Examinando por Materia "Cell tracking"
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- PublicaciónSólo datosComparison of cell contour closing methods in microscopy images(Proceedings of SPIE - The International Society for Optical Engineering, 2020-08-24) Forero, Manuel G.; La Cruz, Alexandra; Español, Jorge; Urrego, DiegoCell 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.
- PublicaciónSólo datosEvaluation of filtering techniques for cell tracking in confocal microscopy images(Proceedings of SPIE - The International Society for Optical Engineering, 2021-08-01) Forero, Manuel G.; Morales, Kelly DanielaThe process of cell regeneration is a field of study and analysis that has grown in recent years in the field of biology. For its study, 4D confocal microscopy images are acquired that allow the visualization of cell regeneration over time. However, the process of recognition and tracking of cells is done in many cases by manual techniques, making this task complex, biased and time consuming. In addition, the very low S/N ratio of this type of images makes it necessary to implement smoothing filters that do not affect the quality of the edges, making them more diffuse, and allowing a better detection of the number of cells over time. Although a freely available semi-automatic tracking technique has been implemented, such as the Track-Mate tool, which facilitates the user's work, it only has a median filter for the smoothing process. Therefore, this paper presents the study, development and implementation of the image smoothing methods A trous, anisotropic diffusion, bilateral, guided, enhanced propagated, K-SVD, non local means, bilateral enhanced propagated, ROF and TVL, as integrated filters within the Track-Mate tool, to analyze their behavior in practical cases of progenitor cell detection and tracking, taking as criteria the noise attenuation in an optimal way with the lowest loss of information and the highest cell count in 4D images of Parhyale hawaiensis, to find the most efficient and accurate techniques for cell tracking and, thus, improve this analysis tool, allowing the user to improve the results of the studies performed in confocal microscopy images.
- PublicaciónSólo datosEvaluation of segmentation techniques for cell tracking in confocal microscopy images(Proceedings of SPIE - The International Society for Optical Engineering, 2021-08-01) Forero, Manuel G.; Rodriguez, Luis H.; Miranda, Sergio L.In different biological studies, such as cell regeneration studies, cell tracking over time is required. Thus, in these studies, the evolution of an amputated limb of the crustacean Parhyale hawaiensis is tracked using 4D confocal microscopy images. However, given the high number of images, noise level and number of cells make the manual cell tracking process a complex, cumbersome and difficult task. The tracking process using image processing techniques generally includes three stages: image enhancement, segmentation and cell identification. A tool made for this purpose, as a plugin of the ImageJ program is TrackMate, commonly used by biologists, which includes for segmentation the Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) edge detectors. To provide even more powerful detectors, the filtering methods based on the second derivative of Deriche and Shen and Castan were implemented and included in TrackMate. These four methods were evaluated for cell detection in images of Parhyale hawaiensis, finding that the Deriche and, Shen and Castan filters detected an appreciable number of false positives, due to sensitivity to noise and because the same cell was counted multiple times. As for the LoG and DoG methods, they presented the best results, being very similar because the DoG is basically an approximation of the LoG, finding that the DoG method slightly outperformed the LoG.