TY - GEN
T1 - Mixed-model noise removal in 3d via rotation-and-scale invariant non-local means
AU - Liu, Xiangyuan
AU - Liu, Quansheng
AU - Wu, Zhongke
AU - Wang, Xingce
AU - Sole, Jose Pozo
AU - Frangi, Alejandro
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Mixed noise is a major issue influencing quantitative analysis in different forms of magnetic resonance image (MRI), such as T1 and diffusion image like DWI and DTI. Using different filters sequentially to remove mixed noise will severely deteriorate such medical images. We present a novel algorithm called rotation-and-scale invariant nonlocal means filter (RSNLM) to simultaneously remove mixed noise from different kinds of three-dimensional (3D) MRI images. First, we design a new similarity weights, including rank-ordered absolute difference (ROAD), coming from a trilateral filter (TriF) that is obtained to detect the mixed and high-level noise. Then, we present a shape view to consider the MRI data as a 3D operator, with which the similarity between the patches is calculated with the rigid transformation. The translation, rotation and scale have no influence on the similarity. Finally, the adaptive parameter estimation method of ROAD is illustrated, and the effective proof that validates the proposed algorithm is presented. Experiments using synthetic data with impulse noise, Rician noise, and the real MRI data confirm that the proposed method yields superior performance compared with current state-of-the-art methods.
AB - Mixed noise is a major issue influencing quantitative analysis in different forms of magnetic resonance image (MRI), such as T1 and diffusion image like DWI and DTI. Using different filters sequentially to remove mixed noise will severely deteriorate such medical images. We present a novel algorithm called rotation-and-scale invariant nonlocal means filter (RSNLM) to simultaneously remove mixed noise from different kinds of three-dimensional (3D) MRI images. First, we design a new similarity weights, including rank-ordered absolute difference (ROAD), coming from a trilateral filter (TriF) that is obtained to detect the mixed and high-level noise. Then, we present a shape view to consider the MRI data as a 3D operator, with which the similarity between the patches is calculated with the rigid transformation. The translation, rotation and scale have no influence on the similarity. Finally, the adaptive parameter estimation method of ROAD is illustrated, and the effective proof that validates the proposed algorithm is presented. Experiments using synthetic data with impulse noise, Rician noise, and the real MRI data confirm that the proposed method yields superior performance compared with current state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85071045947&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-13835-6_5
DO - 10.1007/978-3-030-13835-6_5
M3 - Conference contribution
AN - SCOPUS:85071045947
SN - 9783030138349
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 33
EP - 41
BT - Processing and Analysis of Biomedical Information- 1st International SIPAIM Workshop, SaMBa 2018 Held in Conjunction with MICCAI 2018, Revised Selected Papers
A2 - Lepore, Natasha
A2 - Racoceanu, Daniel
A2 - Brieva, Jorge
A2 - Joskowicz, Leo
A2 - Romero, Eduardo
PB - Springer-Verlag Italia
T2 - 1st International SIPAIM Workshop on Processing and Analysis of Biomedical Information, SaMBa 2018 held in Conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 20 September 2018 through 20 September 2018
ER -