scholarly journals Hyperspectral Ophthalmoscope Images for the Diagnosis of Diabetic Retinopathy Stage

2020 ◽  
Vol 9 (6) ◽  
pp. 1613 ◽  
Author(s):  
Hsin-Yu Yao ◽  
Kuang-Wen Tseng ◽  
Hong-Thai Nguyen ◽  
Chie-Tong Kuo ◽  
Hsiang-Chen Wang
Keyword(s):  
Diabetic Retinopathy ◽  
Oxygen Saturation ◽  
Principal Component ◽  
Reflectance Spectra ◽  
Oxygen Utilization ◽  
Score Plot ◽  
Average Reflectance ◽  
Accuracy Rates ◽  
Arteries And Veins ◽  
Normal Background

A methodology that applies hyperspectral imaging (HSI) on ophthalmoscope images to identify diabetic retinopathy (DR) stage is demonstrated. First, an algorithm for HSI image analysis is applied to the average reflectance spectra of simulated arteries and veins in ophthalmoscope images. Second, the average simulated spectra are categorized by using a principal component analysis (PCA) score plot. Third, Beer-Lambert law is applied to calculate vessel oxygen saturation in the ophthalmoscope images, and oxygenation maps are obtained. The average reflectance spectra and PCA results indicate that average reflectance changes with the deterioration of DR. The G-channel gradually decreases because of vascular disease, whereas the R-channel gradually increases with oxygen saturation in the vessels. As DR deteriorates, the oxygen utilization of retinal tissues gradually decreases, and thus oxygen saturation in the veins gradually increases. The sensitivity of diagnosis is based on the severity of retinopathy due to diabetes. Normal, background DR (BDR), pre-proliferative DR (PPDR), and proliferative DR (PDR) are arranged in order of 90.00%, 81.13%, 87.75%, and 93.75%, respectively; the accuracy is 90%, 86%, 86%, 90%, respectively. The F1-scores are 90% (Normal), 83.49% (BDR), 86.86% (PPDR), and 91.83% (PDR), and the accuracy rates are 95%, 91.5%, 93.5%, and 96%, respectively.

Venular oxygen saturation is increased in young patients with type 1 diabetes and mild nonproliferative diabetic retinopathy

2020 ◽  
Vol 98 (8) ◽  
pp. 800-807 ◽  
Author(s):  
Nina C.B.B. Veiby ◽  
Aida Simeunovic ◽  
Martin Heier ◽  
Cathrine Brunborg ◽  
Naila Saddique ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Retinopathy ◽  
Oxygen Saturation ◽  
Young Patients ◽  
Nonproliferative Diabetic Retinopathy

Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network

2007 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiao Jun ◽  
Michael Ngadi ◽  
Ning Wang ◽  
Aynur Gunenc ◽  
Mariana Monroy ◽  
...  
Keyword(s):  
Neural Network ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Objective Assessment ◽  
Principal Component ◽  
Pork Quality ◽  
Pca Analysis ◽  
Error Frequency ◽  
Quality Classification ◽  
Average Reflectance

Pork quality is usually determined subjectively as PSE, PFN, RFN, RSE and DFD based on color, texture and exudation of the meat. In this study, a hyperspectral-imaging-based technique was developed to achieve rapid, accurate and objective assessment of pork quality. The principal component analysis (PCA) and stepwise operation methods were used to select feature waveband from the entire spectral wavelengths (430 to 980 nm). Then the feature waveband images were extracted at the selected feature wavebands from raw hyperspectral images, and the average reflectance (R) was calculated within the whole loin-eye area. Artificial neural network was used to classify these groups. Results showed that PCA analysis had a better performance than that of stepwise operation for feature waveband images selection. The 1st derivative data gave a better result than that of mean reflectance spectra data. The best classified result was 87.5% correction. The error frequency showed that RSE samples were easier to classify. The PFN and PSE samples were difficult to separate from each other.

scholarly journals Retinal blood oxygen saturation and aqueous humour biomarkers in early diabetic retinopathy

