Non-Invasive Vein Detection Method using Infrared Light

Marcotti, Aida; Hidalgo, M. B.; Mathé, Ladislao

doi:10.1109/tla.2013.6502814

Cited by 5 publications

(2 citation statements)

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“…From 700 to 900 nm, IR light can penetrate the skin deeply, reaching the blood vessels located in subcutaneous tissue. 23,24 Vein detection through near-IR (NIR) light is based on the absorption principle of IR radiation by principal blood components like oxyhemoglobin, deoxyhemoglobin, and water. [24][25][26] Through IR radiation, we obtain an image in which veins appear darker than the surrounding tissue in response to IR radiation exposure.…”

Section: Home Databasementioning

confidence: 99%

Zernike moment invariants for hand vein pattern description from raw biometric data

et al. 2019

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We propose an invariant description method based on Zernike moments to classify hand vein patterns from raw infrared (IR) images. Orthogonal moments provide linearly independent descriptors and are invariant to affine transformations, such as translation, rotation, and scaling. A mathematical expression is given to derive a set of moment invariants. The obtained features have all the properties of moment invariants with the additional feature of image contrast invariance. For dorsal hand vein pattern acquisition, an IR imaging system is implemented. Also, a public database is used for a palm vein recognition task. A correct rate classification (CRC) above 99.9% is achieved using a set of rotation, scale, and intensity Zernike moment invariants. Additionally, multilayer perceptron and K-nearest neighbors are used as classifiers having as input data the Zernike normalized moments. A discriminative feature evaluation of the image moments allows the reduction of the number of descriptors while maintaining a high classification rate of 99%. The efficiency of the moment descriptors is evaluated in terms of accuracy and reduced computational cost by (a) avoiding the necessity of a preprocessing stage and (b) reducing the feature vector dimension. Experimental results show that Zernike moment invariants are able to achieve hand vein recognition without image preprocessing or image normalization with respect to change of size, rotation, and intensity.

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Section: Home Databasementioning

confidence: 99%

Zernike moment invariants for hand vein pattern description from raw biometric data

et al. 2019

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“…Therefore, several studies have discussed the veins visualization system which is focused on applying visualization with direct projections on the patient's skin. Marcotti et al [3] was discussed about developement of veins visualization system. In his research, the position of the projector was placed far above the camera to get the correct projection.…”

Section: Introductionmentioning

confidence: 99%

Veins projection performance based on ultrasonic distance sensor in various surface objects

Gunawan

Sigit

Gunawan

2020

IJEECS

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Intravenous therapy aims to inject fluids such as medicine or nutritions into the body via vein vessel. This procedure is needed in various cases whether in an ordinary or emergency. Every person has a different difficulty level thus a nurse usually encountered a problem when locating the position of vein vessel. A visualization device that able to work in realtime and have high mobility is really necessary for an emergency situation to speed up the intravenous access. In this study, a stand-alone veins visualization system was developed. The back-projection method that can adjust based on distance was used to speed up the visualization process. The distance between the device and the object is obtained by an ultrasonic distance sensor. The results of this projection method with a flat surface have maximum shift of 0.48 mm. While on various surfaces, projection shifts under 0.9 mm reach 89% from 140 measurement points. Projection shifts that reach more than 0.9 mm occurred due to the sensor readings are on steep curvature or large angles between segments and sensors.

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High-Order Near-Infrared Image Technology within Smart Medical Care Systems

Liou

Chen

2019

2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

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Non-Invasive Vein Detection Method using Infrared Light

Cited by 5 publications

References 0 publications

Zernike moment invariants for hand vein pattern description from raw biometric data

Zernike moment invariants for hand vein pattern description from raw biometric data

Veins projection performance based on ultrasonic distance sensor in various surface objects

High-Order Near-Infrared Image Technology within Smart Medical Care Systems

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