Authors:
Nicole Kerrison
and
Andy Bulpitt
Affiliation:
University of Leeds, United Kingdom
Keyword(s):
Segmentation, Lighting Correction, Cell Motility, Microscopy, Lamellipodia, DIC.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Medical Image Applications
;
Segmentation and Grouping
Abstract:
Understanding cell movement is important in helping to prevent and cure damage and disease. Increasingly,
this study is performed by obtaining video footage of cells in vitro. However, as the number of images
obtained for cellular analysis increases, so does the need for automated segmentation of these images, since
this is difficult and time consuming to perform manually. We propose to automate the process of segmenting
all parts of a cell visible in DIC microscopy video frames by providing an efficient method for correcting the
lighting bias and a novel combination of techniques to detect different cell areas and isolate parts of the cell
vital to their movement. To the best of our knowledge we contribute the only method able to automatically
detect the thin cellular membranes in DIC images. We show that the method can be used to isolate features in
order to detect variations vital to motility in differently affected cells.