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Proceedings Paper

Fully automatic ventricle detection from cardiac MR images using machine learning
Author(s): John J. Weng; Ajit Singh; Ming-Yee Chiu
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Paper Abstract

The objective of this work is to develop a technique that is reliable, adaptive, versatile to solve the problem of region detection for a relatively wide class of medical images. Learning is essential in approaching this objective. In order to fully use the properties of the medical images and obtain a high efficiency, we compute a binary visual attention map which contains the region of interest as well as other things. The learning takes place in two stages: (1) learning for automatic selection of threshold values; (2) learning for automatic selection of the region of interest from candidate regions in the attention map. The result from the second stage is evaluated based on a learned cost measure and the outcome is fed back to the first stage when necessary. This feedback enhances the reliability of the entire system. Experiments have been conducted to approximately locate the endocardium boundaries of the left and right ventricles from gradient-echo MR images.

Paper Details

Date Published: 11 May 1994
PDF: 12 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175086
Show Author Affiliations
John J. Weng, Michigan State Univ. (United States)
Ajit Singh, Siemens Corp. Research, Inc. (United States)
Ming-Yee Chiu, Siemens Corp. Research, Inc. (United States)

Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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