Special Issue on Molecular and Cellular Bioimaging

R. F. Murphy, E. Meijering, G. Danuser

IEEE Transactions on Image Processing, vol. 14, no. 9, September 2005, pp. 1233-1236

Understanding the structure and architectural dynamics of the complex cellular and molecular machinery driving living organisms has become the prime target of biological research in the postgenomic era. The social and economic relevance of these efforts follows from the fact that detailed knowledge of the spatial and temporal relationships of cells and molecules in the context of specific physiological functions can be harnessed to improve health and well-being. It seems likely, therefore, that this knowledge will become an increasingly important factor in future human health care. Evidently, images and image sequences play a key role in obtaining this knowledge.

Over the past two decades, enormous progress has been made in the development of microscopy imaging hardware and methodology to visualize cells and molecules with high specificity. Advances in fluorescence microscopy have been especially noteworthy, including the development of the laser scanning confocal microscope, the advent of CCD cameras for digital image acquisition, the development of methods for using naturally fluorescent proteins (notably the green fluorescent protein) and for engineering a host of derived fluorescent probes. All these developments have led to an explosive increase in the acquisition of digital image data in biological studies.

There is now a growing consensus that sophisticated computational methods are necessary not only to handle the growing rate at which images are acquired, but, more importantly, to provide a level of sensitivity and objectivity that human observers cannot match. Efficient and robust image analysis tools generating accurate and reproducible quantitative results are desperately needed in support of high-throughput biological research. The high variability of the image data in biological imaging, as opposed to medical imaging with its highly standardized image acquisition protocols, poses a huge challenge to the image processing and computer vision community.

Despite an increasing number of praiseworthy efforts in applying computational methods to biological image data, the field is still very much in its infancy. In many biological research labs, the application of computational tools is often limited to low-level image signal manipulation, while the extraction of biologically meaningful information from the image data is still done manually. A possible explanation of this fact is that it takes biologists and computer scientists working closely together to develop and successfully apply automated biological image analysis tools: different algorithms need to be developed and constantly adapted to specific tasks, requiring substantial domain knowledge. Our motivation for this Special Issue was to stimulate the interaction between researchers from both communities, by presenting some of the cutting-edge work currently being done in the field and by revealing the challenges that still lie ahead.

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