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Novel Technology for Lung Tumor Detection Using Nanoimage

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  • S. G. Armato, F. Li, M. L. Giger, K. Doi, S. Sone, and H. Mac Mahon, “Lung cancer: Performance of automated lung nodule detection applied to cancers missed in a CT screening program,” Radiology, 685–92, 2002. doi: 10.1148/radiol.2253011376
  • S. G. Armato III, et al., “Lung image database consortium: Developing a resource for the medical imaging research community,” Radiology, Vol. 232, no. 3, pp. 739–48, 2004. doi: 10.1148/radiol.2323032035
  • K. D. Miller, R. L. Siegel, and A. Jemal, “Cancer statistics,” CA Cancer J. Clin., Vol. 01, pp. 00–20, 2017.
  • A. Kadir, and A. Susanto. Pengolahan Citra Teori dan Aplikasi. 1st ed. Yogyakarta, Indonesia: ANDI, 2012.
  • A. Ali, and A. Farag, “Automatic lung segmentation of volumetric low-dose CT scans using graph cuts,” Adv. Visual Comput., Vol. 5358, pp. 258–67, 2008. doi: 10.1007/978-3-540-89639-5_25
  • H. Arimura, T. Magome, Y. Yamashita, and D. Yamamoto, “Computer-aided diagnosis systems for brain diseases in magnetic resonance images,” Algorithms, Vol. 2, no. 3, pp. 925–52, 2009. doi: 10.3390/a2030925
  • T. Jain, and K. Kewal, “Nanotechnology in clinical laboratory diagnostics,” Clinica. Chimica. Acta., Vol. 358, pp. 37–54, 2005. doi: 10.1016/j.cccn.2005.03.014
  • D. T. Lin, C. R. Yan, and W. T. Chen, “Autonomous detection of pulmonary nodules on CT images with a neural network-based Fuzzy system,” Comput. Med. Imaging Graph., Vol. 29, no. (6), pp. 447–458, 2005. doi: 10.1016/j.compmedimag.2005.04.001
  • M. Ranjita, A. Sarbari, and K. S. Sanjeeb, “Cancer nanotechnology: Application of nanotechnology in cancer therapy's,” Drug Discov. Today, Vol. 15, pp. 19–20, 2010.
  • R. Zeineldin, “Biomaterials for cancer therapeutics,” Diagnosis, Prevention and Therapy, pp. 137–164, 2013. doi: 10.1533/9780857096760.3.137
  • Adem Beriso, “Early cancer detection and treatment with nanotechnology,” J. Nanomater. Mol. Nanotechnology, Vol. 6, no. 5, 2017. doi: 10.4172/2324-8777.1000233
  • D. Naveen Raju, S. Shanmugan, and M. Anto Bennet, “On feature image recognition of melanoma using nanotechnology applications,” Mech. Mater. Sci. Eng., 2017. doi: 10.2412/mmse.82.25.192
  • Xiao-Jie Chen, Xue-Qiong Zhang, Qi Liu, Jing Zhang, and Gang Zhou, “Nanotechnology: A promising method for oral cancer detection and diagnosis,” J. Nanobiotechnology, pp. 1–17, 2018.
  • S. Diciotti, G. Picozzi, M. Falchini, M. Mascalchi, N. Villari, and G. Valli. 3-D segmentation algorithm of small lung nodules in spiral CT images. IEEE Trans. Inf. Technol. Biomed., Vol. 12, no. 1, pp. 7–19, 2008. doi: 10.1109/TITB.2007.899504
  • G. De Nunzio, et al., “Automatic lung segmentation in CT images with accurate handling of the hilar region,” J. Digital Imaging, Vol. 24, pp. 11–27, 2011. doi: 10.1007/s10278-009-9229-1
  • D. M. Campos, A. Simões, I. Ramos, and A. Campilho. Feature-based supervised lung nodule segmentation. no. Ci, pp. 23–26, 2014.
  • A. M. R. Schilham, K. Murphy, B. van Ginneken, B. J. de Hoop, H. a. Gietema, and M. Prokop. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal., Vol. 13, no. 5, pp. 757–770, 2009.
  • H. Shao, L. Cao, and Y. Liu. “A detection approach for solitary pulmonary nodules based on CT images,” in: 2nd International Conference on Computer Science and Network Technology, IEEE, Dec 29–31, Changchun, China, pp. 1253–57, 2012.
  • I. Sluimer, M. Prokop, and B. van Ginneken, “Toward automated segmentation of the pathological lung in CT,” IEEE Trans. Med. Imaging, Vol. 24, pp. 1025–38, 2005. doi: 10.1109/TMI.2005.851757
  • Q. Wang, E. Song, R. Jin, P. Han, X. Wang, Y. Zhou, and J. Zeng, “Segmentation of lung nodules in computed tomography images using dynamic programming and multi direction fusion techniques,” Acad. Radiol., Vol. 16, pp. 678–88, 2009. doi: 10.1016/j.acra.2008.12.019
  • X. Ye, X. Lin, J. Dehmeshki, G. Slabaugh, and G. Beddoe, “Shape-based computer-aided detection of lung nodules in thoracic CT images,” IEEE Trans. Biomed. Eng., Vol. 56, pp. 1810–20, 2009. doi: 10.1109/TBME.2009.2017027
  • B. Zhao, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke, “Three-dimensional multi criterion automatic segmentation of pulmonary nodules of helical computed tomography images,” Opt. Eng., Vol. 38, pp. 1340–47, 1999a. doi: 10.1117/1.602176
  • A. A. Farag, H. E. Abd El Munim, J. H. Graham, and A. A. Farag, “A Novel Approach for Lung Nodules Segmentation in Chest CT using level Sets,” IEEE Trans. Image Process., Vol. 22, no. 12, pp. 5202–13, 2013. doi: 10.1109/TIP.2013.2282899
  • V. Kalpana, D. S. Varadarajan, and T. Milinda purna, “Performance evaluation of DICOM lung ROI with different sizes of morphological structuring elements and noise,” Int. J. Appl. Eng. Res., Vol. 10, no. 6, pp. 4991–6, 2015.
