Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watershed-based segmentation and region merging
Computer Vision and Image Understanding
Modern Information Retrieval
Proceedings of the sixth annual international conference on Computational biology
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
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
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Arabidopsis thaliana automatic cell file detection and cell length estimation
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Discriminative segmentation of microscopic cellular images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Optical flow based arabidopsis thaliana root meristem cell division detection
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Automatic assessment of leishmania infection indexes on in vitro macrophage cell cultures
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
| Hi-index | 0.00 |
To obtain development information of individual plant cells, it is necessary to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Automation of cell detection/marking process is important to provide research tools in order to ease the search for special events, such as cell division. In this paper we discuss an automatic cell detection approach for Arabidopsis thaliana based on 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 a cell descriptor constructed from the shape and edge strength of the cells' contour. In addition we proposed a novel cell merging criterion based on edge strength along the line that connects adjacent cells' centroids, which is a valuable tool in the reduction of cell over-segmentation. The result is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cells. When comparing the results after merging with the basic watershed segmentation, we obtain 1.5% better coverage (increase in F-measure) and up to 27% better precision in correct cell segmentation.