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Mythili, P.
- Is Child Rearing Practice Influence the Child Adaptive Behaviour in School?
Abstract Views :139 |
PDF Views:0
Authors
G. Maheswari
1,
S. Menu Krishnana
2,
V. Murugan
2,
V. S. Vimal Kumar
2,
Selma Thomas
2,
P. Mythili
2,
R. Mani
2
Affiliations
1 Saveetha University, Chennai, IN
2 Dhanvantri College of Nursing, Namakkal (Dt), IN
1 Saveetha University, Chennai, IN
2 Dhanvantri College of Nursing, Namakkal (Dt), IN
Source
International Journal of Nursing Education and Research, Vol 2, No 3 (2014), Pagination: 237-240Abstract
Child rearing behaviors are directly perceived, interpreted and evaluated by the child while adaptive behaviors are indirectly perceived, interpret and evaluated by others. But these two behaviors are directly influence the personal development of the child.
Objectives: The main objectives of this study are to assess the correlation between the child rearing practices and adaptive behaviour in school going children.
Design: A descriptive research design was used for the study.
Setting: SRK matriculation school at Pachampalayam, Namakkal (Dist).
Sample: School going children, SRK matriculation school at Pachampalayam, Namakkal (Dist).
Sampling Technique: Purposive sampling technique was used.
Sampling criteria: School going children in the age group of 6-8 years and both the sex. Data collection: Rating scale was used to collect the data.
Result: From the findings of the study it can be conclude that the highest percentage of childrens were in the age group of 8 years. Most of them were female.Most of the children where the second child, Educational status of the mother were good, illiterate mothers are very rare and most of them were unemployed.60% of the mothers were authoritative in child rearing practice and similarly 60% of the children having well adaptive behaviour.
Conclusion: The study can be concluded that there is a significant positive correlation between the child rearing practices and child adaptive behaviour.
Keywords
Child Rearing Practices, Adaptive Behaviour, School Going Children, Correlation.
- Microarray Image Gridding Using Grid Line Refinement Technique
Abstract Views :174 |
PDF Views:0
Authors
V. G. Biju
1,
P. Mythili
1
Affiliations
1 School of Engineering, Cochin University of Science and Technology, IN
1 School of Engineering, Cochin University of Science and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 5, No 4 (2015), Pagination: 1010-1016Abstract
An important stage in microarray image analysis is gridding. Microarray image gridding is done to locate sub arrays in a microarray image and find co-ordinates of spots within each sub array. For accurate identification of spots, most of the proposed gridding methods require human intervention. In this paper a fully automatic gridding method which enhances spot intensity in the preprocessing step as per a histogram based threshold method is used. The gridding step finds co-ordinates of spots from horizontal and vertical profile of the image. To correct errors due to the grid line placement, a grid line refinement technique is proposed. The algorithm is applied on different image databases and results are compared based on spot detection accuracy and time. An average spot detection accuracy of 95.06% depicts the proposed method's flexibility and accuracy in finding the spot co-ordinates for different database images.Keywords
cDNA Microarray, Gridding, Image Processing, Spot Detection, Grid Line Refinement.- An Improved Fuzzy Clustering Algorithm for Microarray Image Spots Segmentation
Abstract Views :179 |
PDF Views:1
Authors
V. G. Biju
1,
P. Mythili
2
Affiliations
1 Department of Electronics and Communication Engineering, College of Engineering Munnar, IN
2 Division of Electronics Engineering, School of Engineering, CUSAT, IN
1 Department of Electronics and Communication Engineering, College of Engineering Munnar, IN
2 Division of Electronics Engineering, School of Engineering, CUSAT, IN
Source
ICTACT Journal on Image and Video Processing, Vol 6, No 2 (2015), Pagination: 1107-1114Abstract
An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM) to segment the spot foreground (FG) from background (BG). The PFLICM improves fuzzy local information c means (FLICM) algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF), Probability of error (pe), Discrepancy distance (D) and Normal mean square error (NMSE). SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.Keywords
Gridding, Spot Segmentation, Local Information, Spatial Information, Typicality, Clustering, Gene Expression.- Possibilistic Reformed Fuzzy Local Information Clustering Technique for Noisy Microarray Image Spots Segmentation
Abstract Views :285 |
PDF Views:90
Authors
V. G. Biju
1,
P. Mythili
2
Affiliations
1 Department of Electronics and Communication Engineering, College of Engineering Munnar, Munnar-685 612, IN
2 Division of Electronics, School of Engineering, Cochin University of Science and Technology, Cochin-682 022, IN
1 Department of Electronics and Communication Engineering, College of Engineering Munnar, Munnar-685 612, IN
2 Division of Electronics, School of Engineering, Cochin University of Science and Technology, Cochin-682 022, IN
Source
Current Science, Vol 113, No 06 (2017), Pagination: 1072-1080Abstract
The cDNA microarray image provides useful information about thousands of gene expressions simultaneously. This information is used by bioinformatics researchers for diagnosis of different diseases and drug designs. Microarray image spot segmentation using an improved fuzzy clustering algorithm is proposed in this article. The proposed Possibilistic Reformed Fuzzy Local Information C Means (PRFLICM) algorithm is a variant of Possibilistic Fuzzy Local Information C Means (PFLICM) algorithm. The parameters used for testing the proposed algorithm include segmentation matching factor (SMF), probability of error (pe), discrepancy distance (D), normalized mean square error and sum of square distance (SSD). The performance of the algorithm is validated with a set of simulated cDNA microarray images with known gene expression values. From the results of SMF, the proposed PRFLICM shows an improvement of 0.4% and 0.1% for high noise and low noise microarray images respectively when compared to PFLICM algorithm. The proposed algorithm is applied to yeast microarray database (YMD) and is used to find the yeast cell life cycle generated genes. The results show that the proposed algorithm has identified 101 cell life cycle regulated genes out of 104 such genes published in the YMD database.Keywords
Fuzzy Clustering, Gene Expression, Image Processing, Microarray.Full Text
References
- Yang, Y. H., Buckley, M. J., Dudoit, S. and Speed, T. P., Comparison of methods for image analysis on cDNA microarray data. J. Comput. Graph. Stat., 2002, 11(1), 108–136.
