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Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging
by
Tsao, Yu-Ming
, Lin, Fen-Chi
, Wang, Hsiang-Chen
, Mukundan, Arvind
, Fan, Wen-Shuang
, Wang, Ching-Yu
, Liu, Yu-Sin
in
Arteries
/ Blood vessels
/ Cameras
/ Coordinate transformations
/ Diabetes
/ Diabetes mellitus
/ Diabetic retinopathy
/ Image processing
/ Lighting
/ Medical personnel
/ Medical prognosis
/ Medical treatment
/ Neural networks
/ Patients
/ Precision medicine
/ Principal components analysis
/ Retina
/ Retinopathy
/ Veins & arteries
2023
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Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging
by
Tsao, Yu-Ming
, Lin, Fen-Chi
, Wang, Hsiang-Chen
, Mukundan, Arvind
, Fan, Wen-Shuang
, Wang, Ching-Yu
, Liu, Yu-Sin
in
Arteries
/ Blood vessels
/ Cameras
/ Coordinate transformations
/ Diabetes
/ Diabetes mellitus
/ Diabetic retinopathy
/ Image processing
/ Lighting
/ Medical personnel
/ Medical prognosis
/ Medical treatment
/ Neural networks
/ Patients
/ Precision medicine
/ Principal components analysis
/ Retina
/ Retinopathy
/ Veins & arteries
2023
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Do you wish to request the book?
Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging
by
Tsao, Yu-Ming
, Lin, Fen-Chi
, Wang, Hsiang-Chen
, Mukundan, Arvind
, Fan, Wen-Shuang
, Wang, Ching-Yu
, Liu, Yu-Sin
in
Arteries
/ Blood vessels
/ Cameras
/ Coordinate transformations
/ Diabetes
/ Diabetes mellitus
/ Diabetic retinopathy
/ Image processing
/ Lighting
/ Medical personnel
/ Medical prognosis
/ Medical treatment
/ Neural networks
/ Patients
/ Precision medicine
/ Principal components analysis
/ Retina
/ Retinopathy
/ Veins & arteries
2023
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Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging
Journal Article
Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging
2023
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Overview
The severity of diabetic retinopathy (DR) is directly correlated to changes in both the oxygen utilization rate of retinal tissue as well as the blood oxygen saturation of both arteries and veins. Therefore, the current stage of DR in a patient can be identified by analyzing the oxygen content in blood vessels through fundus images. This enables medical professionals to make accurate and prompt judgments regarding the patient’s condition. However, in order to use this method to implement supplementary medical treatment, blood vessels under fundus images need to be determined first, and arteries and veins then need to be differentiated from one another. Therefore, the entire study was split into three sections. After first removing the background from the fundus images using image processing, the blood vessels in the images were then separated from the background. Second, the method of hyperspectral imaging (HSI) was utilized in order to construct the spectral data. The HSI algorithm was utilized in order to perform analysis and simulations on the overall reflection spectrum of the retinal image. Thirdly, principal component analysis (PCA) was performed in order to both simplify the data and acquire the major principal components score plot for retinopathy in arteries and veins at all stages. In the final step, arteries and veins in the original fundus images were separated using the principal components score plots for each stage. As retinopathy progresses, the difference in reflectance between the arteries and veins gradually decreases. This results in a more difficult differentiation of PCA results in later stages, along with decreased precision and sensitivity. As a consequence of this, the precision and sensitivity of the HSI method in DR patients who are in the normal stage and those who are in the proliferative DR (PDR) stage are the highest and lowest, respectively. On the other hand, the indicator values are comparable between the background DR (BDR) and pre-proliferative DR (PPDR) stages due to the fact that both stages exhibit comparable clinical-pathological severity characteristics. The results indicate that the sensitivity values of arteries are 82.4%, 77.5%, 78.1%, and 72.9% in the normal, BDR, PPDR, and PDR, while for veins, these values are 88.5%, 85.4%, 81.4%, and 75.1% in the normal, BDR, PPDR, and PDR, respectively.
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