Classification of Skin Abnormalities Using Oblique-Incidence Diffuse Reflectance Spectroscopy

2002 ◽  
Author(s):  
A. Garcia-Uribe ◽  
N. Kehtarnavaz ◽  
G. Marquez ◽  
M. Mehrubeoglu ◽  
V. Prieto ◽  
...  
Keyword(s):  
Oblique Incidence ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Skin Abnormalities

Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS)

2007 ◽  
Vol 101 (4) ◽  
pp. 1565-1570 ◽  
Author(s):  
D BERTELLI ◽  
M PLESSI ◽  
A SABATINI ◽  
M LOLLI ◽  
F GRILLENZONI
Keyword(s):  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Mid Infrared

scholarly journals O-OGC03 Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancer specimens using diffuse reflectance spectroscopy

2021 ◽  
Vol 108 (Supplement_9) ◽  
Author(s):  
Scarlet Nazarian ◽  
Ioannis Gkouzionis ◽  
Michal Kawka ◽  
Nisha Patel ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Real Time ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Tracking System ◽  
Reflectance Spectroscopy ◽  
Tumour Tissue ◽  
Acquisition Time ◽  
Margin Assessment ◽  
Real Time Tracking

Abstract Background Diffuse reflectance spectroscopy (DRS) is a technique that allows discrimination of normal and abnormal tissue based on spectral data. It is a promising technique for cancer margin assessment. However, application in a clinical setting is limited by the inability of DRS to mark the tissue that has been scanned and its lack of continuous real-time spectral measurements. This aim of this study was to develop a real-time tracking system to enable localisation of the tip of a handheld DRS probe to aid classification of tumour and non-tumour tissue. Methods A coloured marker was attached to the DRS fibre probe and was detected using colour segmentation. A Kalman filter was used to estimate the probe’s tip position during scanning of the tissue specimen. In this way, the system was robust to partial occlusion allowing real-time detection and tracking. Supervised classification algorithms were used for the discrimination between tumour and non-tumour tissue, and evaluated in terms of overall accuracy, sensitivity, specificity, and the area under the curve (AUC). A live augmented view with all the tracked and classified optical biopsy sites were presented, providing visual feedback to the surgeons. Results A green coloured marker was successfully used to track the DRS probe. The measured root mean square error of probe tip tracking was 1.18±0.58mm and 1.05±0.28mm for the X and Y directions, respectively, whilst the maximum measured error was 1.76mm. Overall, 47 distinct sets of tumour and non-tumour tissue data were recorded through real-time tracking of ex vivo oesophageal and gastric tissue. The overall diagnostic accuracy of the system to classify tumour and non-tumour tissue in real-time was 94% for stomach and 96% for the oesophagus. Conclusions We have been able to successfully develop a real-time tracking system for a DRS probe when used on stomach and oesophageal tissue for tumour detection, and the accuracy derived demonstrates the strength and clinical value of the technique. The method allows real-time tracking and classification with short data acquisition time to aid margin assessment in cancer resection surgery.

scholarly journals Pancreatic tumor margin detection by oblique incidence diffuse reflectance spectroscopy

2011 ◽  
Author(s):  
Alejandro Garcia-Uribe ◽  
Cheng-Chung Chang ◽  
Jun Zou ◽  
Bhaskar Banerjee ◽  
John Kuczynski ◽  
...  
Keyword(s):  
Oblique Incidence ◽  
Diffuse Reflectance ◽  
Pancreatic Tumor ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Tumor Margin ◽  
Margin Detection

Classification of Pathogenic Bacteria Using Near-Infrared Diffuse Reflectance Spectroscopy

2019 ◽  
Vol 85 (6) ◽  
pp. 1029-1036 ◽  
Author(s):  
Pin Wang ◽  
Jie Wang ◽  
Lirui Wang ◽  
Meifang Yin ◽  
Yongming Li ◽  
...  
Keyword(s):  
Pathogenic Bacteria ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy

scholarly journals Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data

2019 ◽  
Vol 10 (8) ◽  
pp. 3889 ◽  
Author(s):  
Rajitha Gunaratne ◽  
Isaac Monteath ◽  
Joshua Goncalves ◽  
Raymond Sheh ◽  
Charles N Ironside ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Spectroscopy Data ◽  
Joint Tissue ◽  
Machine Learning Classification ◽  
Human Joint
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