• 한국가시화정보학회지
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• 제19권1호
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• pp.36-42
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• 2021

This study measured the velocity of magnetic particles inside the power generation using external heat sources. Single Plane Illumination Microscopy (SPIM) was used to measure magnetic particles that are simultaneously affected by bubbly flow and magnetic field. It has the advantage of reducing errors due to particle superposition by illuminating the thin light sheet. The hydraulic diameter of the power generation is 3mm. Its surface is covered with a coil with a diameter of 0.3 mm. The average diameter of a magnetic particle is 200nm. The excitation and emission wavelengths are 530 and 650nm, respectively. In order to find out the flow characteristics, a total of four velocity fields were calculated in wide and narrow gap air bubbles, between the wall and the air bubble and just below the air bubble. Magnetic particles showed up to 8.59% velocity reduction in the wide gap between air bubbles due to external magnetic field.

• 대한의용생체공학회:의공학회지
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• 제33권4호
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• pp.169-176
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• 2012

Tumor cell morphology is closely related to its migratory behaviors. An active tumor cell has a highly irregular shape, whereas a spherical cell is inactive. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use 3D time-lapse phase-contrast microscopy to analyze single cell morphology because it enables to observe long-term activity of living cells without photobleaching and phototoxicity, which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we calculated the Fourier descriptor that morphological characteristics of cell to classify tumor cells into active and inactive groups. We validated classification accuracy by comparing our findings with manually obtained results.