2019 ◽  
Vol 97 (5) ◽  
Author(s):  
Faryan Tayyari ◽  
Lee‐Anne Khuu ◽  
Jeremy M. Sivak ◽  
John G. Flanagan ◽  
Shaun Singer ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Oxygen Saturation ◽  
Aqueous Humour ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation

Use of near infrared spectroscopy and spectral databases to assess the quality of pharmaceutical products and aid characterization of unknown components

2019 ◽  
Vol 27 (5) ◽  
pp. 379-390
Author(s):  
Mazlina Mohd Said ◽  
Simon Gibbons ◽  
Anthony Moffat ◽  
Mire Zloh
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Score Plot ◽  
Principal Component Analysis Score ◽  
Infrared Spectral ◽  
Herbal Formulations ◽  
Spectral Database

This research was initiated as part of the fight against public health problems of rising counterfeit, substandard and poor quality medicines and herbal products. An effective screening strategy using a two-step combination approach of an incremental near infrared spectral database (step 1) followed by principal component analysis (step 2) was developed to overcome the limitations of current procedures for the identification of medicines by near infrared spectroscopy which rely on the direct comparison of the unknown spectra to spectra of reference samples or products. The near infrared spectral database consisted of almost 4000 spectra from different types of medicines acquired and stored in the database throughout the study. The spectra of the test samples (pharmaceutical and herbal formulations) were initially compared to the reference spectra of common medicines from the database using a correlation algorithm. Complementary similarity assessment of the spectra was conducted based on the observation of the principal component analysis score plot. The validation of the approach was achieved by the analysis of known counterfeit Viagra samples, as the spectra did not fully match with the spectra of samples from reliable sources and did not cluster together in the principal component analysis score plot. Pre-screening analysis of an herbal formulation (Pronoton) showed similarity with a product containing sildenafil citrate in the database. This finding supported by principal component analysis has indicated that the product was adulterated. The identification of a sildenafil analogue, hydroxythiohomosildenafil, was achieved by mass spectrometry and Nuclear Magnetic Resonance (NMR) analyses. This approach proved to be a suitable technique for quick, simple and cost-effective pre-screening of products for guiding the analysis of pharmaceutical and herbal formulations in the quest for the identification of potential adulterants.

scholarly journals The separation of softwood and hardwood in historical wooden statues of the Nazenji-temple in Japan using NIR spectroscopy

IAWA Journal ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 740-750 ◽  
Author(s):  
Hisashi Abe ◽  
Yohei Kurata ◽  
Ken Watanabe ◽  
Atsuko Ishikawa ◽  
Shuichi Noshiro ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Forest Products ◽  
Chamaecyparis Obtusa ◽  
Score Plot ◽  
Hardwood Species ◽  
Cercidiphyllum Japonicum ◽  
Sciadopitys Verticillata ◽  
Second Derivative Spectra

Abstract The applicability of near-infrared (NIR) spectroscopy to the identification of wood species of archaeologically/historically valuable wooden artifacts in a non-invasive manner was investigated using reference wood samples from the xylarium of the Forestry and Forest Products Research Institute (TWTw) and applied to several wooden statues carved about 1000 years ago. Diffuse-reflectance NIR spectra were obtained from five standard wood samples each of five softwood species (Chamaecyparis obtusa, Cryptomeria japonica, Sciadopitys verticillata, Thujopsis dolabrata, Torreya nucifera) and five hardwood species (Aesculus turbinata, Cercidiphyllum japonicum, Cinnamomum camphora, Prunus jamasakura, Zelkova serrata). A principal component analysis (PCA) model was developed from the second derivative spectra. The score plot of the first two components clearly showed separation of the wood sample data into softwood and hardwood clusters. The developed PCA model was applied to 370 spectra collected from 21 wooden statues preserved in the Nazenji-temple in Shizuoka Prefecture in Japan, including 14 made from Torreya spp. and 7 made from Cinnamomum spp. In the score plot, the statue spectra were also divided into two clusters, corresponding to either softwood (Torreya spp.) or hardwood (Cinnnamomum spp.) species. These results show that NIR spectroscopy combined with PCA is a powerful technique for determining whether archaeologically/historically valuable wooden artifacts are made of softwood or hardwood.

