Study of Medical Image Processing techniques applied to Lung Cancer
Lung cancer is the leading cause of death from cancer worldwide. Medical images are essential in the diagnosis and prognosis of lung cancer. Medical image processing techniques such as Radiomics allow extracting information from these images that it is not accessible without computational means, and...
- Autores:
-
Moreno, Silvia
Bonfante, Mario
Zurek, Eduardo
San Juan, Homero
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/3702
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/3702
- Palabra clave:
- Lung Cancer
Medical Image Processing
Radiomics
- Rights
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.title.eng.fl_str_mv |
Study of Medical Image Processing techniques applied to Lung Cancer |
title |
Study of Medical Image Processing techniques applied to Lung Cancer |
spellingShingle |
Study of Medical Image Processing techniques applied to Lung Cancer Lung Cancer Medical Image Processing Radiomics |
title_short |
Study of Medical Image Processing techniques applied to Lung Cancer |
title_full |
Study of Medical Image Processing techniques applied to Lung Cancer |
title_fullStr |
Study of Medical Image Processing techniques applied to Lung Cancer |
title_full_unstemmed |
Study of Medical Image Processing techniques applied to Lung Cancer |
title_sort |
Study of Medical Image Processing techniques applied to Lung Cancer |
dc.creator.fl_str_mv |
Moreno, Silvia Bonfante, Mario Zurek, Eduardo San Juan, Homero |
dc.contributor.author.none.fl_str_mv |
Moreno, Silvia Bonfante, Mario Zurek, Eduardo San Juan, Homero |
dc.subject.eng.fl_str_mv |
Lung Cancer Medical Image Processing Radiomics |
topic |
Lung Cancer Medical Image Processing Radiomics |
description |
Lung cancer is the leading cause of death from cancer worldwide. Medical images are essential in the diagnosis and prognosis of lung cancer. Medical image processing techniques such as Radiomics allow extracting information from these images that it is not accessible without computational means, and may be useful in the detection and treatment of cancer. This article presents the state of the art of image processing techniques applied in the study of lung cancer, emphasizing in two main tasks: segmentation of nodules or tumors, and extraction of useful features for classification and prognosis of tumor evolution using Radiomics. |
publishDate |
2019 |
dc.date.accessioned.none.fl_str_mv |
2019-08-13T13:59:14Z |
dc.date.available.none.fl_str_mv |
2019-08-13T13:59:14Z |
dc.date.issued.none.fl_str_mv |
2019-07 |
dc.type.eng.fl_str_mv |
article |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.identifier.isbn.none.fl_str_mv |
9789899843493 |
dc.identifier.issn.none.fl_str_mv |
21660727 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12442/3702 |
identifier_str_mv |
9789899843493 21660727 |
url |
https://hdl.handle.net/20.500.12442/3702 |
dc.language.iso.eng.fl_str_mv |
eng |
language |
eng |
dc.rights.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
dc.publisher.eng.fl_str_mv |
IEEE |
dc.source.eng.fl_str_mv |
IEEE Xplore Digital Library 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) |
institution |
Universidad Simón Bolívar |
dc.source.uri.eng.fl_str_mv |
https://ieeexplore.ieee.org/document/8760888 |
