@inproceedings{ca15503bf0014c5a8958f6b5e80f07e2,
title = "CT image noise reduction using rotational-invariant feature in Stockwell transform",
abstract = "Iterative reconstruction and other noise reduction methods have been employed in CT to improve image quality and to reduce radiation dose. The non-local means (NLM) filter emerges as a popular choice for image-based noise reduction in CT. However, the original NLM method cannot incorporate similar structures if they are in a rotational format, resulting in ineffective denoising in some locations of the image and non-uniform noise reduction across the image. We have developed a novel rotational-invariant image texture feature derived from the multiresolutional Stockwell-transform (ST), and applied it to CT image noise reduction so that similar structures can be identified and fully utilized even when they are in different orientations. We performed a computer simulation study in CT to demonstrate better efficiency in terms of utilizing redundant information in the image and more uniform noise reduction achieved by ST than by NLM.",
keywords = "CT dose reduction, Image denoising, Image feature, Multiresolutional analysis, Non-local means filtering, Rotational invariant, Stockwell transform",
author = "Jian Su and Zhoubo Li and Lifeng Yu and Joshua Warner and Daniel Blezek and Bradley Erickson",
year = "2014",
doi = "10.1117/12.2044360",
language = "English (US)",
isbn = "9780819498274",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",
note = "Medical Imaging 2014: Image Processing ; Conference date: 16-02-2014 Through 18-02-2014",
}