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3-Dimensional Multi-Linear Transformation Based Multimedia Cryptosystem Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Detection of Cancer Cell Growth in Lung Image Using Artificial Neural Network

Authors : R. Pandian, S. LalithaKumari, R. Raja Kumar

Published in: New Trends in Computational Vision and Bio-inspired Computing

Publisher: Springer International Publishing

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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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previous chapter A New Ensemble Clustering Approach for Effective Information Retrieval
next chapter Single Image Dehazing Using Deep Belief Neural Networks to Reduce Computational Complexity
Literature
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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.
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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.
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Metadata
Title
Detection of Cancer Cell Growth in Lung Image Using Artificial Neural Network
Authors
R. Pandian
S. LalithaKumari
R. Raja Kumar
Copyright Year
2020
Publisher
Springer International Publishing
Book
New Trends in Computational Vision and Bio-inspired Computing

Print ISBN: 978-3-030-41861-8

Electronic ISBN: 978-3-030-41862-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41862-5_150

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