Intelligent hand vein image exposure system to aid peripheral intravenous access
Difficulty in achieving peripheral intravenous (N) access in some patients is a clinical problem. These difficulties may lead to some negative impacts such as fainting, hematoma and pain associated with multiples punctures. As a result, ultrasound and infrared imaging devices have been used to aid...
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/7642/1/MARLINA_BINTI_YAKNO.PDF http://umpir.ump.edu.my/id/eprint/7642/ |
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| Summary: | Difficulty in achieving peripheral intravenous (N) access in some patients is a clinical problem. These difficulties may lead to some negative impacts such as fainting, hematoma and pain associated with multiples punctures. As a result, ultrasound and infrared imaging devices
have been used to aid IV access. Although these devices have shown to be able to aid IV access, infrared system has not been able to produce satisfactorily clear vein patterns and using ultrasound device is time consuming. Therefore, this research focuses on developing a hand vein exposure system with enhanced image of hand vein patterns to aid IV access. It consists of three major sub-systems namely, a hand vein image-acquisition system, image processing component and hand vein image-projection system. The image acquisition system consists of forty eight near-infrared light emitting diode with wavelength of 0.89um. The image processing system involves six stages. In the first stage, a noisy hand vein image is filtered using a feed-forward neural network (FFNN) based on standard median filter. In the second stage, a newly proposed technique based on finger-webs and finger-tips characteristics is applied to obtain a larger region of interest (ROl). In the third stage, the ROl images are enhanced using a combination of fuzzy histogram hyperbolization and contrast limited adaptive histogram equalization. Then, in the fourth stage, vein patterns are segmented using local adaptive threshold.
In the fifth stage, a noisy binary vein patterns are enhanced using a combination of FINN pixel correction, binary median filter and massive noise removal. In the last stage, an enhanced vein patterns are registered into the original hand vein layout. Finally, the last sub-system projects the registered vein patterns onto a patient's hand.
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