  • T. Kubota, A. K. Jerebko, M. Dewan, M. Salganicoff, and A. Krishnan, “Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models,” Med. Image Anal., 133–54, 2011. doi: 10.1016/j.media.2010.08.005
  • S. Lee, A. Kouzani, and E. Hu, “Random forest based lung nodule classification aided by clustering,” Comput. Med. Imaging Gr., Vol. 34, pp. 535–42, 2010. doi: 10.1016/j.compmedimag.2010.03.006
  • K. Okada, D. Comaniciu, and A. Krishnan, “Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT,” IEEE Trans. Med. Imaging, Vol. 24, pp. 409–23, 2005. doi: 10.1109/TMI.2004.843172
  • A. Retico, et al., “Pleural nodule identification low-dose and thin-slice lung computed tomography,” Comput. Biol. Med., Vol. 39, no. 12, pp. 1137–44, 2009. doi: 10.1016/j.compbiomed.2009.10.005
  • J. Suarez-Cuenca, P. Tahoces, M. Souto, M. Lado, M. Remy-Jardin, J. Remy, and J. Jose Vidal, “Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images,” Comput. Biol. Med., Vol. 39, pp. 921–33, 2009. doi: 10.1016/j.compbiomed.2009.07.005
  • B. Van Ginneken, M. Prokop, I. Sluimer, and A. Schilham, “Computer analysis of computed tomography scans of the lung: A survey,” IEEE Trans. Med. Imaging, 385–405, Vol. 2, 2006.
  • E. Van Rikxoort, B. de Hoop, M. Viergever, M. Prokop, and B. van Ginneken, “Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection,” Med. Phys., Vol. 36, pp. 2934, 2009. doi: 10.1118/1.3147146
  • K. M. Lee and W. N. Street. “ Dynamic leaning of shape for automatic object recognition,” in 17th ICML-2000 Workshop.
  • R. Opfer, and R. Wiemker. “Performance analysis for computer-aided lung nodule detection on LIDC data,” in Proceedings of SPIE Medical Imaging, San Diego, CA, USA, 17; Vol. 6515, pp. 65151C, February 2007.
  • R. M. Haralick, and K. Shanmugam, “Its’hak Dinstein, Texture features for image classification,” IEEE Transaction on Systems, Man and Cybernetics SMC, Vol. 3, no. 6, pp. 610–21, 1973. doi: 10.1109/TSMC.1973.4309314
  • L. B. Nascimento, A. C. Paiva, and A. C. Silva. “Lung nodules classification in CT images using Shannon and Simpson diversity indices and SVM,” in Machine learning and data mining in pattern recognition, P. Perner, Heidelberg: Springer, 2012, pp. 454–66.
  • M. H. Orozco, O. O. V. Villegas, H. J. O. Dominguez, and V. G. C. Sanchez. “Lung nodule classification in CT thorax images using support vector machines,” in: Proceedings of the 12th Mexican International Conference on Artificial Intelligence (MICAI); Mexico City, Mexico, USA: IEEE; 2013. pp. 277–83, 2013.
  • P.-W. Huang, and C.-H. Lee, “Automatic classification for pathological prostate images based on fractal analysis,” IEEE Trans. Med. Imaging, Vol. 28, no. 7, pp. 1037–50, 2009. doi: 10.1109/TMI.2009.2012704
  • Y. Zhu, Y. Tan, Y. Hua, M. Wang, G. Zhang, and J. Zhang, “Feature selection and performance evaluation of support vector machine (svm)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography,” J. Digit Imaging, Vol. 23, pp. 51–65, 2010. doi: 10.1007/s10278-009-9185-9
  • R. O. Duda, and P. E. Hart. Pattern Classification and Scene Analysis. 3rd ed. New York: Wiley, 1973.
  • S. Diciotti, S. Lombardo, G. Coppini, L. Grassi, M. Falchini, and M. Mascalchi, “The Log Characteristic Scale: A consistent measurement of lung nodule size in CT imaging,” IEEE Trans. Med. Imaging, Vol. 29, no. 2, pp. 397–409, 2010. doi: 10.1109/TMI.2009.2032542
  • Serena Ricciardi, Silverio Tomao, and Filippo de marinis. Efficacy and safety of erlotinib in the treatment of metastatic non-small-cell lung cancer. Lung Cancer: Target & Therapy, Vol. 2, pp. 1–9, 2011.
  • J. Suarez Blanco, B. E. Amendola, N. Perez, M. Amendola, and X. Wu. The use of Lattice Radiation Therapy (LRT) in the treatment of bulky tumors: a case report of a large metastatic mixed milesian ovarian tumor. Cureus, Vol. 7, no. 11, p. 389, 2015. doi:10.7759/cureus.389.
  • S. Sivakumar and C. Chandrasekar. Lung nodule detection using fuzzy clustering and support vector machines. Int. J. Eng. Res. Technol., Vol. 5, no. 1, Feb-Mar. 2013.
  • E. Dandil, M. Cakiroglu, Z. Eksi, M. Ozkan, O. K. Kurt, and A. Canan. “ 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); Tunis, Tunisia. USA, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2014.

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