Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Thamilselvan, P.
- Detection and classification of lung cancer MRI images by using enhanced k nearest neighbor algorithm
Abstract Views :186 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Bishop Heber College, Tiruchirappalli - 620017, Tamil Nadu, IN
1 Department of Computer Science, Bishop Heber College, Tiruchirappalli - 620017, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 43 (2016), Pagination:Abstract
Objectives: To detect and classify the malignant cancer tissues and benign cancer tissues in MR lung cancer images by using k nearest neighbor mining algorithm. Methods/Statistical Analysis: In this paper, the Enhanced K Nearest Neighbor (EKNN) algorithm is executed to identify the lung cancer images. The k nearest neighbor technique is an important method of data mining algorithms. Findings: This work implicates four stages such as pre-processing, feature extraction, classification and detection of cancer tissues. In preprocessing stage, morphological process is used to filter the irrelevant noisy data in images. In the second phase, statistical and discriminator algorithm is used to extract the images. In the last stage, the improved k Nearest Neighbor (EKNN) method has been used to classify and identify the cancerous tissues in MRI images. The detection of cancer tissues and classification is done by executing four steps of Enhanced k Nearest Neighbor method which are measuring the Euclidean distance value, determining the k value, calculating the minimum distance and detecting the cancerous cells. Improvements/Applications: The experimental study with enhanced k nearest neighbor method shows better and promising classification result for classifying benign and malignant tissues.Keywords
Geometrical and Statistical Properties, Image Classification, Image Mining, MRI Images, k Nearest Neighbor, Morphological Method.Full Text
- Development and Standardization of Mindfulness Qualities Scale for College Students
Abstract Views :335 |
PDF Views:0
Authors
B. Arunya
1,
P. Thamilselvan
1
Affiliations
1 Department of Psychology, PSG College of Arts and Science, Affiliated to Bharathiar University, Coimbatore, IN
1 Department of Psychology, PSG College of Arts and Science, Affiliated to Bharathiar University, Coimbatore, IN
Source
Indian Journal of Positive Psychology, Vol 8, No 1 (2017), Pagination: 64-67Abstract
With the increasing demands, we are put on a 24x7 clock running behind goals, weekend targets. Mindfulness provides an opportunity to open doors to the beauty of present moment. With empirical evidence supporting the benefits of mindfulness, an indigenous scale is essential. This study focused on developing an indigenous scale to measure the various affective and cognitive qualities that fosters mindfulness as conceptualized by Jon Kabat-Zinn and Shauna-Shapiro, Jeffrey-Schwartz. An intensive set of focus group discussion, trial run and expert reviews were done. Post expert reviews, a sample of 105 college students were given the scale to assess the Internal consistency (Cronbach's Alpha 0.796) and Test Retest reliability of 0.548 respectively. Concurrent validity of the scale based upon its relation with Cognitive and Affective Mindfulness Scale-Revised (Greg Feldman, Adele Haves, Sameet, Kumar, Jeff Greeson, & Jean Philippe Laurenceau, 2006) is found to be 0.576. Item were reduced throughout the standardization procedure and a final version of 30 item scale with the response category- Never, Sometimes, Often, Always was formulated along with norms. The scale shall be extended further beyond college population.
References
- Alidina, S. (2014). Mindfulness for Dummies. Book, Wiley.
- Bishop, S.R. (2002). What do we really know about mindfulness-based stress reduction? Psychosomatic Medicine,64(y), 71-83.
- Didonna,F. (2008). ClinicalHandbookofMindfulness. Book, Springer New York.
- Feldman, G., Hayes, A., Kumar, S., Greeson, J., & Laurenceau, J. P. (2007). Mindfulness and emotion regulation: The development and initial validation of the Cognitive and Affective Mindfulness Scale-Revised (CAMS-R). Journal of Psychopathology and BehavioraI Assessment,29(3),177-190.
- Fralich, T. (2013). The Five Core Skills of Mindfulness: A Direct Path to More Confidence, joy and Love, Book, Pesi Publishing and Media
- Halliwell, E. (2015). Mindfulness: How to Live Well by Paying Attention. Book, Hay House.
- Huxter, M. (2016). Healing the Heart and Mind with Mindfulness: Ancient Path, Present Moment Book,Taylor and Francis.
