heideman, D. H. Johnson, C. S. Burrus, "Gauss and the history of the fast Fourier transform", IEEE ASSP Magazine. Vol. 1, No.4, pp. 14–21, 1984. doi:10.1109/MASSP.1984.1162257.
[15] Ahmed, N.; Natarajan, T.; Rao, K. R. (January 1974), "Discrete Cosine Transform", IEEE Transactions on Computers, Vol. 23, No. 1, pp. 90–93, doi:10.1109/T-C.1974.223784.
[16] Wu B., Zhu J., Najm F, "Dynamic Range Estimation". IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 25, No. 9, pp. 1618-1636, 2006.
[17] S. Mallat, “A Wavelet Tour of Signal Processing”, 2nd ed. San Diego, CA: Academic, 1999.
[18] Akansu, Ali N. Haddad and Richard A., “Multiresolution signal decomposition: transforms, subbands, and wavelets’, Boston, MA: Academic Press, ISBN 978-0-12-047141-6. 1992.
[19] Huffman, D. (1952). "A Method for the Construction of Minimum-Redundancy Codes". Proceedings of the IRE. Vol. 40, No. 9, pp.1098–1101, doi:10.1109/JRPROC.1952.273898.

S. Boopathiraja, P. Kalavathi, "A Near Lossless Multispectral Image Compression using 3D-DWT with application to LANDSAT Images", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.332-336, 2018.

Alzheimer’s disease is one of the brain disorders caused due to shrinkage of brain tissues, particularly the White Matter and Grey Matter. Many imaging modalities are used to acquire the image of human brain, in order to diagnose the disorder. MRI is widely used technique to detect Alzheimer’s disease. In this research work, we aimed to develop a computational method to quantify the brain tissue loss in MRI human head scans. In this proposed method, we used particle swam optimization (PSO) technique to find the optimal cluster centroids to segment the brain tissue. These segmented White Matter and Gray matter are further analysed to quantify the Alzheimer’s disease. The output of this method is quantitatively and qualitatively evaluated by the similarity measures – Jaccard, and Dice based on the expert segmented results.

Brain Tissue Segmentation, Clustering, Particle Swarm Optimization, MRI Images, Grey Matter and White Matter

