Abstract
A knowledge based system for detection of honeycombing patterns in HRCT lung images is described. In the system, rules generated by machine learning on low level image pixel-based features and heuristic rules from the domain expert on high level region-based features are combined using a generalized ripple down rules (GRDR) framework. Results demonstrate that the systems' performance can be incrementally improved. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Singh, P. K., & Compton, P. (2005). Combining machine learned and heuristic rules using GRDR for detection of honeycombing in HRCT lung images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3682 LNAI, pp. 131–137). Springer Verlag. https://doi.org/10.1007/11552451_18
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