| dc.description | The 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. | |
