Abstract

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 hyperspectral 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.

© 2013 Optical Society of America

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