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 streamlin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1118 |
| _version_ | 1797297068891439104 |
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| author | Henry Ndu Akbar Sheikh-Akbari Jiamei Deng Iosif Mporas |
| author_facet | Henry Ndu Akbar Sheikh-Akbari Jiamei Deng Iosif Mporas |
| author_sort | Henry Ndu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:14:53Z |
| format | Article |
| id | doaj.art-587da2bec2364a1ea3edf548a87195f5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:14:53Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-587da2bec2364a1ea3edf548a87195f52024-02-23T15:33:37ZengMDPI AGSensors1424-82202024-02-01244111810.3390/s24041118HyperVein: A Hyperspectral Image Dataset for Human Vein DetectionHenry Ndu0Akbar Sheikh-Akbari1Jiamei Deng2Iosif Mporas3School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UKSchool of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UKSchool of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UKDepartment of Engineering and Technology, School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UKHyperSpectral 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.https://www.mdpi.com/1424-8220/24/4/1118hyperspectral imagingvein detectionimage classification |
| spellingShingle | Henry Ndu Akbar Sheikh-Akbari Jiamei Deng Iosif Mporas HyperVein: A Hyperspectral Image Dataset for Human Vein Detection Sensors hyperspectral imaging vein detection image classification |
| title | HyperVein: A Hyperspectral Image Dataset for Human Vein Detection |
| title_full | HyperVein: A Hyperspectral Image Dataset for Human Vein Detection |
| title_fullStr | HyperVein: A Hyperspectral Image Dataset for Human Vein Detection |
| title_full_unstemmed | HyperVein: A Hyperspectral Image Dataset for Human Vein Detection |
| title_short | HyperVein: A Hyperspectral Image Dataset for Human Vein Detection |
| title_sort | hypervein a hyperspectral image dataset for human vein detection |
| topic | hyperspectral imaging vein detection image classification |
| url | https://www.mdpi.com/1424-8220/24/4/1118 |
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