Determination of oxygen saturation of the optic nerve head and overlying artery and vein using a snapshot multi-spectral imaging system

2012 ◽  
Author(s):  
Bahram Khoobehi ◽  
Alexander Eaton ◽  
Hussein Wafapoor ◽  
Paul Fournier ◽  
Kim Firn ◽  
...  
Keyword(s):  
Optic Nerve ◽  
Oxygen Saturation ◽  
Optic Nerve Head ◽  
Imaging System ◽  
Spectral Imaging

Oxygen saturation changes in the optic nerve head during acute intraocular pressure elevation in monkeys

2009 ◽  
Author(s):  
Bahram Khoobehi ◽  
Hiroyuki Kawano ◽  
Jinfeng Ning ◽  
Claude F. Burgoyne ◽  
David A. Rice ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
Optic Nerve ◽  
Oxygen Saturation ◽  
Optic Nerve Head ◽  
Pressure Elevation ◽  
Intraocular Pressure Elevation

Determination of Pear Leaf Nitrogen Content Based on Multi-Spectral Imaging Technology and Multivariate Calibration

2011 ◽  
Vol 467-469 ◽  
pp. 718-724 ◽  
Author(s):  
Hai Qing Yang ◽  
Gang Lv
Keyword(s):  
Nitrogen Content ◽  
Input Data ◽  
Imaging System ◽  
Spectral Imaging ◽  
Fruit Trees ◽  
Leaf Nitrogen ◽  
Front Face ◽  
Leaf Nitrogen Content ◽  
Calibration Models

Fast determination of mineral nutrition contents of fruit trees is essential for orchard precision fertilizing management. A multi-spectral imaging system was developed and tested for the measurement of leaf nitrogen content of fruit trees in the study. Images taken using this system included visible images(R-G-B) and near-infrared image(NIR). These images were further processed into several indices such as RVI, NDVI, GNDVI, -log(R) and –log(G). Total 185 leaf samples were picked from Huang-hua pear trees which were planted in three orchards with different nitrogen fertilizing levels. Among them, 135 samples were randomly sorted out as calibration set with the remaining 50 as prediction set. A SPAD-502 chlorophyll meter was used for nitrogen reference measurement. In calibration modeling, leaf front and back faces were photographed respectively. Calibration models were developed based on single variant as well as multiple variants. The result shows that calibration models based on leaf front face are better than those based on leaf back face. Among others, R and G are the most important factors for nitrogen determination with less contribution of B and NIR. Based on the images of leaf front face, R, G, RVI, NDVI, GNDVI, -log(R) and –log(G) were found significantly correlated with nitrogen content with correlation coefficients of prediction (r_pre^2) of 0.7516, 0.7396, 0.7332, 0.7220, 0.7588, 0.7598 and 0.7379 respectively. The linear combinations of R-G-B-NIR, RVI-NDVI-GNDVI and NDVI-GNDVI achieved better prediction accuracy with r_pre^2 of 0.8157, 0.7775 and 0.7661 respectively. To further improve the prediction accuracy, a three-layer BP-ANN was developed with the three combinations as its input data. The result shows that BP-ANN has an excellent performance to predict nitrogen contents. BP-ANN with the input of R-G-B-NIR performs best with r_pre^2 of 0.9386 and maximum error of 3.52(SPAD). The study suggests that multi-spectral imaging system integrated with prediction model of BP-ANN with original reflectance intensity of R-G-B-NIR channels as its input data is promising for in situ measurement of nitrogen content of fruit tree.

Application of Multi-Spectral Imaging Technique in the Determination of Leaves Nitrogen Level of Fruit Tree

2011 ◽  
Vol 181-182 ◽  
pp. 272-275 ◽  
Author(s):  
Hai Qing Yang ◽  
Gang Lv
Keyword(s):  
Imaging System ◽  
Back Propagation ◽  
Spectral Imaging ◽  
Fruit Tree ◽  
Nutrition Status ◽  
Calibration Error ◽  
Nitrogen Status ◽  
Infrared Wavelength ◽  
Error Back Propagation

A new optical instrument for fast determination of pear leaves nitrogen status was designed and fabricated. A multi-spectral imaging system was used as optical detector. In the paper, the principle of multi-spectral imaging for the measurement of leaves nitrogen status was first introduced. Then, the method of using error back propagation artificial neural network (BP-ANN) for calibration modeling was elaborated. Mean reflective light intensities in all images covering blue, green, red and infrared wavelength were taken as the input data to BP-ANN. The structure of BP-ANN with three layers has been optimized to minimize its calibration error. In the test, total 200 leave samples picked from Huang-hua pear trees planted in three orchards with different nitrogen fertilizing schemes. Among them, 150 samples were selected randomly out as for calibration set with the remaining 50 for prediction set. The result shows that correlation coefficient of R2 between predicted and measured values of nitrogen content reaches 0.82 with maximum prediction error less than 4.72(SPAD). The study suggests that the new optical method integrating multi-spectral images with BP-ANN is promising for fast diagnosis of fruit tree nutrition status.

scholarly journals A new snapshot hyperspectral imaging system to image optic nerve head tissue

2013 ◽  
Vol 92 (3) ◽  
pp. e241-e241 ◽  
Author(s):  
Bahram Khoobehi ◽  
Kim Firn ◽  
Ellie Rodebeck ◽  
Spencer Hay
Keyword(s):  
Optic Nerve ◽  
Hyperspectral Imaging ◽  
Optic Nerve Head ◽  
Imaging System

Assessing changes in oxygen saturation using a low cost multi-spectral imaging system

2019 ◽  
Author(s):  
Amir S. Bernat ◽  
Frank J. Bolton ◽  
Kfir Bar-Am ◽  
Steven L. Jacques ◽  
David Levitz
Keyword(s):  
Oxygen Saturation ◽  
Imaging System ◽  
Low Cost ◽  
Spectral Imaging
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