Lung structure classification using 3D geometric measurements and SVM

Authors:
João Rodrigo Ferreira Da Silva Sousa;Aristófanes Corrêa Silva;Anselmo Cardoso De Paiva
Affiliations:
Federal University of Maranhão, UFMA, Bacanga, São Luís, MA, Brazil;Federal University of Maranhão, UFMA, Bacanga, São Luís, MA, Brazil;Federal University of Maranhão, UFMA, Bacanga, São Luís, MA, Brazil
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

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Abstract

In this paper, a set of three features for aiding classification of lung nodule bearing candidates based upon morphological characteristics is proposed. Metrics were validated using Support Vector Machine (SVM) technique as classifier. Preliminary results indicate the efficiency of the adopted measurements, taking into account the sensitivity and specificity high rates obtained from the studied samplings.