Shape description of biological objects via stereo light microscopy

Computational stereo vision techniques are applied to the analysis of the three-dimensional (3-D) shape of biological specimens imaged through a stereo light microscope. 3-D shape descriptions are derived by integrating binocular and monocular measurements. Microscopic biological objects viewed through a light microscope generally have ill-defined boundaries in shape and reflectance and often exhibit transparency. In addition, the limited illumination available in the light microscope often makes it difficult to obtain images of sufficiently high contrast. In the approach presented, a new matching algorithm is introduced using intensity gradient information to solve the problem. Shape descriptions are computed by exploiting a number of additional processing steps. Two different classes of shapes are considered, which lead to two distinct approaches for 3-D analysis: solid objects and vascular networks. The first class of objects is described using a visible surface representation, while the second is expressed in terms of a set of space curves. The efficacy of each approach is demonstrated using microscopic-scale solid and microvascular specimens