Title
A Hybrid Approach for Arabidopsis Root Cell Image Segmentation
Abstract
In vivoobservation and tracking of the Arabidopsis thalianaroot meristem, by time-lapse confocal microscopy, is important to understand mechanisms like cell division and elongation. The research herein described is based on a large amount of image data, which must be analyzed to determine the location and state of cells. The automation of the process of cell detection/marking is an important step to provide research tools for the biologists in order to ease the search for special events, such as cell division. This paper discusses a hybrid approach for automatic cell 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 the shape and edge strength of the cells' contour. The merging criterion is based on edge strength along the line that connects adjacent cells' centroids. The resulting segmentation is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cell division.
Year
DOI
Venue
2008
10.1007/978-3-540-69812-8_73
ICIAR
Keywords
Field
DocType
hybrid approach,adjacent cell,edge strength,cell detection,resulting segmentation,individual cell,cell division,automatic cell segmentation,best cell candidate,watershed-based image segmentation,arabidopsis root cell image,over-segmented cell,support vector machine,image segmentation,confocal microscopy
Cell division,Computer vision,Pattern recognition,Computer science,Segmentation,Support vector machine,Discrete cosine transform,Automation,Image segmentation,Artificial intelligence,Classifier (linguistics),Centroid
Conference
Volume
ISSN
Citations 
5112
0302-9743
4
PageRank 
References 
Authors
0.55
6
5
Name
Order
Citations
PageRank
Monica Marcuzzo1815.53
Pedro Quelhas226121.51
Ana Campilho3181.58
Ana Maria Mendonça466143.86
Aurélio J. C. Campilho532140.49