Author_Institution :
Dept. of Biomath., Inst. of Physiol., Prague, Czech Republic
Abstract :
Presents a short introduction to stereology and an overview of stereological methods focused on estimation of geometrical characteristics such as volume, surface area, and number. Disector and fakir methods are described in more detail as examples of more recent stereological methods. Computer-aided stereology is also discussed. The authors attempt to show the advantages of stereological methods, beginning with “old” methods such as the Cavalieri principle or point-counting method, up to the most recent methods. In principle, design-based stereological methods are the choice in morphometry whenever unbiased estimates of geometrical characteristics of 3-D structure are required to be obtained in an efficient way. Manual evaluation of sections (or projections) by using stereological test systems is usually preferable to manual tracing of profile contours as well as to automatic segmentation by image analysis. The former is often inefficient and unprecise, and the second is not always feasible, as in biology where the problems with computer-aided segmentation of structural components are frequent. In stereology, the principles, being based on rigorous theoretical results, are precisely defined, which enables not only experimental but also theoretical studies of their efficiency. Thus, it is possible to find for each specific measurement efficient stereological procedures and sampling designs that give reliable results
Keywords :
area measurement; image segmentation; medical image processing; reviews; stereo image processing; volume measurement; 3-D structures; Cavalieri principle; automatic segmentation; biomedicine; geometrical characteristics; image analysis; manual tracing; morphometry; point-counting method; profile contours; sampling designs; stereological methods; structural components; unbiased estimates; Biological system modeling; Biology computing; Biomedical measurements; Cells (biology); Engine cylinders; Humans; Image reconstruction; Neurons; Solid modeling; Surface reconstruction;