TY - JOUR
T1 - ARMBIS
T2 - Accurate and robust matching of brain image sequences from multiple modal imaging techniques
AU - Shen, Qi
AU - Xiao, Goayu
AU - Zheng, Yingwei
AU - Wang, Jie
AU - Liu, Yue
AU - Zhu, Xutao
AU - Jia, Fan
AU - Su, Peng
AU - Nie, Binbin
AU - Xu, Fuqiang
AU - Zhang, Bin
N1 - Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].
PY - 2019/12/15
Y1 - 2019/12/15
N2 - Motivation: Study of brain images of rodent animals is the most straightforward way to understand brain functions and neural basis of physiological functions. An important step in brain image analysis is to precisely assign signal labels to specified brain regions through matching brain images to standardized brain reference atlases. However, no significant effort has been made to match different types of brain images to atlas images due to influence of artifact operation during slice preparation, relatively low resolution of images and large structural variations in individual brains. Results: In this study, we develop a novel image sequence matching procedure, termed accurate and robust matching brain image sequences (ARMBIS), to match brain image sequences to established atlas image sequences. First, for a given query image sequence a scaling factor is estimated to match a reference image sequence by a curve fitting algorithm based on geometric features. Then, the texture features as well as the scale and rotation invariant shape features are extracted, and a dynamic programming-based procedure is designed to select optimal image subsequences. Finally, a hierarchical decision approach is employed to find the best matched subsequence using regional textures. Our simulation studies show that ARMBIS is effective and robust to image deformations such as linear or non-linear scaling, 2D or 3D rotations, tissue tear and tissue loss. We demonstrate the superior performance of ARMBIS on three types of brain images including magnetic resonance imaging, mCherry with 4′,6-diamidino-2-phenylindole (DAPI) staining and green fluorescent protein without DAPI staining images.
AB - Motivation: Study of brain images of rodent animals is the most straightforward way to understand brain functions and neural basis of physiological functions. An important step in brain image analysis is to precisely assign signal labels to specified brain regions through matching brain images to standardized brain reference atlases. However, no significant effort has been made to match different types of brain images to atlas images due to influence of artifact operation during slice preparation, relatively low resolution of images and large structural variations in individual brains. Results: In this study, we develop a novel image sequence matching procedure, termed accurate and robust matching brain image sequences (ARMBIS), to match brain image sequences to established atlas image sequences. First, for a given query image sequence a scaling factor is estimated to match a reference image sequence by a curve fitting algorithm based on geometric features. Then, the texture features as well as the scale and rotation invariant shape features are extracted, and a dynamic programming-based procedure is designed to select optimal image subsequences. Finally, a hierarchical decision approach is employed to find the best matched subsequence using regional textures. Our simulation studies show that ARMBIS is effective and robust to image deformations such as linear or non-linear scaling, 2D or 3D rotations, tissue tear and tissue loss. We demonstrate the superior performance of ARMBIS on three types of brain images including magnetic resonance imaging, mCherry with 4′,6-diamidino-2-phenylindole (DAPI) staining and green fluorescent protein without DAPI staining images.
UR - http://www.scopus.com/inward/record.url?scp=85077770120&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btz404
DO - 10.1093/bioinformatics/btz404
M3 - Article
C2 - 31114841
AN - SCOPUS:85077770120
SN - 1367-4803
VL - 35
SP - 5281
EP - 5289
JO - Bioinformatics
JF - Bioinformatics
IS - 24
ER -