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

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.

[1]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[2]  Valéria Paula Sassoli Fazan,et al.  Morphometry of saphenous nerve in young rats , 2008, Journal of Neuroscience Methods.

[3]  C Veraart,et al.  Automatic morphometry of nerve histological sections , 2000, Journal of Neuroscience Methods.

[4]  Susan E. Mackinnon,et al.  Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve , 2007, Journal of Neuroscience Methods.

[5]  B Weyn,et al.  A multiparametric assay for quantitative nerve regeneration evaluation , 2005, Journal of microscopy.

[6]  Roland T. Chin,et al.  Automated analysis of nerve-cell images using active contour models , 1996, IEEE Trans. Medical Imaging.

[7]  Antonio J. Plaza,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spectral–Spatial Hyperspectral Image Segmentation Using S , 2022 .

[8]  Diego F Alvarez,et al.  Hyperspectral imaging microscopy for identification and quantitative analysis of fluorescently‐labeled cells in highly autofluorescent tissue , 2012, Journal of biophotonics.

[9]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach , 2012, IEEE Transactions on Image Processing.

[10]  A. F. H. Goetz,et al.  Mineralogical Mapping in the Cuprite Mining District, Nevada , 1985 .

[11]  Valéria Paula Sassoli Fazan,et al.  Peripheral nerve morphometry: Comparison between manual and semi-automated methods in the analysis of a small nerve , 2007, Journal of Neuroscience Methods.

[12]  J. Astola,et al.  Vector median filters , 1990, Proc. IEEE.

[13]  Tuan Vo-Dinh,et al.  Hyperspectral imaging system using acousto‐optic tunable filter for flow cytometry applications , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[14]  Liangpei Zhang,et al.  A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Xiaoling Zhang,et al.  Signal Processing for Microwave Array Imaging: TDC and Sparse Recovery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Yanjun Zeng,et al.  Automatic identification and morphometry of optic nerve fibers in electron microscopy images , 2010, Comput. Medical Imaging Graph..

[17]  E. Wachman,et al.  AOTF microscope for imaging with increased speed and spectral versatility. , 1997, Biophysical journal.

[18]  Patrick J. Treado,et al.  Imaging Spectrometers for Fluorescence and Raman Microscopy: Acousto-Optic and Liquid Crystal Tunable Filters , 1994 .

[19]  P. Treado,et al.  Indium Antimonide (InSb) Focal Plane Array (FPA) Detection for Near-Infrared Imaging Microscopy , 1994 .

[20]  M. Malessy,et al.  Electrophysiology and morphometry of the Aα- and Aβ-fiber populations in the normal and regenerating rat sciatic nerve , 2004, Experimental Neurology.

[21]  Yiting Wang,et al.  Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology , 2012, Neurochemistry International.

[22]  Rodolphe Marion,et al.  A Theoretical Framework for Hyperspectral Anomaly Detection Using Spectral and Spatial A Priori Information , 2007, IEEE Geoscience and Remote Sensing Letters.

[23]  Jon Atli Benediktsson,et al.  A spatial-spectral kernel-based approach for the classification of remote-sensing images , 2012, Pattern Recognit..

[24]  Qing-Li Li,et al.  Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system: Evaluation of erythropoietin efficacy on diabetic retinopathy based on molecular hyperspectral imaging (MHSI) system , 2012 .

[25]  Jianwei Qin,et al.  Hyperspectral Imaging Instruments , 2010 .

[26]  Hongying Liu,et al.  Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique , 2012 .

[27]  Keerthana Prasad,et al.  Microanatomical and immunohistochemical study of the human lateral antebrachial cutaneous nerve of forearm at the antecubital fossa and its clinical implications , 2010, Clinical anatomy.

[28]  D L Farkas,et al.  Imaging acousto-optic tunable filter with 0.35-micrometer spatial resolution. , 1996, Applied optics.

[29]  A. Plaza,et al.  Spatial/Spectral analysis of hyperspectral image data , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[30]  Alexander F. H. Goetz,et al.  Three decades of hyperspectral remote sensing of the Earth: a personal view. , 2009 .

[31]  Jon Atli Benediktsson,et al.  Evaluation of Kernels for Multiclass Classification of Hyperspectral Remote Sensing Data , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[32]  D L Farkas,et al.  Near-simultaneous hemoglobin saturation and oxygen tension maps in mouse brain using an AOTF microscope. , 1997, Biophysical journal.

[33]  Antonio Plaza,et al.  Parallel Spatial-Spectral Processing of Hyperspectral Images , 2008 .

[34]  Mirza Faisal Beg,et al.  A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images , 2011, Journal of Neuroscience Methods.

[35]  Qingli Li,et al.  Molecular Spectral Imaging System for Quantitative Immunohistochemical Analysis of Early Diabetic Retinopathy , 2009, Applied spectroscopy.

[36]  G. Lauria,et al.  Morphometry of dermal nerve fibers in human skin , 2011, Neurology.

[37]  Fulvio Urso-Baiarda,et al.  Practical nerve morphometry , 2006, Journal of Neuroscience Methods.

[38]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[39]  Khalid Sayood,et al.  Lossless hyperspectral image compression using context-based conditional averages , 2005, Data Compression Conference.