Automated image analysis software, CellC, was developed and validated for quantification of bacterial cells from digital microscope images. CellC enables automated enumeration of bacterial cells, comparison of total count and specific count images [e.g., 4',6-diamino-2-phenylindole (DAPI) and fluorescence in situ hybridization (FISH) images], and provides quantitative estimates of cell morphology. The software includes an intuitive graphical user interface that enables easy usage as well as sequential analysis of multiple images without user intervention. Validation of enumeration reveals correlation to be better than 0.98 when total bacterial counts by CellC are compared with manual enumeration, with all validated image types. The software is freely available and modifiable: the executable files and MATLAB source codes can be obtained at www. cs. tut.fi/sgn/csb/cellc.

Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.

Detection and three dimensional reconstruction of cell structures from brightfield microscopy video clips using digital image processing algorithms is presented. While the confocal microscopy offers an efficient technique for three dimensional measurements, extensive and repeated measurements are still often better to be performed using permanent staining and brightfield microscopy. By processing of brightfield microscopy videos using automated and efficient digital image processing algorithms, the tedious task of manual analysis can be avoided. Our two-stage algorithm is applied for 1) cell soma detection and 2) identification of the 3D structure of entire neurons. To verify the results, we present 3D reconstructions of the detected cells

Subcellular organelles are commonly analyzed using 2D fluorescent microscopy. However, 3D reconstruction and analysis of organelle topology in a high-throughput manner promises to result in a better understanding of cellular systems. We developed image analysis methods for automated quantitative analysis of peroxisome shapes. The methods employ 3D image stacks obtained by confocal microscopy. There are three fundamental phases: image preprocessing and segmentation, 3D reconstruction, and automated quantification of peroxisome topology in 3D using shape descriptors. The algorithms are shown to produce results that can be used to classify objects of different topologies, and to enable visual studies of peroxisomes in 3D.

Detection and three dimensional reconstruction of cell structures from brightfield microscopy video clips using digital image processing algorithms is presented. While the confocal microscopy offers an efficient technique for three dimensional measurements, extensive and repeated measurements are still often better to be performed using permanent staining and brightfield microscopy. By processing of brightfield microscopy videos using automated and efficient digital image processing algorithms, the tedious task of manual analysis can be avoided. Our two-stage algorithm is applied for 1) cell soma detection and 2) identification of the 3D structure of entire neurons. To verify the results, we present 3D reconstructions of the detected cells