Cell Tracing and Analysis Using Image Segmentation Algorithms
Date Issued
2016
Date
2016
Author(s)
Cheng, Ya-Hsin
Abstract
Study of living cells like cell movement, cell death, and cell division are more and more popular these days. Before cell tracking and analysis, cell segmentation should first be performed. Without accurate cell segmentation, the later biological analysis will have large error due to accumulation of preprocessing error. As a result, good segmentation is an important step for cell tracking and analysis. This thesis describes the methods of cell segmentation that we proposed. In cell segmentation, we have to deal with problems such as different kinds of shapes of the cells, background interference, the quality of the image, etc. We solve those issues and propose a robust cell segmentation method. First, we apply automatic adaptive thresholding to separate images into cell region and background. Second, applying improved shortest path segmentation to cell regions which need to be further segmented. Finally, we construct the fundamental 3D cell segmentation by applying 3D cell labeling to the result of 2D cell segmentation. Simulations show that our proposed method segments most of cell images efficiently and outperforms state-of-the-art methods.
Subjects
cell segmentation
image segmentatio
cell image preprocessing
Type
thesis
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