Automatic vessel wall contour detection and quantification of wall thickness in in‐vivo MR images of the human aorta
IM Adame, RJ Van Der Geest… - Journal of Magnetic …, 2006 - Wiley Online Library
Journal of Magnetic Resonance Imaging: An Official Journal of the …, 2006•Wiley Online Library
Purpose To develop an automated technique to trace the contours of the lumen and outer
boundary of the aortic wall, and measure aortic wall thickness in axial MR images. Materials
and Methods The algorithm uses prior knowledge of vessel wall morphology. A geometrical
model (ellipse) is deformed, translated and rotated to obtain a rough approximation of the
contours. Model‐matching is based on image gradient measurements. To enhance edges,
the images were preprocessed using gray‐level stretching. Refinement is performed by …
boundary of the aortic wall, and measure aortic wall thickness in axial MR images. Materials
and Methods The algorithm uses prior knowledge of vessel wall morphology. A geometrical
model (ellipse) is deformed, translated and rotated to obtain a rough approximation of the
contours. Model‐matching is based on image gradient measurements. To enhance edges,
the images were preprocessed using gray‐level stretching. Refinement is performed by …
Purpose
To develop an automated technique to trace the contours of the lumen and outer boundary of the aortic wall, and measure aortic wall thickness in axial MR images.
Materials and Methods
The algorithm uses prior knowledge of vessel wall morphology. A geometrical model (ellipse) is deformed, translated and rotated to obtain a rough approximation of the contours. Model‐matching is based on image gradient measurements. To enhance edges, the images were preprocessed using gray‐level stretching. Refinement is performed by means of dynamic programming. Wall thickness is computed by measuring the distance between inner and outer contour of the aortic wall.
Results
The algorithm has been tested on high‐resolution axial MR images from 28 human subjects of the descending thoracic aorta. The results demonstrate: High correspondence between automatic and manual area measurements: lumen (r = 0.99), outer (r = 0.96), and wall thickness (r = 0.85).
Conclusion
Though further optimization is required, our algorithm is a powerful tool to automatically draw the boundaries of the aortic wall and measure aortic wall thickness in aortic wall devoid of major lesions. J. Magn. Reson. Imaging 2006. © 2006 Wiley‐Liss, Inc.
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