Object segmentation techniques for use in laboratory visual automation systems

In designing automated systems for interpretation or manipulation of laboratory image data such as that derived from microphotographs, it is often the goal to perform operations that extract information about the structure of objects, and to separate and discern various objects within the data. Measurements of the events, called features, can then be calculated and used for process or statistical analysis. Given a transformation of the pixel based image data into an explicit symbolic representation of the objects (i.e., the creation of objects of interest or Tokens), desired information can be extracted and characterized from the visual data. Simple segmentation schemes often lack the sophistication to deal with intricate or very subtle details of this image data. This paper will discuss advanced techniques useful in obtaining information relevant to the recognition and extraction of objects of interest in laboratory vision automation applications.