In designing automated systems for interpretation of micrographs, it is often the goal to separate and discern various objects within the image data. After segmentation, measurements of the objects, called features, can then be calculated and used for process or statistical analysis. Simple segmentation schemes based on single threshold operations, often lack the sophistication to deal with intricate or subtle details of the image data, or require user intervention in the threshold selection process. This talk will present and discuss advanced techniques which adapt to the data, and which can operate autonomously without human supervision.
Segmentation is a process which transforms pixel based image data into symbolic descriptors representing groups of pixel elements. These descriptors are called objects and take the form of lines, regions, polygons, points, windows of interests, or other unique representations. Most laboratory image analysis software packages utilize segmentation schemes based on the principle of intensity analysis.