document: The use of visual search for knowledge gathering in image decision support This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements . The framework involves the construction of a generic image feature extraction library , from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis . The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features . The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure . The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers . In all cases , the accuracy of the students improved from near random decision making -LRB- 33 % -RRB- to accuracies ranging from 50 % to 68 %
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