Mr. Jorge D. Español Profile

Jorge D. Español

at Univ de Ibagué

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Proceedings Article | 21 August 2020 Presentation + Paper

Comparison of cell contour closing methods in microscopy images

Manuel Forero, Alexandra La Cruz, Jorge Español, Diego Urrego

Proceedings Volume 11510, 115101N (2020) https://doi.org/10.1117/12.2568165

KEYWORDS: Microscopy, Image segmentation, Image processing, Signal processing, Algorithm development, Interference (communication), Image analysis, Hough transforms, Edge detection

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Cell counting and tracking approaches are widely used in microscopy image processing. Cells may be of different shapes and may be very crowded or relatively close together. In both cases, the correct identification of each cell requires the detection and tracking of its contour. But, this is not always possible due to noise, image blurring from signal degradation during the acquisition process and staining problems. Generally, cell segmentation approaches use filtering techniques, Hough transform, combined with morphological operators to address this problem. However, usually, not all contours can be closed. Therefore, heuristic contour closing techniques have been employed to achieve better results. Despite being necessary, no comparative studies on this type of methods were found in the literature. For that reason, this paper compares three approaches to contour tracking and closing. Two of them use one end of a contour as a starting point and trace a path along the edge of the cell seeking to find another endpoint of the cell. This is done using the first or second ring of neighboring pixels around the starting point. The heuristics used are based on region growing taking the information from the first or second ring of neighboring pixels and keeping the direction along the plotted path. The third method employs a modification of Dijkstra's algorithm. This approach employs two seed points located at each possible end of the contour. This paper presents a description of these techniques and evaluates the results in microscopy images.

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