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AOTF based molecular hyperspectral imaging system and its applications on nerve morphometry

机译：基于AOTF的分子高光谱成像系统及其在神经形态学中的应用

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摘要

The neuroanatomical morphology of nerve fibers is an important description for understanding the pathological aspects of nerves. Different from the traditional automatic nerve morphometry methods, a molecular hyperspectral imaging system based on an acousto-optic tunable filter (AOTF) was developed and used to identify unstained nerve histological sections. The hardware, software, and system performance of the imaging system are presented and discussed. The gray correction coefficient was used to calibrate the system's spectral response and to remove the effects of noises and artifacts. A spatial-spectral kernel-based approach through the support vector machine formulation was proposed to identify nerve fibers. This algorithm can jointly use both the spatial and spectral information of molecular hyper-spectral images for segmentation. Then, the morphological parameters such as fiber diameter, axon diameter, myelin sheath thickness, fiber area, and g-ratio were calculated and evaluated. Experimental results show that the hyperspectral-based method has the potential to recognize and measure the nerve fiber more accurately than traditional methods.
机译：神经纤维的神经解剖形态是理解神经病理方面的重要描述。与传统的自动神经形态测量方法不同，开发了基于声光可调滤镜（AOTF）的分子高光谱成像系统，并将其用于识别未染色的神经组织切片。介绍并讨论了成像系统的硬件，软件和系统性能。灰色校正系数用于校准系统的光谱响应并消除噪声和伪影的影响。提出了一种通过支持向量机公式的基于空间光谱核的方法来识别神经纤维。该算法可以联合使用分子高光谱图像的空间和光谱信息进行分割。然后，计算并评估形态学参数，例如纤维直径，轴突直径，髓鞘厚度，纤维面积和g比值。实验结果表明，与传统方法相比，基于高光谱的方法具有更准确地识别和测量神经纤维的潜力。

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