Paper Info
Title
Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging.
Abstract
To obtain development information of individual plant cells, it is necessary to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Automation of cell detection/marking process is important to provide research tools in order to ease the search for special events, such as cell division. In this paper we discuss an automatic cell detection approach for Arabidopsis thaliana based on segmentation, which selects the best cell candidates from a starting watershed-based image segmentation and improves the result by merging adjacent regions. The selection of individual cells is obtained using a support vector machine (SVM) classifier, based on a cell descriptor constructed from the shape and edge strength of the cells' contour. In addition we proposed a novel cell merging criterion based on edge strength along the line that connects adjacent cells' centroids, which is a valuable tool in the reduction of cell over-segmentation. The result is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cells. When comparing the results after merging with the basic watershed segmentation, we obtain 1.5% better coverage (increase in F-measure) and up to 27% better precision in correct cell segmentation.
Year
DOI
Venue
2009
10.1016/j.compbiomed.2009.06.008
Comp. in Bio. and Med.
Keywords
DocType
Volume
edge strength,cell over-segmentation,automated arabidopsis plant root,biomedical image processing,correct cell segmentation,cell division,individual cell,best cell candidate,biological cells,adjacent cell,cell descriptor,automatic cell detection approach,svm classification,cell detection
Journal
39
Issue
ISSN
Citations 
9
1879-0534
14
PageRank 
References 
Authors
1.03
13
5
Name
Order
Citations
PageRank
Monica Marcuzzo1815.53
Pedro Quelhas226121.51
Ana Campilho3181.58
Ana Maria Mendonça466143.86
Aurélio J. C. Campilho532140.49