- Jamie Marich, P. D. L. S., & Foreword by Christine Valters Paintner, P. D. O. O. S. B. R (2015). Dancing Mindfulness: A Creative Path to Healing and Transformation. Book,Long Hill Partners,Incorporated.
- Kabat-Zinn, J. (2012). Mindfulness for Beginners: Reclaiming the Present Moment and Your Life. Book, Sounds True.
- Kaufman, S. B., & Gregoire, C. (2015). Wired to Create: Unraveling the Mysteries of the Cretive Mind. Book, Penguin Publishing Group.
- Labbe, E. E. (2011). Psychology Moment by Moment: A Guide to Enhancing Your Clinical Practice with Mindfulness and Meditation. Book, New Harbinger Publications.
- Langer, E. J., & Moldoveanu, M. (2000). The construct of mindfulness. Journal of Social Jssues,56(1),19.
- Tanay, G., & Bernstein, A. (2013). State Mindfulness Scale (SMS): Development and initial validztion. PsychoIogical Assessment 25(4), 12-86.
- Improving Medical Image Preprocessing Using Denoising Technique
Abstract Views :167 |
PDF Views:0
Authors
Affiliations
1 PG and Research Department of Computer Science, Bishop Heber College, IN
1 PG and Research Department of Computer Science, Bishop Heber College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2650-2654Abstract
Image denoising is a main issue found in medical images and computer vision issues. There are different existing techniques in denoising image but the significant property of a decent image denoising model is that eliminate noise beyond what many would consider possible just as protect edges. Digital images accept a fundamental part both in step-by-step medical image applications, for instance, satellite TV, figured tomography. This method implemented for removing the noise from the lung cancer medical images with securing, transmission and gathering and capacity and recovery measures. This paper presents a preprocessing calculation which is named as Preprocessing Profuse Clustering Technique (PPCT) in light of the super pixel clustering. K-Means clustering, Simple Linear Iterative Clustering, Fusing Optimization algorithms are engaged with this proposed Preprocessing Profuse Clustering Technique and is additionally utilized for denoising the Lung Cancer images to get the more exact outcome in the dynamic interaction.Keywords
Preprocessing, K-Means, Preprocessing, Medical Image, Profuse Clustering Technique, DenoisingReferences
- Kanika Gupta and S.K. Gupta, “Image Denoising Techniques - A Review paper”, International Journal of Innovative Technology and Exploring Engineering, Vol .2, No. 4, pp. 1-12, 2013.
- S.G. Mallat and W.L. Hwang, “Singularity Detection and Processing with Wavelets”, IEEE Transactions on Information Theory, Vol. 38, pp. 617-643, 1993.
- D. L. Donoho, “De-Noising by Soft-Thresholding”, IEEE Transactions on Information Theory, Vol. 41, No. 3, pp. 613-627, 1995.
- Imola K. Fodor and Chandrika Kamath, “Denoising Through Wavelet Shrinkage: An Empirical Study”, Journal of Electronic Imaging, Vol. 12, No. 1, pp. 1-13, 2001.
- R. Coifman and D. Donoho, “Translation Invariant Denoising”, Proceedings of International Conference on Wavelets and Statistics, pp. 125-150, 1995.
- L. Zhang, P. Bao and X.L. Wu, “Multiscale LMMSE-Based Image Denoising with Optimal Wavelet Selection”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 15, No. 4, pp. 469-481, 2005.
- V. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering”, IEEE Transactions on Image Processing, Vol. 16, No. 8, pp. 2080-2095, 2007.
- P. Jain and V. Tyagi, “Spatial and Frequency Domain Filters for Restoration of Noisy Images”, IETE Journal of Education, Vol. 54, No. 2, pp. 108-116, 2013.
- T. Thamaraimanalan, “Multi Biometric Authentication using SVM and ANN Classifiers”, Irish Interdisciplinary Journal of Science and Research, Vol. 2, No. 3, pp. 1-13, 2021.
- C. Venkatesan, P. Karthigaikumar and R. Varatharajan, “FPGA Implementation of Modified Error Normalized LMS Adaptive Filter for ECG Noise Removal”, Cluster Computing, Vol. 22, No. 5, pp. 12233-12241, 2019.
- L. Zhang, W.S. Dong and G.M. Shi, “Two-Stage Image Denoising by Principal Component Analysis with Local Pixel Grouping”, Pattern Recognition, Vol. 43, No.4, pp. 1531–1549, 2010.
