AUTOMATIC ANALYSIS OF 3-D AND 4-D RADIOLOGICAL IMAGES

Project: Research project

Description

DESCRIPTION: Many radiological imaging
modalities now exist that generate three-dimensional (3-D) and four
dimensional (4-D) images of the anatomy. Unfortunately, techniques for
analyzing and making measurements on such data sets remain cumbersome.
Simplying the analysis of 3-D/4-D images is critical to being able to
assess the long-term utility of 3-D/4-D imaging modalities. This proposal
addresses this general issue. It is believed that an automated analysis
system, assisted by operator-supplied cues, can more thoroughly, more
accurately, and more efficiently extract anatomical information from a
3-D/4-D radiological image than purely manual techniques. The applicants propose to devise an interactive automated 3-D/4-D
radiological image analysis system by which the operator can define many
types of iconic and symbolic problem cues through a flexible display
interface with the cues going into an object-oriented model, the
interactive dynamic scene (IDS) model. Drawing on the problem information
stored in the IDS model, an automatic scene-analysis (ASA) engine extracts
the desired anatomical regions. The ASA engine uses a multi-stage analysis
procedure that exploits organ morphological, geometrical (shape-based), and
functional characteristics; it also uses global topological
(organ-to-organ) constraints. The early emphasis of this project is on devising the algorithms for the
ASA engine and IDS model. As methods mature, the applicants propose to
construct a working prototype of the system. Validation would be done
throughout and would focus on four applications: (1) extraction of the
endocardial and epicardial borders from 3-D/4-D X-ray computed tomography
(CT) images; (2) analysis of true 3-D angiograms; (3) extraction and
analysis of the upper airway in 3-D Magnetic Resonance Imaging (MRI)
images; and (4) analysis of congenital heart defects in 3-D MRI images.
While algorithms are being devised, validation tests, involving a small
subset of data, would be done to establish the algorithm functionality.
Later, as algorithms mature, detailed tests, using larger sequences of
data, would be done to quantitatively compare automatically generated
results to manually generated results.
StatusFinished
Effective start/end date1/1/9112/31/96

Funding

  • National Institutes of Health
  • National Institutes of Health: $109,860.00
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health: $77,473.00

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Engines
Image analysis
Tomography
Dynamic models
Imaging techniques
X rays
Defects
Magnetic Resonance Imaging