Abstract
Image compression finds an exhaustive application in the field of medical image file storage. Image files contain considerable amount of redundant and irrelevant data and suitable image compression algorithms can be used to eliminate this. In this paper, Coiflet wavelet is used to perform the compression for computer tomography image of coronel view. The CT images are encoded using the different types of encoding techniques .The performances of compression are measured in terms of PSNR, Compression ratio, Means square error and bits per pixel. The Image texture features are extracted, and it is proven that after compression and further it can be used for classification of images.
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References
Lalitha Kumari, R. Pandian, J. Aran Glenn."Analysis of multi scale features of compressed medical images", 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016.
Minh N Do, Countourlet transform An efficient directional multi resolutional image representation, IEEE Trans on image proc,14(2005) 2091-2106.
Gemma paella, Adaptive lifting schemes combining semi norms for lossless image compression, IEEE Int conf on image proc,1(2005) 753-756.
Ginneken B V, Romeny B M & Viergever M A , Computer-aided diagnosis in chest radiography survey, IEEE Tran on med image,20(2001) 1228 – 1241.
Han, Guo-Guang Chen, Maximum kurtosis principle for the parameter selection of Gabor wavelet and its application to ultrasonic signal processing, Russ. J. Nondestr. Test , 2009, Volume 45, Number 6, Page 436.
Chenwei Deng, Weisi Lin & Jianfei Cai, Content-Based Image Compression for Arbitrary-Resolution Display Devices,IEEE Trans on M media,4(2012) 1127-1139.
Pandian R, Evaluation of image compression algorithms, IEEE Und Tech (UT) NIOT, (2015) 1-3
Pandian R & Vigneswaran T, Adaptive wavelet packet basis selection for zerotree image coding, Int J of Sig and Imag Sys Eng9 (2016) 388-392.
Pandian R, Vigneswaran T and Lalitha Kumari S Characterization of CT Cancer Lung Image Using Image Compression Algorithms and Featu Extraction, Journal of Scientific & Industrial Research, Vol. 75, December 2016, pp. 747-751.
Y. Wang, S. J. Chen, S. J. Liu, Best wavelet basis for wavelet transforms in acoustic emission signals of concrete damage process, Russ. J. Nondestr. Test , March 2016, Volume 52, Issue 3, pp 125–133.
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Pandian, R., LalithaKumari, S., Raja Kumar, R. (2020). Detection of Cancer Cell Growth in Lung Image Using Artificial Neural Network. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_150
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DOI: https://doi.org/10.1007/978-3-030-41862-5_150
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