Work

Adaptive Nonlocal Means Method for Denoising Basis Material Images From Dual-Energy Computed Tomography

Year: 2018

Type: article

Abstract: We propose an adaptive nonlocal means approach for image-domain material decomposition in low-dose dual-energy micro-computed tomography. The key idea is to create a distribution map for decomposition error and assign a smooth weight for a given pixel. This method is applied to the decomposed images of 3 basis materials: bone, soft tissue, and gold in our applications. We assume that bone and gold cannot coexist in the same pixel and regroup these basis materials into 2 categories. For soft tissue, the proposed algorithm is implemented in a noniterative mode. For bone and gold, an iterative mode is used and followed by a postiteration process. Both our numerical simulation and in vivo preclinical experiment results show that the proposed adaptive nonlocal means outperforms other state-of-the-art denoising algorithms, such as the original nonlocal means and total variation minimization methods.

Source: Journal of Computer Assisted Tomography

DOI: 10.1097/rct.0000000000000805

Authors: Yuan Yuan, Yanbo Zhang, Hengyong Yu

Institution: University of Massachusetts Lowell

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Institute of Biomedical Imaging and Bioengineering

Award: G2523454737