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A two layer case-based reasoning architecture for medical image understanding
 

Summary: A two layer case-based reasoning architecture for
medical image understanding
Morten Grimnes and Agnar Aamodt
Department of Informatics
Norwegian University of Science and Technology
N-7034 Trondheim, Norway
Abstract. The paper describes a novel architecture for image understanding. It
is based on acquisition of radiologist knowledge, and combines low-level struc-
ture analysis with high-level interpretation of image content, within a task-ori-
ented model. A case based reasoner working on a segment case-base contains
the individual image segments. These cases with labels are considered indexes
for another case based reasoner working on an organ interpretation case base.
Both are Creek type case based reasoners, here operating within a propose-cri-
tique-modify task structure. Methods for criticizing suggested interpretations by
way of explanation, and how interpretations may be modified, are presented. An
example run illustrates the system architecture and its key concepts.
1 Introduction
Image understanding has turned out to be a very difficult application task for AI
methods. Methods exist that are able to do edge detection, and to some extent object
identification, but methods for interpreting and understanding the content and meaning

  

Source: Aamodt, Agnar - Department of Computer and Information Science, Norwegian University of Science and Technology

 

Collections: Computer Technologies and Information Sciences