In this work we present a time-lapsed confocal microscopy image analysis technique for an automated gene expression study of multiple single living cells. Fluorescence Resonance Energy Transfer (FRET) is a technology by which molecule-to-molecule interactions are visualized. We analyzed a dynamic series of ~102 images obtained using confocal microscopy of fluorescence in yeast cells containing RNA reporters that give a FRET signal when the gene promoter is activated. For each time frame, separate images are available for three spectral channels and the integrated intensity snapshot of the system. A large number of time-lapsed frames must be analyzed to identify each cell individually across time and space, as it is moving in and out of the focal plane of the microscope. This makes it a difficult image processing problem. We have proposed an algorithm here, based on scale-space technique, which solves the problem satisfactorily. The algorithm has multiple directions for even further improvement. The ability to rapidly measure changes in gene expression simultaneously in many cells in a population will open the opportunity for real-time studies of the heterogeneity of genetic response in a living cell population and the interactions between cells that occur in a mixed population, such as the ones found in the organs and tissues of multicellular organisms.
One of the recent efforts in development of cone-beam CT is aimed at the construction of a volumetric CT apparatus with distributed X-ray sources. This new concept in 3D CT requires a CT reconstruction algorithm designed for X-ray foci uniformly distributed on a surface
rather than on a curve. To research the properties of such algorithm an exact reconstruction formula is derived for a continuous distribution of sources on a surface of a sphere. The algorithm is implemented using finite number of focal spots for simulated phantom projection data. High resolution images were obtained for 100-400 focal spots for both noiseless and noisy input. The results exhibit a potential for CT image reconstruction from highly undersampled projection data.