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Proceedings Paper

Segmenting Echocardiographic Image Sequences Using Expert Labeling
Author(s): Keith J. Dreyer; Ishwar K. Sethi; A. Christian Held; Joseph Simko
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Paper Abstract

Echocardiographic images are generally of poor quality. From a strict gray scale perspective, there are few edges which correspond to the underlying anatomy. Trained ultrasonographers must use considerable a priori information regarding normal human anatomy and use this information extensively to 'fill in' the missing and scanty details of a typical echocardiogram. Due to these constraints, the conventional image processing approaches often fail miserably for echocardiographic images. Knowledge-based approaches for segmenting echo images have been proposed in the recent past; these appear to be much more successful. One of the limitations of knowledge-based approaches is that the acquisition of knowledge and the formation of rules is a relatively difficult task. Moreover, there are instances where experts may have different opinions; this is specially true in the context of echocardiographic images. Additionally, it is much more difficult to codify the expert's knowledge at the image level. Considering these factors plus the typical medical research scenario, where a series of images are acquired at one sitting for a patient, we present in this paper an approach for segmenting echocardiographic image sequences by directly acquiring the expert's knowledge at the image level. The present implementation yields a look up table capturing the expert's knowledge leading to near real time segmentation of echocardiographic images. The results obtained are quite in agreement with the segmentations obtained through trained ultrasonographers.

Paper Details

Date Published: 1 March 1990
PDF: 14 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969794
Show Author Affiliations
Keith J. Dreyer, Wayne State University (United States)
Ishwar K. Sethi, Wayne State University (United States)
A. Christian Held, Wayne State University (United States)
Joseph Simko, Wayne State University (United States)


Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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