A Hybrid Approach for Arabidopsis Root Cell Image Segmentation

Authors:
Monica Marcuzzo;Pedro Quelhas;Ana Campilho;Ana Maria Mendonça;Aurélio Campilho
Affiliations:
INEB - Instituto de Engenharia Biomédica Divisão de Sinal e Imagem,;INEB - Instituto de Engenharia Biomédica Divisão de Sinal e Imagem,;Department of Molecular Genetics, University of Utrecht, and Institute of Biotechnology, University of Helsinki,;INEB - Instituto de Engenharia Biomédica Divisão de Sinal e Imagem, and Departamento de Engenharia Electrotécnica e Computadores, Universidade do Porto, Faculdade de Engenharia,;INEB - Instituto de Engenharia Biomédica Divisão de Sinal e Imagem, and Departamento de Engenharia Electrotécnica e Computadores, Universidade do Porto, Faculdade de Engenharia,
Venue:
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Year:
2008

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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.