Qi Shen1,2,3, Goayu Xiao1,2,3, Yingwei Zheng4,5,6, Jie Wang5, Yue Liu5, Xutao Zhu4,5, Fan Jia5, Peng Su6, Binbin Nie7, Fuqiang Xu4,5,6,8, Bin Zhang1,2,3. 1. Department of Genetics and Genomic Sciences, New York, NY, USA. 2. Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA. 3. The Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 4. Key Laboratory of Nuclear Radiation and Nuclear Energy Technology, Institute of High Energy Physics, University of Chinese Academy of Sciences, Beijing, China. 5. State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China. 6. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China. 7. Division of Nuclear Technology and Applications, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China. 8. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
Abstract
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. AVAILABILITY AND IMPLEMENTATION: The R software package is freely available at https://www.synapse.org/#!Synapse:syn18638510/wiki/591054 for Not-For-Profit Institutions. If you are a For-Profit Institution, please contact the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: The R software package is freely available at https://www.synapse.org/#!Synapse:syn18638510/wiki/591054 for Not-For-Profit Institutions. If you are a For-Profit Institution, please contact the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Kevin T Beier; Arpiar Saunders; Ian A Oldenburg; Kazunari Miyamichi; Nazia Akhtar; Liqun Luo; Sean P J Whelan; Bernardo Sabatini; Constance L Cepko Journal: Proc Natl Acad Sci U S A Date: 2011-08-08 Impact factor: 11.205
Authors: Y Ma; P R Hof; S C Grant; S J Blackband; R Bennett; L Slatest; M D McGuigan; H Benveniste Journal: Neuroscience Date: 2005-09-13 Impact factor: 3.590
Authors: B N Smith; B W Banfield; C A Smeraski; C L Wilcox; F E Dudek; L W Enquist; G E Pickard Journal: Proc Natl Acad Sci U S A Date: 2000-08-01 Impact factor: 11.205