Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Performance of Low-Level Motion Estimation Methods for Confocal Microscopy of Plant Cells in vivo
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Cell Division Detection on the Arabidopsis Thaliana Root
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging
Computers in Biology and Medicine
Cell segmentation, tracking, and mitosis detection using temporal context
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Evaluation of contrast enhancement filters for lung nodule detection
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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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 bad 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%.