Ndu, H.; Sheikh-Akbari, A.; Deng, J.; Mporas, I. HyperVein: A Hyperspectral Image Dataset for Human Vein Detection. Sensors2024, 24, 1118.
Ndu, H.; Sheikh-Akbari, A.; Deng, J.; Mporas, I. HyperVein: A Hyperspectral Image Dataset for Human Vein Detection. Sensors 2024, 24, 1118.
Ndu, H.; Sheikh-Akbari, A.; Deng, J.; Mporas, I. HyperVein: A Hyperspectral Image Dataset for Human Vein Detection. Sensors2024, 24, 1118.
Ndu, H.; Sheikh-Akbari, A.; Deng, J.; Mporas, I. HyperVein: A Hyperspectral Image Dataset for Human Vein Detection. Sensors 2024, 24, 1118.
Abstract
Hyperspectral (HS) imaging plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. Hyperspectral image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper investigates the effectiveness of three dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection using HS images. A HS image dataset, encompassing left and right-hand images captured from 100 subjects with varying skin tones was created and annotated using anatomical data to represent vein and non-vein areas within the images. 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 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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