[1] K.Somasundaram and P.Kalavathi, “Brain Segmentation in Magnetic Human Head Scans using Multi-Seed Region Growing”, Imaging Science Journal, Vol.62,No.5, Pp.273-284, 2014.
[2] K.Somasundaram and P.Kalavathi, “ A Novel Skull Stripping Technique for T1-weighted MRI Human Head Scans”, ACM Digital Library, Pp. 1-8, 2012.
[3] K.Somasundaram and P.Kalavathi, “Skull Stripping of MRI Head Scans based on Chan-Vese Active Contour Model”, International Journal of Knowledge Management & e-learning Vol.3, No.1, Pp.7-14, 2011.
[4] A. Chinnu, “MRI Brain Tumor Classification Using SVM and Histogram Based Image Segmentation”, International Journal of Science and Research, Vol.4, No.4, Pp.1647-1650, 2015.
[5] P.Kalavathi, “ Brain Tissue Segmentation in MR Brain Images Using Otsu’s Multiple Thresholding Technique”, IEEE Xplore Digital Library, Pp.638-642, 2013.
[6] K.Somasundaram and P.Kalavathi, “A Hybrid Method for Automatic Skull Stripping of Magnetic Resonance Images (MRI) of Human Head Scans”, IEEE Xplore Digital Library, Vol.18, No.5, Pp.1-5, 2010.
[7] T. Law, H. Ltoh, H. Seki, “Image Filtering Edge Detection and Edge Tracing Using Fuzzy Reasoning”, IEEE Transactions on Pattern Analysis and Machine Learning Vol.18, No.5, Pp.481-491, 1996.
[8] N.Sharma, A.K. Ray, “Computer Aided Segmentation of Medical Images Based on Hybridized Approach of Edge and Region Based Techniques”, Proceedings of International Conference on Mathematical Biology, Mathematical Biology Recent Trends by ANAMAYA Publishers, Pp.150-155, 2006.
[9] P.Kalavathi , S.Naganandhini, S.Boopathi Raja and A.Arul Annis Christy, “Detection of Alzhemier’s Disease in MRI Head Scans Using Spatial Fuzzy Clustering with Level Set Method”, International Journal of Engineering Science and Mathematics, Pp.68-74, 2017.
[10] N.Sharma, A.K.Ray, S.Sharma, K.K. Shukla, S.Pradhan, and L.M. Aggarwal, “Segmentation and Classification of Medical Images using Texture- Primitive Features: Application of BAM-Type Artificial Neural Network”, International Journal of Medical Physics, Vol. 33, No. 3, pp. 119-126, 2008.
[11] F.J.Martinez-Murcia, .J.M. Gorriz, J.Ranmirez, A.Ortiz, A Spherical brain mapping of MR images for the dectection of Alzheimer`s disease, Current Alzheimer Research, Vol. 13, No. 5, pp. 575-588, 2016.
[12] Shira K, “Amygdata in Alzheimer‘s Disease”,InTechOpen, 2012.
[13] Kannan.S.R, Ramathilagam.S, Pandiyarajan.R and Sathya.A, “Fuzzy Clustering Approach in Segmentation of T1-T2 brain MRI”, International Journal of Recent Trends in Engineering, Volume 2, Number 1, pp. 157-160, 2009.
[14] Abinaya.K.S, Pandiselvi.T, “Brain Tissue Segmentation From Magnetic Resonance Image Using Particle Swarm Optimization Algorithm”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue 3, March-2014, Pg. 404-408.
[15] T.Kalaiselvi, N.Kalaichelvi and P.Sriramakrishnan, “Knowledge Based K-Means Clustering System for Segmenting Brain Tissues from MRI Head Scans”, Computational Methods, Communication Techniques and Informatics, Pp-145-150, 2017.
[16] Kaufman L, Rousseeuw P.J, “Finding Group Data, An Introduction to Cluster Analysis”, John Wiley & Sons, Inc., Canada , 1990.
[17] Pena J.M, Lozano J.A, Larranaga P, “An Empirical Comparison of Four Initialization methods for the K-means Algorithm”, Pattern Recognition Letters, Vol.20, No.10, Pp-1027-1040, 1999.
[18] K.Somasundaram and P.Kalavathi, “Contour-Based Brain Segmentation Method for Magnetic Resonance Imaging Human Head Scans”, Journal of Computer Assisted Tomography, Vol. 37, No.3, Pp. 203-214, 2010.
[19] Sudipta Roy, Samir Kumar Bandyopadhyay, “ A New Method of Brain Tissue Segmentation From MRI with Accuracy Estimation”, ELSEVIER, Procedia Computer Science, Vol.85, Pp-362-369, 2016.
[20] ZhiguangQin, Fei wang, Zhe Xido, Tian Lan, Yi Ding, “ Brain Tissue Segmentation With the GKA Method in MRI”, IEEE International Conference on Signal and Image Processing, Pp-273-276, 2016.
[21] James C. Church, Yixin Chen, and Stephen V. Rice “A Spatial Median Filter for Noise Removal in Digital Images”, Southeastcon, 2008.
[22] T.Sun and Y. Nuevo, “Detail Preserving median based filters in image processing”, Pattern Recognition, Vol.15, pp.341-347, 1994.
[23] Kajal Gautam and Dr. Rahul Singhai, “ Color Image Segmentation using Particle Swarm Optimization in Lab Color Space”, International Journal of Engineering Development and Research, Volume 6, Issue 1, ISSN:2321-9939, Pp.341-347, 1994.
[24] http://www.cma.mgh.harvard.edu/ibsr/index.html
[25] http://sve.bmp.ucla.edu/instructions/m

S. Naganandhini and P. Kalavathi, "White Matter and Gray Matter Segmentation in Brain MRI Images Using PSO Based Clustering Technique", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.337-341, 2018.