scholarly journals Investigation of the Performance of Hyperspectral Imaging by Principal Component Analysis in the Prediction of Healing of Diabetic Foot Ulcers

2018 ◽  
Vol 4 (12) ◽  
pp. 144 ◽  
Author(s):  
Qian Yang ◽  
Shen Sun ◽  
William Jeffcoate ◽  
Daniel Clark ◽  
Alison Musgove ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Oxygen Saturation ◽  
Hyperspectral Imaging ◽  
Ulcer Healing ◽  
Diabetic Foot ◽  
Principal Component ◽  
Component Analysis ◽  
Foot Ulcers ◽  
Diabetic Foot Ulcers ◽  
Care Providers

Diabetic foot ulcers are a major complication of diabetes and present a considerable burden for both patients and health care providers. As healing often takes many months, a method of determining which ulcers would be most likely to heal would be of great value in identifying patients who require further intervention at an early stage. Hyperspectral imaging (HSI) is a tool that has the potential to meet this clinical need. Due to the different absorption spectra of oxy- and deoxyhemoglobin, in biomedical HSI the majority of research has utilized reflectance spectra to estimate oxygen saturation (SpO2) values from peripheral tissue. In an earlier study, HSI of 43 patients with diabetic foot ulcers at the time of presentation revealed that ulcer healing by 12 weeks could be predicted by the assessment of SpO2 calculated from these images. Principal component analysis (PCA) is an alternative approach to analyzing HSI data. Although frequently applied in other fields, mapping of SpO2 is more common in biomedical HSI. It is therefore valuable to compare the performance of PCA with SpO2 measurement in the prediction of wound healing. Data from the same study group have now been used to examine the relationship between ulcer healing by 12 weeks when the results of the original HSI are analyzed using PCA. At the optimum thresholds, the sensitivity of prediction of healing by 12 weeks using PCA (87.5%) was greater than that of SpO2 (50.0%), with both approaches showing equal specificity (88.2%). The positive predictive value of PCA and oxygen saturation analysis was 0.91 and 0.86, respectively, and a comparison by receiver operating characteristic curve analysis revealed an area under the curve of 0.88 for PCA compared with 0.66 using SpO2 analysis. It is concluded that HSI may be a better predictor of healing when analyzed by PCA than by SpO2.

scholarly journals Discrimination of Volatiles of Shiitakes (Lentinula edodes) Produced during Drying Process by Electronic Nose

2020 ◽  
Vol 16 (1-2) ◽  
Author(s):  
Hui Zhang ◽  
Jing Peng ◽  
Yu-ren Zhang ◽  
Qiang Liu ◽  
Lei-qing Pan ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Lentinula Edodes ◽  
Solid Phase ◽  
Principal Component ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Drying Process ◽  
Drying Time ◽  
Discriminant Models ◽  
Accuracy Rates

AbstractThis study aimed to investigate the potential of electronic nose (E-nose) to differentiate volatiles of shiitakes produced at different drying stages. Shiitakes at different drying time slots were categorized into four groups (fresh, early, middle and late stage) by sensory evaluation. E-nose was used to analyze the volatiles and compared with headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry (HS/GC-MS). The principal component analysis results showed that shiitakes at each stage could be successfully discriminated by E-nose and HS/GC-MS. The differences in volatile organic compounds produced at each stage were mainly caused by sulfurs and alcohols, leading to apparent changes of sensors sensitive to sulfurs, alcohols and aromatic compounds. The discriminant models were established by partial least squares discriminant analysis and support vector machine classification, with accuracy rates of 91.25 % and 95.83 %, respectively. The results demonstrated the potential use of E-nose in classifying and monitoring shiitakes during drying process.

Sign in / Sign up

Export Citation Format

Share Document