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Moreno, Silviafdbaaeb0-3b4f-4fb1-aa77-ebcfe2658250Bonfante, Mario5b91ad34-74dc-42ed-be44-06beeaf401dcZurek, Eduardof76fa751-7944-4c11-819b-3dae164a6215San Juan, Homero24d53aba-87c5-47ac-9a37-6a67c6f3177b2019-08-13T13:59:14Z2019-08-13T13:59:14Z2019-07978989984349321660727https://hdl.handle.net/20.500.12442/3702Lung cancer is the leading cause of death from cancer worldwide. Medical images are essential in the diagnosis and prognosis of lung cancer. Medical image processing techniques such as Radiomics allow extracting information from these images that it is not accessible without computational means, and may be useful in the detection and treatment of cancer. This article presents the state of the art of image processing techniques applied in the study of lung cancer, emphasizing in two main tasks: segmentation of nodules or tumors, and extraction of useful features for classification and prognosis of tumor evolution using Radiomics.engIEEEAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2IEEE Xplore Digital Library2019 14th Iberian Conference on Information Systems and Technologies (CISTI)https://ieeexplore.ieee.org/document/8760888Lung CancerMedical Image ProcessingRadiomicsStudy of Medical Image Processing techniques applied to Lung Cancerarticlehttp://purl.org/coar/resource_type/c_6501Cancer Research UK, “Worldwide cancer statistics.” [Online]. Available: http://www.cancerresearchuk.org/health-professional/cancerstatistics/ worldwide-cancer. [Accessed: 17-Nov-2017].P. Lambin, E. Rios-Velazquez, R. Leijenaar, S. Carvalho, R. G. P. M. Van Stiphout, P. Granton, C. M. L. Zegers, R. Gillies, R. Boellard, A. Dekker, and H. J. W. L. Aerts, “Radiomics: Extracting more information from medical images using advanced feature analysis,” Eur. J. Cancer, vol. 48, no. 4, pp. 441–446, 2012.G. Niranjana, “A Review on Image Processing Methods in Detecting Lung Cancer Using CT Images - IEEE Conference Publication,” 2017.G. Lee, H. Y. Lee, H. Park, M. L. Schiebler, E. J. R. van Beek, Y. Ohno, J. B. Seo, and A. Leung, “Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art,” Eur. J. Radiol., vol. 86, pp. 297–307, 2017.A. Kulkarni and A. Panditrao, “Classification of lung cancer stages on CT scan images using image processing,” 2014 IEEE Int. Conf. Adv. Commun. Control Comput. Technol., no. 978, pp. 1384–1388, 2014.Y. Gu, V. Kumar, L. O. Hall, D. B. Goldgof, C. Y. Li, R. Korn, C. Bendtsen, E. R. Velazquez, A. Dekker, H. Aerts, P. Lambin, X. Li, J. Tian, R. A. Gatenby, and R. J. Gillies, “Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach,” Pattern Recognit., vol. 46, no. 3, pp. 692–702, 2013.B. C. Lassen, C. Jacobs, J. M. Kuhnigk, B. Van Ginneken, and E. M. Van Rikxoort, “Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans,” Phys. Med. Biol., vol. 60, no. 3, pp. 1307–1323, 2015.Y. Zheng, K. Steiner, T. Bauer, J. Yu, D. Shen, and C. Kambhamettu, “Lung nodule growth analysis from 3D CT data with a coupled segmentation and registration framework,” Proc. IEEE Int. Conf. Comput. Vis., pp. 0–7, 2007.Y. Gao, Z. Shen, Y. Zhang, and W. Chen, “Tumor Segmentation for Lung 4D-CT Data Using Graph Cuts with Inter-Phase Shape Prior,” vol. 6, no. 3, pp. 634–639, 2016.W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, “Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images,” IEEE Trans. Image Process., vol. 24, no. 12, pp. 5854–5867, 2015.S. Mukherjee, X. Huang, and R. R. Bhagalia, “Lung nodule segmentation using deep learned prior based graph cut,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, pp. 1205–1208.W. Sun, X. Huang, T.-L. B. Tseng, and W. Qian, “Automatic lung nodule graph cuts segmentation with deep learning false positive reduction,” SPIE Med. Imaging, vol. 10134, p. 101343M, 2017.M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” International Journal of Computer Vision, vol. 1, no. 4. pp. 321–331, 1988.M. Keshani, Z. Azimifar, F. Tajeripour, and R. Boostani, “Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system,” Comput. Biol. Med., vol. 43, no. 4, pp. 287–300, 2013.I. C. Plajer and D. Richter, “A new approach to model based active contours in lung tumor segmentation in 3D CT image data,” Proc. 10th IEEE Int. Conf. Inf. Technol. Appl. Biomed., pp. 1–4, 2010.Z. Nadealian, B. Nazari, S. Sadri, and M. Momeni, “Detection of pulmonary