A first glance on the enhancement of digital cell activity videos from glioblastoma cells with nuclear staining

In this work we explore a set of image enhancement techniques for improving contrast and removing noise from digital images of cell activity. The cells studied were extracted from cancerous brain tissue and exposed to different chemo-therapeutic agents, as microbiologists aim to analyze the behavior of cells exposed to different chemo-therapies. To ease and improve the precision of such analysis, an automatic cell tracking framework is of great interest. Thus, in this work we focus on the first stage of such framework, which refers to image preprocessing, aiming to noise removal and contrast enhancement, in order to improve cell segmentation and tracking performance. We compared the segmentation precision using different image preprocessing techniques based on the Deceived Weighting Average Framework (DeWAFF), reaching improvements of around 15 percent of segmentation accuracy, over no preprocessed images.