Classification of lung disease pattern using seeded region growing

Authors:
James S. J. Wong;Tatjana Zrimec
Affiliations:
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;Centre for Health Informatics, University of New South Wales, Sydney, NSW, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 3
Cited 0

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Abstract

Honeycombing is a disease pattern seen in High-Resolution Computed Tomography which allows a confident diagnosis of a number of diseases involving fibrosis of the lung. An accurate quantification of honeycombing allows radiologists to determine the progress of the disease process. Previous techniques commonly applied a classifier over the whole lung image to detect lung pathologies. This resulted in spurious classifications of honeycombing in regions where the presence of honeycombing was highly improbable. In this paper, we present a novel technique which uses a seeded region growing algorithm to guide the classifier to regions with potential honeycombing. We show that the proposed technique improves the accuracy of the honeycombing detection. The technique was tested using ten-fold cross validation on forty two images over eight different patients. The proposed technique classified regions of interests with an accuracy of 89.7%, sensitivity of 96.6% and a specificity of 88.6%.