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.

中文翻译：

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ARMBIS：通过多模态成像技术对大脑图像序列进行准确而稳健的匹配。


动机 研究啮齿类动物的大脑图像是了解大脑功能和生理功能的神经基础的最直接的方法。大脑图像分析的一个重要步骤是通过将大脑图像与标准化的大脑参考图集进行匹配，将信号标签精确地分配给指定的大脑区域。然而，由于切片制备过程中伪影操作的影响、图像分辨率相对较低以及个体大脑的较大结构变化，尚未做出重大努力来将不同类型的大脑图像与图谱图像进行匹配。结果在这项研究中，我们开发了一种新颖的图像序列匹配程序，称为精确且鲁棒的匹配大脑图像序列（ARMBIS），以将大脑图像序列与已建立的图谱图像序列进行匹配。首先，对于给定的查询图像序列，通过基于几何特征的曲线拟合算法来估计缩放因子以匹配参考图像序列。然后，提取纹理特征以及尺度和旋转不变的形状特征，并设计基于动态规划的程序来选择最佳图像子序列。最后，采用分层决策方法来使用区域纹理找到最佳匹配的子序列。我们的模拟研究表明，ARMBIS 对于图像变形（例如线性或非线性缩放、2D 或 3D 旋转、组织撕裂和组织丢失）是有效且稳健的。我们证明了 ARMBIS 在三种类型的脑图像上的卓越性能，包括磁共振成像、具有 4',6-二脒基-2-苯基吲哚 (DAPI) 染色的 mCherry 和没有 DAPI 染色图像的绿色荧光蛋白。可用性和实现 R 软件包可在 https://www.synapse.org/# 上免费获取！Synapse：syn18638510/wiki/591054 适用于非营利机构。如果您是营利机构，请联系通讯作者。补充信息 补充数据可在生物信息学在线获取。