Date of Graduation

5-2017

Document Type

Thesis

Degree Name

Bachelor of Science in Biomedical Engineering

Degree Level

Undergraduate

Department

Biomedical Engineering

Advisor/Mentor

Muldoon, Timothy J.

Committee Member/Reader

Wolchok, Jeffrey C.

Committee Member/Second Reader

Jensen, Morten

Abstract

A leukocyte differential count can be used to diagnosis a myriad blood disorders, such as infections, allergies, and efficacy of disease treatments. In recent years, attention has been focused on developing point-of-care (POC) systems to provide this test in global health settings. Acridine orange (AO) is an amphipathic, vital dye that intercalates leukocyte nucleic acids and acidic vesicles. It has been utilized by POC systems to identify the three main leukocyte subtypes: granulocytes, monocytes, and lymphocytes. Subtypes of leukocytes can be characterized using a fluorescence microscope, where the AO has a 450 nm excitation wavelength and has two peak emission wavelengths between 525 nm (green) and 650 nm (red), depending on the cellular content and concentration of AO in the cells. The full spectra of AO stained leukocytes has not been fully explored for POC applications. Optical instruments, such as a spectrometer that utilizes a diffraction grating, can give specific spectral data by separating polychromatic light into distinct wavelengths. The spectral data from this setup can be used to create object-specific emission profiles.

Yellow-green and crimson microspheres were used to model the emission peaks and profiles of AO stained leukocytes. Whole blood was collected via finger stick and stained with AO to gather preliminary leukocyte emission profiles. A MATLAB algorithm was designed to analyze the spectral data within the images acquired using the image-based spectrometer. The algorithm utilized watershed segmentation and centroid location functions to isolate independent spectra from an image. The output spectra represent the average line intensity profiles for each pixel across a slice of an object. First steps were also taken in processing video frames of manually translated microspheres. The high-speed frame rate allowed objects to appear in multiple consecutive images. A function was applied to each image cycle to identify repeating centroid locations.

The yellow-green (515 nm) and crimson (645 nm) microspheres exhibited a distinct separation in colorimetric emission with a peak-to-peak difference of 36 pixels, which is related to the 130 nm peak emission difference. Two AO stained leukocytes exhibited distinct spectral profiles and peaks across different wavelengths. This could be due to variations in the staining method (incubation period and concentration) effecting the emissions or variations in cellular content indicating different leukocyte subtypes. The algorithm was also effective when isolating unique centroids between video frames.

We have demonstrated the ability to extract spectral information from data acquired from the image-based spectrometer of microspheres, as a control, and AO stained leukocytes. We determined that the spectral information from yellow-green and crimson microspheres could be used to represent the wavelength range of AO stained leukocytes, thus providing a calibration tool. Also, preliminary spectral information was successfully extracted from yellow-green microspheres translated under the linear slit using stationary images and video frames, thus demonstrating the feasibility of collecting data from a large number of objects.

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