- Lehmussola, A., Ruusuvuori, P. and Yli-Harja, O., Evaluating the performance of microarray segmentation algorithms. Bioinformatics, 2006, 22(23), 2910–2917.
- Schena, M., Shalon, D., Davis, R. W. and Brown, P. O., Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science-New York Then Washington, 1995, pp. 467–467.
- Zacharia, E. and Maroulis, D., 3-D spot modeling for automatic segmentation of cDNA microarray images. IEEE Trans. Nanobiosci., 2010, 9(3), 181–192.
- Athanasiadis, E., Cavouras, D., Spyridonos, P., Glotsos, D., Kalatzis, I. and Nikoforidis, G., Segmentation of microarray images using gradient vector flow active contours boosted by Gaussian mixture models. In Second International Conference on Experiments/ Process/System Modeling/Simulation/Optimization (2nd IC-EpsMsO), Athens, Greece, 2007.
- Athanasiadis, E., Cavouras, D., Kostopoulos, S., Glotsos, D., Kalatzis, I. and Nikiforidis, G., A wavelet-based Markov random field segmentation model in segmenting microarray experiments. Comput. Methods Programs Biomed., 2011, 104(3), 307–315.
- Uslan, V. and Bucak, I. Ö., Microarray image segmentation using clustering methods. Math. Comput. Appl., 2010, 15(2), 240– 247.
- Biju, V. G. and Mythili, P., A genetic algorithm based fuzzy C mean clustering model for segmenting microarray images. Int. J. Comput. Appl., 2012, 52(11), 42–48.
- Pal, N. R., Pal, K., Keller, J. M. and Bezdek, J. C., A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst., 2005, 13(4), 517–530.
- Krinidis, S. and Chatzis, V., A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process., 2010, 19(5), 1328–1337.
- Biju, V. G. and Mythili, P., Fuzzy clustering algorithms for cDNA microarray image spots segmentation. Procedia Comput. Sci., 2015, 46, 417–424.
- Biju, V. G. and Mythili, P., An improved fuzzy clustering algorithm for microarray spots segmentation. ICTACT J. Image Video Process., 2015, 6(2), 1107–1114.
- Biju, V. G. and Mythili, P., Microarray image gridding using grid line refinement technique. ICTACT J. Image Video Process., 2015, 5(4), 1010–1016.
- Gong, M., Zhou, Z. and Ma, J., Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process., 2012, 21(4), 2141–2151.
- Spellman, P. T. et al., Comprehensive identification of cell cycle– regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell, 1998, 9(12), 3273–3297.
- Tran, D. and Wagner, M., Noise clustering-based speaker verification. Lecture Notes in Computer Science, 2002, pp. 325–331.
- Betal, D., Roberts, N. and Whitehouse, G. H., Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology. Br. J. Radiol., 1997, 70(837), 903–917.
- Athanasiadis, E. I., Cavouras, D. A., Spyridonos, P. P., Glotsos, D. T., Kalatzis, I. K. and Nikiforidis, G. C., Complementary DNA microarray image processing based on the fuzzy Gaussian mixture model. IEEE Trans. Inform. Technol. Biomed., 2009, 13(4), 419– 425.
- Wang, Y. P., Gunampally, M., Chen, J., Bittel, D., Butler, M. G. and Cai, W. W., A comparison of fuzzy clustering approaches for quantification of microarray gene expression. J. Signal Process. Syst., 2008, 50(3), 305–320.
