Basic image and spectral processing in biophotonic techniques for preclinical applications
Biomedical imaging and sensing techniques have always been improving and are constantly being developed. Among imaging techniques, optical imaging has in recent years come into prominence. Due to the recent advancement in laser and nano-roughened substrate surface fabrication techniques, techniques...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/72189 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Biomedical imaging and sensing techniques have always been improving and are constantly being developed. Among imaging techniques, optical imaging has in recent years come into prominence. Due to the recent advancement in laser and nano-roughened substrate surface fabrication techniques, techniques such as photoacoustic imaging (PAI) and surface enhanced Raman spectroscopy (SERS) can now be performed. However, the information obtained from these techniques still requires a lot of post processing before it can be used in identifying and quantifying biomarkers of interest in biomedical research. In this report, an overview of photoacoustic imaging and SERS is given followed by their respective working principles. The need for spectral unmixing for these two techniques are then explained. This report also briefly covers three methods of spectral unmixing that are commonly used for photoacoustic imaging. They are linear regression, principal
component analysis (PCA) and independent component analysis (ICA). A program and graphical user interface (GUI) were developed using MATLAB programming software to carry out spectral unmixing for the new photoacoustic microscopy (PAM) system that is being set up at Singapore Bioimaging Consortium (SBIC), Agenecy for Science, Technology and Research (A*STAR). The developed program can carry out the previously mentioned methods of spectral unmixing and the software platform can also apply the linear regression spectral unmixing method for clinical SERS data. In PAI, the capability
of the program was verified for the unmixing of preclinical images of a mouse brain and also for the images obtained from a clinical application of PAI for human skin cancer detection. In SERS, the program’s ability was also demonstrated for the unmixing of spectral data obtained from tuberculosis disease-causing mycobacterium. Finally, the accuracy of the unmixed results is compared to those obtained from a commercially available multi-spectral optoacoustic tomography system. |
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