Detection of Rounded Opacities on Chest Radiographs Using Convergence Index Filter
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
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
Detection of Lung Nodule Candidates in Chest Radiographs
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
Convergence index filter for vector fields
IEEE Transactions on Image Processing
Segmentation of cell nuclei in arabidopsis thaliana roots
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
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The study of cell nuclei is fundamental for plant cell Biology research. To obtain information at cellular level, researchers image cells' nuclei which were modified with fluorescence proteins, through laser scanning confocal microscopy. These images are normally noisy and suffer from high background fluorescence, making grey-scale segmentation approaches inadequate for a usable detection. To obtain a successful detection even at low contrast we investigate the use of a particular convergence filter, the Symmetric Sliding Band filter (SSBF), for cell detection. This filter is based on gradient convergence and not intensity. As such it can detect low contrast cell nuclei which otherwise would be lost in the background noise. Due to the characteristics of cell nuclei morphology, a symmetry constrain is integrated in the filter which corrects some inadequate detections and results in a filter response that is more discriminative. We evaluate the use of this filter for cell nuclei detection on the Arabidopsis thaliana root tip, where the nuclei were stained using yellow fluorescence protein. The resulting cell nuclei detection precision is 89%.