Automatic segmentation of lung nodules with growing neural gas and support vector machine

Authors:
Stelmo MagalhãEs Barros Netto;AristóFanes CorrêA Silva;Rodolfo Acatauassú Nunes;Marcelo Gattass
Affiliations:
Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil;Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil;State University of Rio de Janeiro - UERJ, São Francisco de Xavier, 524, Maracanã, 20550-900 Rio de Janeiro, RJ, Brazil;Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. São Vicente, 225, Gávea, 22453-900 Rio de Janeiro, RJ, Brazil
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancer. Unfortunately, this disease is often diagnosed late, affecting the treatment outcome. In order to help specialists in the search and identification of lung nodules in tomographic images, many research centers have developed computer-aided detection systems (CAD systems) to automate procedures. This work seeks to develop a methodology for automatic detection of lung nodules. The proposed method consists of the acquisition of computerized tomography images of the lung, the reduction of the volume of interest through techniques for the extraction of the thorax, extraction of the lung, and reconstruction of the original shape of the parenchyma. After that, growing neural gas (GNG) is applied to constrain even more the structures that are denser than the pulmonary parenchyma (nodules, blood vessels, bronchi, etc.). The next stage is the separation of the structures resembling lung nodules from other structures, such as vessels and bronchi. Finally, the structures are classified as either nodule or non-nodule, through shape and texture measurements together with support vector machine. The methodology ensures that nodules of reasonable size be found with 86% sensitivity and 91% specificity. This results in a mean accuracy of 91% for 10 experiments of training and testing in a sample of 48 nodules occurring in 29 exams. The rate of false positives per exam was of 0.138, for the 29 exams analyzed.