Combining Machine Learned and Heuristic Rules Using GRDR for Detection of Honeycombing in HRCT Lung Images

摘要

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.