- Improved Fingerprint Compression Technique with Decimated Multi-Wavelet Coefficients for Low Bit Rates
Abstract Views :212 |
PDF Views:1
Authors
N. R. Rema
1,
P. Mythili
1
Affiliations
1 Department of Electronics Engineering, School of Engineering, Cochin University of Science and Technology, IN
1 Department of Electronics Engineering, School of Engineering, Cochin University of Science and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1801-1806Abstract
In this paper, a multi-wavelet transform with decimated frequency bands is proposed to be used in the Set Partitioning in Hierarchical Trees (SPIHT) algorithm to improve fingerprint image compression. Either shuffled or unshuffled multi-wavelets can be used for SPIHT algorithm. In both the cases, the quality of the compressed images at lower bit rates either remained the same or slightly improved compared to wavelets. To improve the performance at lower bit rates, a method which utilizes the decimated version of multi-wavelet for the initialization of lists in SPIHT algorithm is used. The multi-wavelet used for the proposed work is SA4 (Symmetric-Antisymmetric). The algorithm was tested and verified using NIST, Shivang Patel, NITGEN and other databases. An overall improvement in performance particularly at lower bit rates (0.01 to 0.09) compared to a multi-wavelet without decimation was obtained using this method. The improvement was 0.798dB, 0.857dB and 0.859dB for the images in NITGEN database for a multi-wavelet decimated by 2, 4 and 8 respectively. Similar performances were observed for other databases. It was further observed that the PSNR was highest when the multi-wavelet was decimated by a factor of 4.Keywords
Compression, Multi-Wavelet, Fingerprint Image, Decimation, Low Bit Rate.References
- V. Strela, P.N. Heller, G. Strang, P. Topiwala and C. Heil, “The Application of Multiwavelet Filterbanks to Image Processing”, IEEE Transactions on Image Processing, Vol.8, No. 4, pp. 548-563,1999.
- V. Priya and B. Ananthi, “Image Compression using Multiwavelet Transform for Medical Image”, Proceedings of IEEE International Conference on Innovations in Green Energy and Healthcare Technologies, pp. 1-5, 2017.
- Y. Feng, H. Lu and X.L. Zeng, “A Fractal Image Compression Method Based on Multi-Wavelet”, TELKOMNIKA, Vol. 13, No. 3, pp. 996-1005, 2015.
- S. Jagadeesh and E. Nagabhooshanam, “Energy Interpolated Mapping for Image Compression with Hierarchical Coding”, Indian Journal of Science and Technology, Vol.10, No. 9, pp. 113-118, 2017.
- R. Sudhakar and S. Jayaraman, “Fingerprint Compression using Multiwavelets”, International Journal of Electronics and Communication Engineering, Vol. 2, No. 7, pp. 1532-1541, 2008.
- U.S. Ragupathy, D. Baskar and A. Tamilarasi, “New Method of Image Compression using Multiwavelets and Set Partitioning Algorithm”, Proceedings of 3rd International Conference on Industrial and Information Systems, pp. 1-6, 2008.
- M.D. Adams and R. Ward, “Wavelet Transforms in the JPEG-2000 Standard”, Proceedings of IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp. 160-163, 2001.
- B.S. Emmanuel, M.D. Muazu, S. M. Sani and S. Garba, “Improved Algorithm for Biometric Fingerprint Image Compression”, American Journal of Computation, Communication and Control, Vol. 1, No. 5, pp. 75-85, 2014.
- G. Shao, Y. Wu, A. Yong, X. Liu and T. Guo, “Fingerprint Compression based on Sparse Representation”, IEEE Transactions on Image Processing, Vol. 23, No. 2, pp. 489-501, 2014.
- K.A. Shahanas and M. Selin, “An Efficient Fingerprint Compression Algorithm using Sparse Coding”, International Journal of Current Research, Vol. 7, No. 10, pp. 21670-21676, 2015.
- S. Esakkirajan, T. Veerakumar, V. Senthil Murugan and P.Navaneethan, “Image Compression using Multiwavelet and Multi-stage Vector Quantization”, International Journal of Electronics and Communication Engineering, Vol. 2, No.12, pp. 23-28, 2008.
- Muna F. Al-Sammaraie, “Medical Images Compression using Modified SPIHT Algorithm and Multiwavelets Transformation”, Computer and Information Science, Vol.4, No. 6, pp. 1-16, 2011.
- N.R. Rema, K.T. Shanavaz and P. Mythili “Better Fingerprint Image Compression at Lower Bit-Rates: An Approach using Multiwavelets with Optimised Prefilter Coefficients”, ICTACT Journal on Image and Video Processing, Vol. 8, No. 1, pp. 1588-1595, 2017.
- A. Said and W.A. Pearlman, “A New, Fast, and Efficient Image Codec based on Set partitioning in Hierarchical Trees”, IEEE Transactions on Circuits and systems for Video Technology, Vol. 6, No. 3, pp. 243-250, 1996.
- X.G. Xia, J.S. Geronimo, D.P. Hardin and B.W. Suter, “Design of Prefilters for Discrete Multiwavelet Transforms”, IEEE Transactions on Signal Processing, Vol.44, No. 1, pp. 25-35, 1996.
- M.B. Martin and A.E. Bell, “New Image Compression Techniques using Multiwavelets and Multiwavelet Packets”, IEEE Transactions on Image Processing, Vol. 10, No. 4, pp. 500-510, 2001.
- K.T. Shanavaz and P. Mythili, “A fingerprint-based Hybrid Gender Classification System using Genetic Algorithm”, International Journal of Computational Vision and Robotics, Vol. 6, No. 4, pp. 399-413, 2016.