Abstract
Novel medical imaging techniques have been introduced over the last decade. They provide for the non-invasive study of internal structures and their dynamic behavior because of the large bandwidth characteristics of the new sensors. The processing and automatic interpretation of such images, however, lags far behind. We suggest herein a synergetic approach where novel techniques derived from artificial intelligence (AI), computer vision (CV) and neural networks (NN) could be integrated towards robust and automatic image interpretation. Such image interpretation would be relevant for the analysis of internal organs and/or tissue and to the understanding of time-varying (dynamic) imagery. Within the medical area it is important that such analysis be fault-tolerant (low sensitivity) to noisy data, occlusion/overlap, geometric distortions, and still be efficient.
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© 1992 Springer-Verlag Berlin Heidelberg
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Sood, A., Wechsler, H. (1992). Fault-Tolerant Medical Image Interpretation. In: Todd-Pokropek, A.E., Viergever, M.A. (eds) Medical Images: Formation, Handling and Evaluation. NATO ASI Series, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77888-9_22
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DOI: https://doi.org/10.1007/978-3-642-77888-9_22
Publisher Name: Springer, Berlin, Heidelberg
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