HyperVein: A Hyperspectral Image Dataset for Human Vein Detection
HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.
Item Type | Article |
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Uncontrolled Keywords | hyperspectral imaging; image classification; vein detection |
Subjects |
Chemistry(all) > Analytical Chemistry Computer Science(all) > Information Systems Physics and Astronomy(all) > Instrumentation Physics and Astronomy(all) > Atomic and Molecular Physics, and Optics Engineering(all) > Electrical and Electronic Engineering Biochemistry, Genetics and Molecular Biology(all) > Biochemistry |
Date Deposited | 26 Jul 2024 19:54 |
Last Modified | 26 Jul 2024 19:54 |
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- School of Physics, Engineering & Computer Science
- Department of Engineering and Technology
- Centre for Engineering Research
- Centre for Future Societies Research
- BioEngineering
- Communications and Intelligent Systems
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