Share Email Print

Proceedings Paper

Three-dimensional CT image segmentation by volume growing
Author(s): Dongping Zhu; Richard W. Conners; Philip A. Araman
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The research reported in this paper is aimed at locating, identifying, and quantifying internal (anatomical or physiological) structures, by 3-D image segmentation. Computerized tomography (CT) images of an object are first processed on a slice-by-slice basis, generating a stack of image slices that have been smoothed and pre-segmented. The image smoothing operation is executed by a spatially adaptive filter, and the 2-D pre-segmentation is achieved by a thresholding process whereby each individual pixel in the input image space is consistently assigned a label, according to its CT number, i.e., the gray-level value. Given a sequence of pre-segmented images as 3-D input scene (a stack of image slices), the spatial connectivity that exists among neighboring image pixels is utilized in a volume growing process which generates a number of well-defined volumetric regions or image solides, each representing an individual anatomical or physiological structure in the input scene. The 3-D segmentation is implemented using a volume growing process so that the aspect of pixel spatial connectivity is incorporated into the image segmentation procedure. To initialize the volume growing process for each volumetric region in the input 3-D scene, a seed location for a region is defined and loaded into a queue data structure called seed queue. The volume growing process consists of a set of procedures that perform different operations on the volumetric data of a CT image sequence. Examples of experiment of the described system with CT image data of several hardwood logs are given to demonstrate usefulness and flexibility of this approach. This allows solutions to industrial web inspection, as well as to several problems in medical image analysis where low-level image segmentation plays an important role toward successful image interpretation tasks.

Paper Details

Date Published: 1 November 1991
PDF: 12 pages
Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50364
Show Author Affiliations
Dongping Zhu, Virginia Polytechnic Institute and State Univ. (United States)
Richard W. Conners, Virginia Polytechnic Institute and State Univ. (United States)
Philip A. Araman, USDA Forest Service (United States)

Published in SPIE Proceedings Vol. 1606:
Visual Communications and Image Processing '91: Image Processing
Kou-Hu Tzou; Toshio Koga, Editor(s)

© SPIE. Terms of Use
Back to Top