114 CATALYZING INQUIRY
and distributed across multiple resources can be compared reliably. Through the use of atlases and tools
for surface warping and image registration, it is possible to express the location of anatomical features or
signals in terms of a standardized and quantitative coordinate system, rather by using terms that describe
objects in the field of view. The expression of brain data in terms of atlas coordinates also allows it to be
transformed spatially to provide alternative views that may offer additional information (e.g., flat maps or
additional parcellation schemes). Finally, a standard coordinate system allows the same brain region to be
sampled repeatedly to allow data to be accumulated over time.
4.4.12.4 Image Classification
Image classification is the process through which a set of images can be sorted into meaningful
categories. Categories can be defined through low-level features such as color mix and texture patterns or
through high-level features such as objects depicted. As a rule, low-level features can be computed with
little difficulty, and a number of systems have been developed that take advantage of such features.^151
However, users are generally much more interested in semantic content that is not easily repre-
sented in such low-level features. The easiest method to identify interesting semantic content is simply
to annotate an image manually with text, although this process is quite tedious and is unlikely to
capture the full range of content in an image. Thus, automated techniques hold considerable interest.
The general problem of automatic identification of such image content has not been solved. One
approach described by Huang et al. relies on supervised learning to classify images hierarchically.^152
This approach relies on using good low-level features and then performing feature-space reconfiguration
using singular value decomposition to reduce noise and dimensionality. A hierarchical classification
tree can be generated from training data and subsequently used to sort new images into categories.
A second approach is based on the fact that biological images often contain branching struc-
tures. (For example, both muscle and neural tissue contain blood vessels and dendrites that are
found in branching structures.) The fractal dimensionality of such structures can then be used as a
measure of similarity, and images that contain structures of similar fractal dimension can be
grouped into categories.^153
4.5 Developing Computational Tools,
The computational tools described above were once gleams in the eye of some researcher. Despite
the joy and satisfaction felt when a prototype program supplies the first useful results to its developer,
it is a long, long way to converting that program into a genuine product that is general, robust, and
useful to others. Indeed, in his classic text The Mythical Man-Month (Addison-Wesley, Reading, MA,
1995), Frederick P. Brooks, Jr., estimates the difference in effort necessary to create a programming
systems product from a program as an order of magnitude.
Some of the software engineering considerations necessary to turn a program into a product include
the following:
- Quality. The program, of course, must be as free of defects as possible, not only in the sense of
running without faults, but also of precisely implementing the stated algorithm. It must be tested for all
(^151) See, for example, M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D.
Petkovic, D. Steele, and P. Yanker, “Query by Image and Video Content: The QBIC System,” IEEE Computer 28(9):23-32, 1995,
available at http://wwwqbic.almaden.ibm.com/.
(^152) J. Huang, S.R. Kumar, and R. Zabih, “An Automatic Hierarchical Image Classification Scheme,” ACM Conference on
Multimedia, Bristol, England, September 1998. A revised version appears in EURASIP Journal on Applied Signal Processing, 2003,
available at http://www.cs.cornell.edu/rdz/Papers/Archive/mm98.pdf.
(^153) D. Cornforth, H. Jelinek, and L. Peich, “Fractop: A Tool for Automated Biological Image Classification,” available at http://
csu.edu.au/~dcornfor/Fractop_v7.pdf.