Visualization of confocal microscopic biomolecular data

Biomolecular visualization facilitates insightful interpretation of molecular structures and complex mechanisms underlying bio-chemical processes. Effective visualization techniques are required to deal with confocal microscopic biomolecular data in which intricate structures, fine features, and obscure patterns might be overlooked without sophisticated data processing and image synthesis. This paper presents major challenges in visualizing confocal microscopic biomolecular data, followed by a survey of related work. We then introduce a case study conducted to investigate the interaction between two proteins contained in a budding yeast saccharomyces cerevisiae by embedding custom modules in Amira. The multi-channel confocal microscopic volume data was first processed using an exponential operator to correct z-drop artifacts introduced during data acquisition. Channel correlation was then exploited to extract the overlap between the proteins as a new channel to represent the interaction while a statistical method was employed to compute the intensity of interaction to locate hot spots. To take advantage of crisp surface representation of region boundaries by iso-surfaces and visually pleasing translucent delineation of dense volumes by volume rendering, we adopted hybrid rendering that incorporates these two methods to display clear-cut protein boundaries, amorphous interior materials, and the scattered interaction in the same view volume with suppressed and highlighted parts selected by the user. The highlighted overlap helped biologists learn where the interaction happens and how it spreads, particularly when the volume was investigated in an immersive Cave Automatic Virtual Environment (CAVE) for intuitive comprehension of the data.