Fully automatic ventricle detection from cardiac MR images using machine learning

John J. Weng; Ajit Singh; Ming-Yee Chiu

doi:10.1117/12.175086

11 May 1994 Fully automatic ventricle detection from cardiac MR images using machine learning

John J. Weng, Ajit Singh, Ming-Yee Chiu

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175086
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

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.

Citation Download Citation

John J. Weng, Ajit Singh, and Ming-Yee Chiu "Fully automatic ventricle detection from cardiac MR images using machine learning", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175086

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