Paper
15 May 2003 Towards the automatic detection of large misregistrations
Author Affiliations +
Abstract
In many cases three-dimensional anatomical and functional images (SPECT, PET, MRI, CT) ought to be combined to determine the precise nature and extent of lesions in many parts of the body. The images must be adequately aligned prior to any addition, substraction, or any other combination; registration can be done by experienced radiologists via visual inspection, mental reorientation and overlap of slices, or by an automated registration algorithm. To be useful clinically, the latter case requires validation. The human capacity to evaluate registration results visually is limited and the process is time consuming. This paper describes an algorithmic procedure that distinguishes between badly misregistered pairs and those likely to be clinically useful. Our algorithm used brain and/or skin/air contours and a function based on the principal axes of the contour volumes. The results of the present study indicate that the measure based on the combination of brain and skin contours and a principal-axis function is a good first step to reduce the number of badly registered images reaching the clinician.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Claudia E. Rodriguez-Carranza and Murray H. Loew "Towards the automatic detection of large misregistrations", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481383
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KEYWORDS
Brain

Image registration

Skin

Computed tomography

Visualization

Electronic filtering

Image segmentation

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