- D.D. Muresan and T.W. Parks, “Adaptive Principal Components and Image Denoising”, Proceedings of International Conference on Image Processing, pp 97-101, 2003.
- R.C. Gonzalez and R.E Woods, “Digital Image Processing”, Prentice Hall, 2006.
- A.B. Hamza, P.L. Luque-Escamilla and R. Roman Roldan, “Removing Noise and Preserving Details with Relaxed Median Filters”, Journal of Mathematical Imaging and Vision, Vol. 11, No. 2, pp. 161-177, 2021.
- T. Thamaraimanalan and P. Sampath, “A Low Power Fuzzy Logic Based Variable Resolution ADC for Wireless ECG Monitoring Systems”, Cognitive Systems Research, Vol. 57, pp. 236-245, 2019.
- H. Choi and R. Baraniuk “Analysis of Wavelet-Domain Wiener Filters”, Proceedings of International Symposium on Time-Frequency and Time-Scale Analysis, pp. 613-616, 1998.
- P.L. Combettes and J.C. Pesquet, “Wavelet-Constrained Image Restoration”, International Journal on Wavelets Multiresolution Information Processing, Vol. 2, No. 4, pp. 371-389, 2004.
- R.D. Da Silva R. Minetto and H. Pedrini, “Adaptive Edge Preserving Image Denoising using Wavelet Transforms”, Pattern Analysis and Applications, Vol. 6, No. 4, pp. 567580, 2013.
- C. Venkatesan and P. Karthigaikumar, “An Efficient Noise Removal Technique using Modified Error Normalized LMS Algorithm”, National Academy Science Letters, Vol. 41, No. 3, pp. 155-159, 2018.
- J. Portilla, V. Strela, M.J. Wainwright and E.P. Simoncelli, “Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain”, IEEE Transactions on Image Processing, Vol. 12, No. 11, pp. 1338-1351, 2003.
- V. Strela, “Denoising Via Block Wiener Filtering in Wavelet Domain”, Proceedings of 3rd European Congress of Mathematics, pp 619-625, 2001.
- X.B. Yao, “Image Denoising Research based on Non-Local Sparse Models with Low-Rank Matrix Decomposition”, Ph.D. Dissertation, Department of Computer Science and Engineering, Xidian University, pp. 1-187, 2014.
- Y.Y. Chen and T. Pock, “Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1256-1272, 2017.
- U. Schmidt and S. Roth, “Shrinkage Fields for Effective Image Restoration”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp 2774-2781, 2014.
- J. Kim, J.K. Lee and K.M. Lee, “Accurate Image SuperResolution using very Deep Convolutional Networks”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp 1646-1654, 2016.
- S. Nah, T.H. Kim and K.M. Lee, “Deep Multi-Scale Convolutional Neural Network for Dynamic Scene Deblurring”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp 257-265, 2017.
- V. Jain and H.S. Seung, “Natural Image Denoising with Convolutional Networks”, Proceedings of IEEE
- International Conference on Neural Information Processing Systems, pp 769-776, 2008.
- S. Satheeskumaran, C. Venkatesan and S. Saravanan, “RealTime ECG Signal Pre-Processing and Neuro Fuzzy-based CHD Risk Prediction”, International Journal of Computational Science and Engineering, Vol. 24, No. 4, pp. 323-330, 2021.
- J.Y. Xie, L.L. Xu and E.H. Chen, “Image Denoising and Inpainting with Deep Neural Networks”, Proceedings of IEEE International Conference on Neural Information Processing Systems, pp. 341-349, 2012.
- K. Zhang, W.M Zuo, Y.J. Chen and L. Zhang, “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising”, IEEE Transactions on Image Processing, Vol. 26, No. 7, pp. 3142-3155, 2017.
- K. Zhang, W.M. Zuo and L. Zhang, “FFDNet: Toward A Fast and Flexible Solution for CNN-based Image Denoising”, IEEE Transactions on Image Processing, Vol. 27, No. 9, pp. 4608-4622, 2018.
- C. Cruz, A. Foi, V. Katkovnik and K. Egiazarian, “Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising”, IEEE Signal Processing Letters, Vol. 25, No. 8, pp. 1216-1220, 2018.