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Demand-Aware Opportunistic Spectrum Access: A Game-Theoretic Learning Approach Read chapter Read first chapter

2019 | OriginalPaper | Chapter

DTI Image Denoising Based on Complex Shearlet Domain and Complex Diffusion Anisotropic Filtering

Authors : Shuaiqi Liu, Pengfei Li, Ming Liu, Qi Hu, Mingzhu Shi, Jie Zhao

Published in: Communications, Signal Processing, and Systems

Publisher: Springer Singapore

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Abstract

Diffusion tensor imaging (DTI) is an imaging modality that has developed in recent years. It is a non-invasive technique and needn’t contrast medium. However, the SNR of DTI data is relatively low and clinically polluted by noise, which can bring serious impacts on tensor calculating, fiber tracking and other post-processing. In order to reduce the influence of noise on DTI images and improve the efficiency of diffusion tensor imaging effectively, a new DTI denoising scheme is proposed by combining the complex Shearlet transform and complex diffusion anisotropic filtering. The experiment results acquired from the simulated and real data prove the good performance of the presented algorithm.

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Metadata
Title
DTI Image Denoising Based on Complex Shearlet Domain and Complex Diffusion Anisotropic Filtering
Authors
Shuaiqi Liu
Pengfei Li
Ming Liu
Qi Hu
Mingzhu Shi
Jie Zhao
Copyright Year
2019
Publisher
Springer Singapore
Book
Communications, Signal Processing, and Systems

Print ISBN: 978-981-10-6570-5

Electronic ISBN: 978-981-10-6571-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-10-6571-2_86