Hyperspectral Image Reconstruction from RGB Data and Its Biomedical Applications

Tae Hoon Kim, Purdue University

Abstract

Noninvasive label-free hyperspectral imaging of physiological changes in biological systems has been extensively used as invaluable tools in a variety of research areas, including medical and biological applications. However, conventional approach requires a critical, yet bulky, expensive, and slow optical component (i.e. a mechanical filter wheel, an imaging spectrograph, or a liquid crystal tunable filter) for capturing full spectral information. Therefore, this component limits development of compact, rapid, and cost-effective systems. In this respect, we propose to explore a possibility of spectrometerless (or spectrometer-free) hyperspectral imaging to map out detailed spatial distributions of biochemical absorption compounds or pigments, which are key traits for physiological evaluation. In this work, we demonstrate that a combination of 3-color CCD imaging system and a hyperspectral reconstruction method offers simple instrumentation and provides hyperspectral image data to noninvasively detect physiological changes of various biological tissues. In Chapter 2, we apply this proposed method to plant model to investigate a possibility of visualizing heterogeneous responses to various types of abiotic and biotic stress. We report a spectrometerless (or spectrometer-free) reflectance imaging method that allows for nondestructive and quantitative chlorophyll imaging in individual leaves in situ in a handheld device format. Detailed spatial distribution of chlorophyll content in a whole leaf was successfully visualized with a high correlation between the reflectance spectra pattern and the chlorophyll content. This approach could potentially be integrated into a compact, inexpensive, and portable system, while being of great value in high-throughput phenotyping facilities and laboratory settings. As another application, Chapter 3 shows a potential benefit of the proposed method to supplement qualitative scoring of conjunctival redness developed for farmers to gauge anemia in small ruminants and identify animals that require treatment. We report experimental and numerical results for simple, yet reliable, noninvasive hemoglobin detection that can be correlated with laboratory-based blood hemoglobin testing for anemia diagnosis. In our pilot animal study using calves, we exploit the third eyelid (i.e. palpebral conjunctiva) as an effective sensing site. To further test spectrometer-free (or spectrometerless) hemoglobin assessments, we implement full spectral reconstruction from RGB data and partial least square regression. The unique combination of RGB-based spectral reconstruction and partial least square regression could potentially offer uncomplicated instrumentation and avoid the use of a spectrometer, which is vital for realizing a compact and inexpensive hematology device for quantitative anemia detection in the farm field. In Chapter 4, we present that mathematical hyperspectral reconstruction from RGB images in a simple imaging setup can provide reliable visualization of hemoglobin content in a large skin area. Without relying on a complicated, expensive, and slow hyperspectral imaging system, we demonstrate the feasibility of determining heterogeneous or multifocal areas of inflammatory hyperemia associated with experimental photocarcinogenesis in mice. Sensitive and accurate assessment of dermatologic inflammatory hyperemia would be beneficial to laypeople for monitoring their own skin health on a regular basis, to patients for looking for timely clinical examination, and to primary care physicians or dermatologists for delivering effective treatments. Therefore, we envision that RGB-based reconstructed hyperspectral imaging of subclinical inflammatory hyperemic foci could potentially be integrated with the built-in camera (RGB sensor) of a smartphone to develop a simple imaging device that could offer affordable monitoring of dermatologic health.

Degree

Ph.D.

Advisors

Kim, Purdue University.

Subject Area

Biomedical engineering

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