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Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histog...

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Main Authors: Marlina, Yakno, Junita, Mohamad-Saleh, M. Z., Ibrahim
Format: Article
Language:English
Published: MDPI 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/32365/1/Dorsal%20hand%20vein%20image%20enhancement%20using%20fusion%20of%20CLAHE.pdf
http://umpir.ump.edu.my/id/eprint/32365/
https://doi.org/10.3390/s21196445
https://doi.org/10.3390/s21196445
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spelling my.ump.umpir.323652021-11-08T08:38:22Z http://umpir.ump.edu.my/id/eprint/32365/ Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma Marlina, Yakno Junita, Mohamad-Saleh M. Z., Ibrahim TK Electrical engineering. Electronics Nuclear engineering Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins. MDPI 2021-09-27 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32365/1/Dorsal%20hand%20vein%20image%20enhancement%20using%20fusion%20of%20CLAHE.pdf Marlina, Yakno and Junita, Mohamad-Saleh and M. Z., Ibrahim (2021) Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma. Sensors, 21 (19). pp. 1-18. ISSN 1424-8220 https://doi.org/10.3390/s21196445 https://doi.org/10.3390/s21196445
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Marlina, Yakno
Junita, Mohamad-Saleh
M. Z., Ibrahim
Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma
description Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.
format Article
author Marlina, Yakno
Junita, Mohamad-Saleh
M. Z., Ibrahim
author_facet Marlina, Yakno
Junita, Mohamad-Saleh
M. Z., Ibrahim
author_sort Marlina, Yakno
title Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma
title_short Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma
title_full Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma
title_fullStr Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma
title_full_unstemmed Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma
title_sort dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma
publisher MDPI
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/32365/1/Dorsal%20hand%20vein%20image%20enhancement%20using%20fusion%20of%20CLAHE.pdf
http://umpir.ump.edu.my/id/eprint/32365/
https://doi.org/10.3390/s21196445
https://doi.org/10.3390/s21196445
_version_ 1717093642034741248
score 13.222552

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