Abstract
The study of cell division and growth is a fundamental aspect of plant biology research. In this research the Arabidopsis thaliana plant is the most widely studied model plant and research is based on in vivo observation of plant cell development, by time-lapse confocal microscopy. The research herein described is based on a large amount of image data, which must be analyzed to determine meaningful transformation of the cells in the plants. Most approaches for cell division detection are based on the morphological analysis of the cells' segmentation. However, cells are difficult to segment due to had image quality in the in vivo images. We describe an approach to automatically search for cell division in the Arabidopsis thaliana root meristem using image registration and optical flow. This approach is based on the difference of speeds of the cell division and growth processes (cell division being a much faster process). With this approach, we can achieve a detection accuracy of 96.4%.

Abstract
In vivo observation and tracking of the Arabidopsis thaliana root 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. © 2008 Springer-Verlag Berlin Heidelberg.

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