Abstract
Processing of medical images is important to improve their visibility and quality to facilitate computer-aided analysis and diagnosis in medical science. Such images are usually tainted by noise due to impediments in image capturing devices, unsupportive environment or during transmission over the network. Multi-resolution is a profound technique for decomposing the images into multiple scales and is widely used for image analysis in detail. This paper describes various multi-resolution techniques such as discrete wavelet transform, multi-wavelet transform, and Laplacian pyramid to reduce a wide variety of noise in images. Also, an image de-noising algorithm based on multi-resolution analysis for noise reduction has been described.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Madhura, J., & Ramesh Babu, D. R. (2017). A survey on noise reduction techniques for lung cancer detection. In International Conference on Innovative Mechanisms for Industry Applications (pp. 637–640). IEEE.
Lukin, A. (2006). A multiresolution approach for improving the quality of image denoising algorithm. In Proceedings of International Conference Acoustics, Speech and Signal Processing, ICASS-06 (vol. 2, pp. 857–860).
Mallat, S. (1989). A theory of multiresolution signal decomposition. The wavelet representation. IEEE Transactions Pattern Analysis and Machine Intelligence, 11, 674–693.
Hassan, H., & Saparan, A. (2011). Still image denoising based discrete wavelet transform. In IEEE International Conference on System Engineering and Technology (pp. 188–191).
Vanathe, V., Bhopathy, S., & Manikandan, M. A. (2013). MR image denoising and enhancing using multi-resolution image decomposition techniques. In International Conference on Signal Processing, Image Processing and Pattern Recognition. IEEE.
Nguyen, H. T., & Linh-Trung, N. (2010). The Laplacian pyramid with rational scaling factors and application on image denoising. In 10th International Conference on Information Science, Signal Processing and their Applications (pp. 468–471). IEEE.
Henkelman, R. M. (1985). Measurement of signal intensities in the presence of noise in MR images. Medical Physics, 12(2), 232–233.
Malini, S., & Moni, R. S. (2015). Image denoising using multiresolution analysis and nonlinear filtering. In Fifth International Conference on Advances in Computing and Communications (pp. 387–390). IEEE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bharath, G., Manjunath, A.E., Swarnalatha, K.S. (2019). A Survey on Multi-resolution Methods for De-noising Medical Images. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-6001-5_20
Download citation
DOI: https://doi.org/10.1007/978-981-13-6001-5_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6000-8
Online ISBN: 978-981-13-6001-5
eBook Packages: EngineeringEngineering (R0)