Representing medical images with partitioning trees

Author

Subramanian, K.R. ; Naylor, Bruce

Author_Institution

AT&T Bell Lab., Murray Hill, NJ, USA

fYear

1992

fDate

19-23 Oct 1992

Firstpage

147

Lastpage

154

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Visualization, 1992. Visualization '92, Proceedings., IEEE Conference on

Conference_Location

Boston, MA

Print_ISBN

0-8186-2897-9

Type

conf

DOI

10.1109/VISUAL.1992.235214

Filename

235214