Share Email Print

Proceedings Paper

Automatic detection of boundaries of brain tumor
Author(s): Yi Lu; Lucia J. Zamorano; Federico Moure; Steven G. Schlosser
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

An important computational step in computer-aided neurosurgery is the extraction of boundaries of lesions in a series of images. Currently in many clinical applications, the boundaries of lesions are traced manually. Manual methods are not only tedious but also subjective, leading to substantial inter- and intraobserver variability. Furthermore, recent studies show that human observation of a lesion is not sufficient to guarantee accurate localization. With clinical images, possible confusion between lesions and coexisting normal structures (like blood vessels) is a serious constraint on an observer's performance. Automatic detection of lesions is a non-trivial problem. Typically the boundaries of lesions in CT images are of single-pixel width, and the gradient at the lesion boundary varies considerably. As many studies show, these characteristics of lesions within CT images, in conjunction with the generally low signal-to-ratio of CT images, render simple boundary detection techniques ineffective. In this paper we characterize the brain lesions in CT images, and describe a knowledge-guided boundary detection algorithm. The algorithm is both data- and goal-driven.

Paper Details

Date Published: 1 June 1992
PDF: 12 pages
Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); doi: 10.1117/12.59463
Show Author Affiliations
Yi Lu, Environmental Research Institute of Michigan (United States)
Lucia J. Zamorano, Wayne State Univ. (United States)
Federico Moure, Wayne State Univ. (United States)
Steven G. Schlosser, Environmental Research Institute of Michigan (United States)

Published in SPIE Proceedings Vol. 1652:
Medical Imaging VI: Image Processing
Murray H. Loew, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?