| Title | Optical flow based Arabidopsis thaliana root meristem cell division detection |
| Publication Type | Book |
| Year of Publication | 2010 |
| Authors | Quelhas, P, Mendonça, AM, Campilho, A |
| Series Title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci. |
| Volume | 6112 LNCS |
| Number of Pages | 217 - 226 |
| City | Povoa de Varzim |
| ISBN Number | 03029743 (ISSN); 3642137741 (ISBN); 9783642137747 (ISBN) |
| Keywords | Arabidopsis thaliana, Biology, Biology image processing, cell division detection, Cell divisions, cell proliferation, confocal microscopy, Detection accuracy, Growth process, Image data, Image quality, Image segmentation, Imaging systems, In-vivo, In-vivo images, Metadata, Morphological analysis, Optical data processing, Optical flows, Plant biology, Plant cells, research, Root meristem |
| 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 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%. © 2010 Springer-Verlag. |
| URL | http://www.scopus.com/inward/record.url?eid=2-s2.0-77955346994&partnerID=40&md5=22ef87a91388dfe96acf9e4ac6ad4377 |
