AbstractID: 9152 Title: A Robust 3D Morphological Algorithm for Automatic Skin Contour Detection on CT Images 915222895.doc
External skin contouring is the required initial step in many 3D image-based
applications, such as treatment planning systems, 3D surface-based fusion/registration
applications, and surface rendering algorithms. Local or optimal edge detection
algorithms can be used for this purpose, but the post processing steps such as the
joint/branch disconnections and the edge classifications are known to be very difficult
tasks and these algorithms generally produce open contours. In this study we propose
a new automatic method to segment the patient external skin contours on the radiation
therapy planning CT images by employing a series of 2D/3D morphological image
processing algorithms. The method starts with the iterative global thresholding to
binarize the input CT image and the result binary image goes through several
morphological algorithms; Opening, Connected Component Extraction, and Boundary
Extraction. The Opening disconnects the peripheral objects such as the couch and the
head masks and the Connected Component Extraction collects connected pixels and
label them. Finally the boundary information is represented using the Polygonal
Contour Approximation method. In some cases, it generates artifacts when the couch
or the supporting devices share a wide contact with the patient surface. These artifacts
are removed by detecting the couch position using Hough Transform. This new
method was extensively tested on the CT images of over 500 patients treated in our
institute in 2002. The result showed 98% correct segmentations and 25% of them
needed the Hough Transform to remove the artifacts.
Jinkoo Kim
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3/5/03