Segmentation of Sonicated Blood Cells and Ultrasound Contrast Agent Microbubbles from Time-Lapse Microscopic Images
| dc.contributor.advisor | Dawit, Assefa (PhD) | |
| dc.contributor.author | Mohammed, Aliy | |
| dc.date.accessioned | 2020-03-12T10:20:25Z | |
| dc.date.accessioned | 2023-11-04T15:22:10Z | |
| dc.date.available | 2020-03-12T10:20:25Z | |
| dc.date.available | 2023-11-04T15:22:10Z | |
| dc.date.issued | 2018-02-17 | |
| dc.description.abstract | Blood cells and ultrasound contrast agent microbubbles behave strangely when subjected to an ultrasound field. They migrate towards the nodes of the ultrasound wave and form circular patterns. However, differentiation between blood cells and ultrasound contrast agent microbubbles can be easily determined via time-lapse analysis of the pattern formation. Ultrasound contrast agent microbubbles migrate towards the wave nodes very quickly and form tightly packed clusters. In contrast, formation of tightly packed clusters in the case of blood cells is unlikely based on the sonophore model theory of cells. Moreover, the interaction of the microbubbles with the blood cells and the surrounding medium is not fully understood. To study the behavior and interaction of sonicated blood cells and microbubbles on the time-lapse microscopic images, there is a need to define a contour around the cells and the microbubbles. To do so, first, the microbubbles and the cells should be segmented from the rest of image content. In this regard, this thesis devised a scheme that combines features of the Laplacian of the Gaussian detector and a modified form of the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique for effective analysis of time-lapse microscopic images. The scheme is tested on three datasets (one synthetic and two real) and its subjective and objective performance is found quite pleasing. Absence of ground truth for the real datasets makes the evaluation of the segmentation scheme merely subjective. Objective evaluation is only performed on the computer-simulated time-lapse images. The average segmentation sensitivity, specificity and accuracy of the proposed algorithm are valued around 0.96, 0.91 and 0.95 on the synthetic dataset out of a unit scale, respectively. The result generated could be a crucial input for effective particle tracking and sizing studies. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/21127 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Segmentation | en_US |
| dc.subject | Red Blood Cell | en_US |
| dc.subject | Ultrasound | en_US |
| dc.subject | Microbubbles | en_US |
| dc.subject | Image Processing | en_US |
| dc.subject | Contrast Agent | en_US |
| dc.subject | LoG | en_US |
| dc.subject | CLAHE | en_US |
| dc.title | Segmentation of Sonicated Blood Cells and Ultrasound Contrast Agent Microbubbles from Time-Lapse Microscopic Images | en_US |
| dc.type | Thesis | en_US |