Cognitive hierarchical active partitions in distributed analysis of medical images

Abstract

Semantic oriented image analysis has always been considered a challenging task, as it does not concentrate on segmentation process itself, but on interpretation of various image fragments. Contextuality of the process has recently gained significant research interest, with knowledge from image domain being repeatedly highlighted as crucial in achieving satisfactory method effectiveness. The present article elaborates on the recently described contextual hierarchical active partitions (CHAP) technique and its distributed reformulation. CHAP framework lets domain knowledge to be injected to the automated medical study analysis in a seamless and transparent manner by enabling a human expert to interactively participate in the process, e.g. by solving subtasks currently too difficult to solve by automated agents. Separation of agents makes it easy to design complex analysis algorithms from well tested and predictable components making it easy to inject human expertise at any point as needed. © 2012 The Author(s).