Video-rate near-infrared tomography using spectral analysis for hemodynamic imaging

2009 ◽  
Author(s):  
Min-Chun Pan ◽  
Venkataramanan Krishnaswamy ◽  
Subhadra Srinivasan ◽  
Brian W. Pogue
Keyword(s):  
Spectral Analysis ◽  
Near Infrared ◽  
Infrared Tomography

Video-rate Near-infrared Tomography Using Spectral Analysis for Hemodynamic Imaging

2009 ◽  
Author(s):  
Min-Chun Pan ◽  
Venkataramanan Krishnaswamy ◽  
Subhadra Srinivasan ◽  
Brian W. Pogue
Keyword(s):  
Spectral Analysis ◽  
Near Infrared ◽  
Infrared Tomography

Multispectral near-infrared tomography: a case study in compensating for water and lipid content in hemoglobin imaging of the breast

2002 ◽  
Vol 7 (1) ◽  
pp. 72 ◽  
Author(s):  
Troy O. McBride ◽  
Brian W. Pogue ◽  
Steven Poplack ◽  
Sandra Soho ◽  
Wendy A. Wells ◽  
...  
Keyword(s):  
Lipid Content ◽  
Near Infrared ◽  
Infrared Tomography

scholarly journals Automated region detection based on the contrast-to-noise ratio in near-infrared tomography

2004 ◽  
Vol 43 (5) ◽  
pp. 1053 ◽  
Author(s):  
Xiaomei Song ◽  
Brian W. Pogue ◽  
Shudong Jiang ◽  
Marvin M. Doyley ◽  
Hamid Dehghani ◽  
...  
Keyword(s):  
Near Infrared ◽  
Region Detection ◽  
Noise Ratio ◽  
Infrared Tomography

Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area

2021 ◽  
pp. 0958305X2110301
Author(s):  
Min Yang ◽  
Youning Xu ◽  
Haixing Shang ◽  
Abdullah Abdullah ◽  
Wen Zhang
Keyword(s):  
Heavy Metal ◽  
Spectral Analysis ◽  
Near Infrared ◽  
Heavy Metal Contamination ◽  
Northwest China ◽  
Peak Shift ◽  
Hydroxyl Group ◽  
Band Shift ◽  
Heavy Metal Cations ◽  
Field Reflectance

Loess is an important soil type that is widespread in the Loess Plateau of northwest China. However, mining exploitation, beneficiation, and metallurgy have led to inorganic contamination of soils that threatens the health of residents. The regular absorption peak shift of near-infrared (NIR) spectra in loessal soils represents a new method of soil environmental assessment based on field reflectance spectroscopy and hyperspectral remote sensing. Specifically, the NIR features of loessal soil will shift in response to changes in the soil composition and microstructure induced by heavy metal pollution. This study collected 27 samples from notable regions in the study area. Mid-infrared (MIR) spectral analysis, NIR spectral analysis, modified seven-step Tessier sequential extraction, and X-ray diffraction were used to analyze the band shift phenomenon of MIR and NIR features. The alignment of NIR bands was determined via the correlation between NIR and MIR bands associated with the vibration variations of the hydroxyl group. The correlations established by NIR band positions and exchangeable Cd cations were also analyzed. The results were then discussed according to the mineralogical characteristics of the heavy metal cations adsorbed on the surface and interlayer sites of clay minerals. These results can be used as a reference for the application of NIR technology to detecting heavy metal contamination in the soil of mining regions.

scholarly journals HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2045 ◽  
Author(s):  
Haris Khan ◽  
Sofiane Mihoubi ◽  
Benjamin Mathon ◽  
Jean-Baptiste Thomas ◽  
Jon Hardeberg
Keyword(s):  
Spectral Analysis ◽  
High Resolution ◽  
Matrix Factorization ◽  
Local Binary Pattern ◽  
Near Infrared ◽  
Texture Classification ◽  
Hyperspectral Images ◽  
Close Range ◽  
Local Binary Pattern Feature ◽  
Non Negative Matrix Factorization

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

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