Dictionary learning for medical image denoising, reconstruction, and segmentation

Tong T, Caballero J, Bhatia K, Rueckert D (2016)

Publication Type: Authored book

Publication year: 2016

Publisher: Elsevier Inc.

ISBN: 9780128041147

DOI: 10.1016/B978-0-12-804076-8.00006-2

Abstract

Modeling data as sparse linear combinations of basis elements from a learnt dictionary has been widely used in signal processing and machine learning. The learnt dictionary, which is well adapted to specific data, has proven to be very effective in image restoration and classification tasks. In this chapter, we will review the most popular dictionary learning techniques such as K-SVD and online dictionary learning. We will also demonstrate how these techniques can be applied to medical imaging applications including image denoising, reconstruction, super-resolution and segmentation.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Tong, T., Caballero, J., Bhatia, K., & Rueckert, D. (2016). Dictionary learning for medical image denoising, reconstruction, and segmentation. Elsevier Inc..

MLA:

Tong, T., et al. Dictionary learning for medical image denoising, reconstruction, and segmentation. Elsevier Inc., 2016.

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