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Article

Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis

1
School of Electronic and Communication, Changsha University, Changsha 410003, China
2
Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
*
Authors to whom correspondence should be addressed.
Received: 1 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
(This article belongs to the Special Issue Smart Biomedical Sensors)
An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from SPRi video data. In demonstrations of the method, SPRi video data of different protein microarrays were processed by the method. Results show that our method can locate spots in the microarray accurately regardless of the microarray pattern, spot-background contrast, light nonuniformity and spotting defects, but also can provide address information of the spots. View Full-Text
Keywords: surface plasmon resonance imaging; protein microarray; image enhancement; adaptive threshold binarization surface plasmon resonance imaging; protein microarray; image enhancement; adaptive threshold binarization
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MDPI and ACS Style

Wang, Z.; Huang, X.; Cheng, Z. Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis. Biosensors 2018, 8, 85. https://0-doi-org.brum.beds.ac.uk/10.3390/bios8030085

AMA Style

Wang Z, Huang X, Cheng Z. Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis. Biosensors. 2018; 8(3):85. https://0-doi-org.brum.beds.ac.uk/10.3390/bios8030085

Chicago/Turabian Style

Wang, Zhiyou, Xiaoqing Huang, and Zhiqiang Cheng. 2018. "Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis" Biosensors 8, no. 3: 85. https://0-doi-org.brum.beds.ac.uk/10.3390/bios8030085

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