Paper
1 November 1990 Application of a conditional digital erosion operator in automated segmentation of the anatomy in MR images of the lower spine
Dennis P. Pfeifer, Michael P. Chwialkowski, Peter E. Shile, Robert W. Parkey, Ronald M. Peshock M.D.
Author Affiliations +
Abstract
In binary image processing digital erosion has been used successfully for the removal of small sporadic elements from images at the sacrifice of losing edge detail in objects of interest. Typically an erosion operation is followed by a dilation process generating the familiar opening operation. This step which attempts to restore the original edge information works well in many applications where the edge detail of the original object is unimportant. In this paper a conditional digital erosion operation designed specifically for the processing of inherently noisy magnetic resonance (MR) images of the lower spine is presented. This operation in essence preforms a test for the removal of entire objects prior to the erosion. The method was applied to edge enhanced images and resulted in the elimination of edges of insufficient span regardless of their thickness or absolute length. The developed method has proven useful for the removal of sporadic noise and the retention of small anatomic edge boundaries in edge enhanced MR images of the lower spine. The results of images processed with the operator favorably compare with those obtained from morphological (Minkowski) open and close operations.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dennis P. Pfeifer, Michael P. Chwialkowski, Peter E. Shile, Robert W. Parkey, and Ronald M. Peshock M.D. "Application of a conditional digital erosion operator in automated segmentation of the anatomy in MR images of the lower spine", Proc. SPIE 1350, Image Algebra and Morphological Image Processing, (1 November 1990); https://doi.org/10.1117/12.23587
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Cited by 2 scholarly publications.
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KEYWORDS
Image processing

Image segmentation

Spine

Magnetic resonance imaging

Binary data

Surgery

Image enhancement

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