Analysis of multidimensional biological image data.

Relatively recent strides in noninvasive medical imaging and in vital microscopy have yielded a new form of massive data type: the multi-channel, 3-D, time-course image recording. These multidimensional datasets document dynamic changes within the full volume of a specimen over time, often simultaneously monitoring several different parameters. As a result, visual data collected from a single living specimen can now go beyond the 2-D domain, engaging the viewer’s full capacity to discern changes across space, time, and additional dimensions such as image spectra. A challenge rising out of these advances is to display these data so that the investigator can visualize and interactively explore the recording’s full spatial, temporal. and spectral content, to better understand what cannot be seen directly through the microscope eyepiece. The challenges of multidimensional image analysis are not unique to the biologist or microscopist—space scientists and climatologists have been struggling with these issues for some time in their analysis of atmospheric data. Here we discuss computational approaches to this type of data, including the introduction of a new interdisciplinary effort to develop an effective framework for the analysis of multidimensional image data.