COMPUTATIONAL TOOLS 113
problems in image analysis can be solved, including image registration, shape analysis, and volume and
area estimation. A specific laboratory example would be the segmentation of spots on two-dimensional
electrophoresis gels.
There is no common method or class of methods applicable to even the majority of images. Segmen-
tation is easiest when the objects of interest have intensity or edge characteristics that allow them to be
separated from the background and noise, as well as from each other. For example, an MRI image of the
human body would be relatively easy to segment for bones: all pixels with intensity below a given
threshold would be eliminated, leaving mostly the pixels associated with high-signal-intensity bone.
Generally, edge detection depends on a search for intensity gradients. However, it is difficult to find
gradients when, as is usually the case in biomedical images, intensities change only gradually between
the structure of interest and the surrounding structure(s) from which it is to be extracted. Continuity
and connectivity are important criteria for separating objects from noise and have been exploited quite
widely.
A number of different approaches to image segmentation are described in more detail by Pham et al.^148
4.4.12.3 Image Registration^149
Different modes of imaging instrumentation may be used on the same object because they are
sensitive to different object characteristics. For example, an X-ray of an individual will produce different
information than a CT scan. For various purposes, and especially for planning surgical and radiation
treatment, it can be important for these images to be aligned with each other, that is, for information
from different imaging modes to be displayed in the same locations. This process is known as image
registration.
There are a variety of techniques for image registration, but in general they can be classified based
on the features that are being matched. For example, such features may be external markers that are
fixed (e.g., on a patient’s body), internal anatomic markers that are identifiable on all images, the center
of gravity for one or more objects in the images, crestlines of objects in the images, or gradients of
intensity. Another technique is minimization of the distance between corresponding surface points of a
predefined object. Image registration often depends on the identification of similar structures in the
images to be registered. In the ideal case, this identification can be performed through an automated
segmentation process.
Image registration is well defined for rigid objects but is more complicated for deformable objects or
for objects imaged from different angles. When soft tissue deforms (e.g., because a patient is lying on his
side rather than on his back), elastic warping is required to transform one dataset into the other. The
difficulty lies in defining enough common features in the images to enable specifying appropriate local
deformations.
An example of an application in which image registration is important is the Cell-Centered Database
(CCDB).^150 Launched in 2002, the CCDB contains structural and protein distribution information derived
from confocal, multiphoton, and electron microscopy for use by the structural biology and neuroscience
communities. In the case of neurological images, most of the imaging data are referenced to a higher level
of brain organization by registering their location in the coordinate system of a standard brain atlas.
Placing data into an atlas-based coordinate system provides one method by which data taken across scales
(^148) D.L. Pham, C. Xu, and J.L. Prince, “Current Methods in Medical Image Segmentation,” Annual Review of Biomedical Engineer-
ing 2:315-338, 2000.
(^149) Section 4.4.12.3 is adapted from National Research Council, Mathematics and Physics of Emerging Biomedical Imaging, National
Academy Press, Washington, DC, 1996.
(^150) See M.E. Martone, S.T. Peltier, and M.H. Ellisman, “Building Grid Based Resources for Neurosciences,” unpublished paper
2003, National Center for Microscopy and Imaging Research, Department of Neurosciences, University of California, San Diego,
San Diego, CA, and http://ccdb.ucsd.edu/CCDB/about.shtml.