Taxonomic indexes for differentiating malignancy of lung nodules on CT images
Silva, Giovanni Lucca França da; Carvalho Filho, Antonio Oseas de; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Gattass, Marcelo
http://dx.doi.org/10.1590/2446-4740.04615
Res. Biomed. Eng., vol.32, n3, p.263-272, 2016
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Abstract
Introduction: Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods: This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results: The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion: The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.
Keywords
Medical image, Lung nodule diagnosis, Texture analysis, Taxonomic indexes.
References
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Oliveira FSS, Carvalho AO Fo, Silva AC, Paiva AC, Gattass M. Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM. Computers in Biology and Medicine. 2015; 57:42-53. http://dx.doi.org/10.1016/j.compbiomed.2014.11.016. PMid:25528696.
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Parveen SS, Kavitha C. Classification of lung cancer nodules using SVM Kernels. International Journal of Computers and Applications. 2014; 95(25):25-8. http://dx.doi.org/10.5120/16751-7013.
Pienkowski MW, Watkinson AR, Kerby G, Clarke KR, Warwick RM. A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology. 1998; 35(4):523-31. http://dx.doi.org/10.1046/j.1365-2664.1998.3540523.x.
Srichai MB. Lung cancer. In: Naidich DP, Müller NL, Webb WR, editors. Computed tomography and magnetic resonance of the thorax. 4th ed. Philadelphia: Lippincott Williams & Wilkins; 2007.
Tan BB, Flaherty KR, Kazerooni EA, Iannettoni MD. The solitary pulmonary nodule. Chest. 2003; 123(1 Suppl):89S-96S. http://dx.doi.org/10.1378/chest.123.1_suppl.89S. PMid:12527568.
Carvalho AO Fo, Sampaio WB, Silva AC, Paiva AC, Nunes RA, Gattass M. Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artificial Intelligence in Medicine. 2014; 60(3):165-77. http://dx.doi.org/10.1016/j.artmed.2013.11.002. PMid:24332156.
Carvalho AO Fo, Silva AC, Paiva AC, Nunes RA, Gattass M. Lung-nodule classification based on computed tomography using taxonomic diversity indexes and an SVM. Journal of Signal Processing Systems for Signal, Image, and Video Technology. 2016. In press.
Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology. 2011; 2(3):27. http://dx.doi.org/10.1145/1961189.1961199.
Dandil E, Cakiroglu M, Eksi Z, Ozkan M, Kurt OK, Canan A. Artificial neural network-based classification system for lung nodules on computed tomography scans. In: Proceedings of the 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR); 2014; Tunis, Tunisia. USA: IEEE Transaction on Pattern Analysis and Machine Intelligence; 2014. p. 382-6.
Duda RO, Hart PE. Pattern classification and scene analysis. 3rd ed. New York: Wiley; 1973.
Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani N, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal A, Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England Journal of Medicine. 2012; 366(10):883-92. http://dx.doi.org/10.1056/NEJMoa1113205. PMid:22397650.
Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Upper Saddle River: Prentice Hal; 2007.
Gupta B, Tiwari S. Lung cancer detection using curvelet transform and neural network. International Journal of Computers and Applications. 2014; 86(1):15-7. http://dx.doi.org/10.5120/14949-3082.
Hua K-L, Hsu C-H, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and Therapy. 2015; 8:2015-22. http://dx.doi.org/10.2147/OTT.S80733. PMid:26346558.
Instituto Nacional do Câncer. INCA. Tipos de câncer: pulmão [internet]. Brasília: INCA; 2015. [cited 2015 Mar 20]. Available from: http://www2.inca.gov.br/wps/wcm/connect/tiposdecancer/site/home/pulmao
Jabon SA, Raicu DS, Furst JD. Content-based versus semantic-based retrieval: an LIDC case study. SPIE Medical Imaging. 2009; 7263:1-8.
Krewer H, Geiger B, Hall LO, Goldgof DB, Gu Y, Tockman M, Gillies RJ. Effect of texture features in computer aided diagnosis of pulmonary nodules in low-dose computed tomography. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2013; Manchester, United Kingdom. USA: IEEE; 2013. p. 3887-91.
Kumar D, Wong A, Clausi DA. Lung nodule classification using deep features in CT images. In: Proceedings of the 12th Conference on Computer and Robot Vision (CRV); 2015; Halifax, Nova Scotia. USA: IEEE; 2015. p. 133-8.
Kuruvilla J, Gunavathi K. Lung cancer classification using neural networks for CT images. Computer Methods and Programs in Biomedicine. 2014; 113(1):202-9. http://dx.doi.org/10.1016/j.cmpb.2013.10.011. PMid:24199657.
Magurran AE. Measuring biological diversity. African Journal of Aquatic Science. 2004; 29(2):285-6. http://dx.doi.org/10.2989/16085910409503825.
Moura H, Viana G. Phylogenetic Trees Drawing Web Service. In: Proceedings of the 3rd International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies (BIOTECHNO 2011); 2011; Venice, Italy. Wilmington: IARIA; 2011. p. 73-7.
Nascimento LB, Paiva AC, Silva AC. Lung nodules classification in CT images using Shannon and Simpson diversity indices and SVM. In: Perner P. Machine learning and data mining in pattern recognition. Heidelberg: Springer Berlin Heidelberg; 2012. p. 454-66.
Oliveira FSS, Carvalho AO Fo, Silva AC, Paiva AC, Gattass M. Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM. Computers in Biology and Medicine. 2015; 57:42-53. http://dx.doi.org/10.1016/j.compbiomed.2014.11.016. PMid:25528696.
Orozco MH, Villegas OOV, Dominguez HJO, Sanchez VGC. Lung nodule classification in CT thorax images using support vector machines. In: Proceedings of the 12th Mexican International Conference on Artificial Intelligence (MICAI); 2013; Mexico City, Mexico. USA: IEEE; 2013. p. 277-83.
Parveen SS, Kavitha C. Classification of lung cancer nodules using SVM Kernels. International Journal of Computers and Applications. 2014; 95(25):25-8. http://dx.doi.org/10.5120/16751-7013.
Pienkowski MW, Watkinson AR, Kerby G, Clarke KR, Warwick RM. A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology. 1998; 35(4):523-31. http://dx.doi.org/10.1046/j.1365-2664.1998.3540523.x.
Srichai MB. Lung cancer. In: Naidich DP, Müller NL, Webb WR, editors. Computed tomography and magnetic resonance of the thorax. 4th ed. Philadelphia: Lippincott Williams & Wilkins; 2007.
Tan BB, Flaherty KR, Kazerooni EA, Iannettoni MD. The solitary pulmonary nodule. Chest. 2003; 123(1 Suppl):89S-96S. http://dx.doi.org/10.1378/chest.123.1_suppl.89S. PMid:12527568.
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