nodules in low-dose computed tomography using localized active contours and shape features,” J. Med. Signals Sens., vol. 7, no. 4, pp. 203–212, 2017.S. J. Osher, “Fronts Propagating with Curvature Dependent Speed,” Comput. Phys., vol. 79, no. 1, pp. 1–5, 1988.K. Krishnan, L. Ibanez, W. D. Turner, J. Jomier, and R. S. Avila, “An open-source toolkit for the volumetric measurement of CT lung lesions.,” Opt. Express, vol. 18, no. 14, pp. 15256–15266, 2010.H. Zhu, C. H. Pak, C. Song, S. Dou, H. Zhao, P. Cao, and X. Ye, “A novel lung cancer detection algorithm for CADs based on SSP and Level Set,” Technol. Heal. Care, vol. 25, no. S1, pp. S345–S355, 2017.W. Zhang, X. Zhang, J. Zhao, Y. Qiang, Q. Tian, and X. Tang, “A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise,” pp. 1–25, 2017.S. Hamidian, B. Sahiner, N. Petrick, and A. Pezeshk, “3D convolutional neural network for automatic detection of lung nodules in chest CT,” vol. 10134, p. 1013409, 2017.S. Wang, M. Zhou, Z. Liu, Z. Liu, D. Gu, Y. Zang, D. Dong, O. Gevaert, and J. Tian, “Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation,” Med. Image Anal., vol. 40, pp. 172–183, 2017.H. Jiang, H. Ma, W. Qian, M. Gao, and Y. Li, “An Automatic Detection System of Lung Nodule Based on Multi-Group Patch-Based Deep Learning Network,” IEEE J. Biomed. Heal. Informatics, vol. 2194, no. c, 2017.M. Scrivener, E. E. C. de Jong, J. E. van Timmeren, T. Pieters, B. Ghaye, and X. Geets, “Radiomics applied to lung cancer: a review,” Transl. Cancer Res., vol. 5, no. 4, pp. 398–409, 2016.F. C. Detterbeck and C. J. Gibson, “Turning Gray: The Natural History of Lung Cancer Over Time,” J. Thorac. Oncol., vol. 3, no. 7, pp. 781– 792, 2008.B. de Hoop, H. Gietema, S. van de Vorst, K. Murphy, R. J. van Klaveren, and M. Prokop, “Pulmonary Ground-Glass Nodules: Increase in Mass as an Early Indicator of Growth,” Radiology, vol. 255, no. 1, pp. 199–206, Mar. 2010.A. Kamiya, S. Murayama, H. Kamiya, T. Yamashiro, Y. Oshiro, and N. Tanaka, “Kurtosis and skewness assessments of solid lung nodule density histograms: Differentiating malignant from benign nodules on CT,” Jpn. J. Radiol., vol. 32, no. 1, pp. 14–21, 2014.Y. Chong, J. H. Kim, H. Y. Lee, Y. C. Ahn, K. S. Lee, M. J. Ahn, J. Kim, Y. M. Shim, J. Han, and Y. La Choi, “Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR-TKIs: Are they different from those in neoadjuvant concurrent chemoradiotherapy?,” PLoS One, vol. 9, no. 2, pp. 1–8, 2014.T. Pyka, R. A. Bundschuh, N. Andratschke, B. Mayer, H. M. Specht, L. Papp, N. Zsótér, and M. Essler, “Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy,” Radiat. Oncol., vol. 10, no. 1, p. 100, 2015.T. Win, K. A. Miles, S. M. Janes, B. Ganeshan, M. Shastry, R. Endozo, M. Meagher, R. I. Shortman, S. Wan, I. Kayani, P. J. Ell, and A. M. Groves, “Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer,” Clin. Cancer Res., vol. 19, no. 13, pp. 3591–3599, 2013.M. Saad and T.-S. Choi, “Deciphering unclassified tumors of non-smallcell lung cancer through radiomics,” Comput. Biol. Med., vol. 91, pp. 222–230, 2017.J. Ma, Q. Wang, Y. Ren, H. Hu, and J. Zhao, “Automatic lung nodule classification with radiomics approach,” vol. 9789, p. 978906, 2016.Y. Huang, Z. Liu, L. He, X. Chen, D. Pan, Z. Ma, C. Liang, J. Tian, and C. Liang, “Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer,” Radiology, vol. 281, no. 3, pp. 947–957, Jun. 2016.W. Wu, C. Parmar, P. Grossmann, J. Quackenbush, P. Lambin, J. Bussink, R. Mak, and H. J. W. L. Aerts, “Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology,” Front. Oncol., vol. 6, no. March, pp. 1–11, 2016.N. Emaminejad, S. Yan, Y. Wang, W. Qian, Y. Guan, and B. Zheng, “Applying a radiomics approach to predict prognosis of lung cancer patients,” Prog. Biomed. Opt. Imaging - Proc. 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