3DMA-Rock
Three Dimensional Image Analysis
W. B. Lindquist
Three dimensional, non-invasive imaging technology is hot! It has vital
applications in medical diagnosis, scientific inquiry, industry, and "homeland"
security. In medical diagnosis, computer-aided X-ray tomography (CT) and
magnetic resonance imaging (MRI) are the dominant 3D imaging techniques,
while serious efforts are undergoing to improve acoustic tomography. In
biological fields, especially neroscience, three dimensional laser-scanning
microscopy, either through confocal or multi-photon means, is the dominant
3D imaging tool. In biomedical engineering CT imaging (bone, circulatory
system, lungs) and MRI (heart) are the dominant imaging paradigms. Whole
animal MRI and CT systems have recently come on-line for small animal
imaging. Brain activity studies are dominated by PET-scanning techology.
In material science, where dosage requirments are less stringent, the
penetration capability of CT scanning is important. At molecular length
scales, in physics and chemistry, electron tomography is being rapidly
developed. The fiber industry is adopting CT imaging as a primary means
of examining woven and non-woven fiber products. Global terrorism has
spawned a security industry that relies heavily on X-ray technology.
Here, limited angle CT scanning holds great promise for improved error detection
capability over that provided by classical, single view, 2D X-ray imaging.
Of necessity, primary 3D imaging research has focused on production and
improvement of imaging hardware and on development of the algorithms necessary
to produce the 3D image from the recorded data. One example of this is the
historical development from first generation CT (pencil beam, single pixel
recording) through to current "n-th generation" medical CT scanners
(cone-shaped X-ray beam and areal CCD cameras mounted on a frame that performs
a helical trace around the patient). However, mature two dimensional imaging
technology (including stereoscopic imaging) such as satellite photography
has demonstrated that automation of the analysis of the contents
of images, especially feature recognition, is vital for large scale usage of
imaging technology.
(In mature imaging technologies, a great quantitiy of images can be produced
cheaply, overwhelming manual-based efforts to analyze the images.) In addition,
automated image analysis reduces user-bias and fatique-related errors
associated with human analysis. Character recognition, biometric
recognition of faces, fingerprints - even voices (as acoustic signals are
indeed ``images'') are all recognized as important automated feature
recognition problems.
Three dimensional feature recognition is harder than 2D recognition, but it
has at least the "shoulders" of the work in 2D to "stand upon". The 3DMA
algorithmic suite has focussed on four areas of application for the development
of feature recognition ability in three dimensional imaging technologies.
These are (in historical order), two phase geologic media (rock),
neurons, fiber products and trabecular bone.
Feature detection and automated quantification of parameters of interest in
geologic media is very similar to that in bone and also includes
analysis of concrete, soil, and industrial composite materials.
In general, a 3D imaging problem can be reduced to the following sequence of
steps.