Title :
Representing medical images with partitioning trees
Author :
Subramanian, K.R. ; Naylor, Bruce
Author_Institution :
AT&T Bell Lab., Murray Hill, NJ, USA
Abstract :
The binary space partitioning tree is a method of converting a discrete space representation to a particular continuous space representation. The conversion is accomplished using standard discrete space operators developed for edge detection, followed by a Hough transform to generate candidate hyperplanes that are used to construct the partitioning tree. The result is a segmented and compressed image represented in continuous space suitable for elementary computer vision operations and improved image transmission/storage. Examples of 256×256 medical images for which the compression is estimated to range between 1 and 0.5 b/pixel are given
Keywords :
Hough transforms; data compression; edge detection; image segmentation; medical image processing; tree data structures; trees (mathematics); Hough transform; binary space partitioning tree; candidate hyperplanes; compressed image; continuous space representation; edge detection; elementary computer vision operations; image representation; improved image transmission/storage; medical images; standard discrete space operators; Biomedical imaging; Computer vision; Discrete transforms; Image coding; Image communication; Image converters; Image edge detection; Image segmentation; Image storage; Standards development;
Conference_Titel :
Visualization, 1992. Visualization '92, Proceedings., IEEE Conference on
Conference_Location :
Boston, MA
Print_ISBN :
0-8186-2897-9
DOI :
10.1109/VISUAL.1992.235214
