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Proceedings Paper

Segmentation of anatomical structures in x-ray computed tomography images using artificial neural networks
Author(s): Di Zhang; Daniel J. Valentino
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Paper Abstract

Hierarchies of artificial neural networks(ANN's) were trained to segment regularly-shaped and constantly-located anatomical structures in x-ray computed tomography (CT) images. These neural networks learned to associate a point in an image with the anatomical structure containing the point using the image pixel intensity values located in a pattern around the point. The single layer ANN and the bilayer and multi-layer hierarchies of neural networks were developed and evaluated. The hierarchical Artificial Neural Networks(HANN's) consisted of a high-level ANN that identified large-scale anatomical structures (e.g., the head or chest), whose result was passed to a group of neural networks that identified smaller structures (e.g., the brain, sinus, soft tissue, skull, bone, or lung) within the large-scale structures. The ANN's were trained to segment and classify images based on different numbers of training images, numbers of sampling points per image, pixel intensity sampling patterns, hidden layer configuration. The experimental results indicate that multi-layer hierarchy of ANN's trained with data collected from multiple image series accurately classified anatomical structures in unknown chest and head CT images.

Paper Details

Date Published: 9 May 2002
PDF: 13 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467133
Show Author Affiliations
Di Zhang, Univ. of California/Los Angeles (United States)
Daniel J. Valentino, Univ. of California/Los Angeles (United States)

Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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