This paper focuses on early stage lung cancer detection. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed.
| Published in | International Journal of Medical Imaging (Volume 5, Issue 5) |
| DOI | 10.11648/j.ijmi.20170505.12 |
| Page(s) | 58-62 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Classification, Lung Cancer Detection, Accuracy, Image Processing Techniques
| [1] | Aparna Kanakatte, Nallasamy Mani, Bala Srinivasan, Jayavardhana Gubbi, “Pulmonary Tumor Volume Detection from Positron Emission Tomography Images”, International Conference on Biomedical Engineering and Informatics, pp: 213-217, 2008. |
| [2] | Yongbum Lee, Takeshi Hara, Hiroshi, shigeki and Takeo: IEEE Transactions on Medical Iamaging vol 20 No 7, July 2001. |
| [3] | Samuel H Hawkins, Yoganand Balagurunathan, Virendra kumar, Lawrence, and Robert IEEE Access, vol 2, 2014. |
| [4] | Xing CHEN, Mingfu CAO, Yan Hao Proceedings of the 2005 IEEE Engineering in medical and Biology 27th Annual Conf. China Sep 1-4, 2005. |
| [5] | Noha Lee, Andrew F. L Aine, Guillermo, Jeffrey and John K. Gohagan IEEE Review in Bio Medical Engineering, vol 2, 2009. |
| [6] | David S paik, Christipher, Geoffrey, rubeen, Rubak Acar, Joyoni Dey and Sandy Nepel: IEEE Transactions on Medical Iamaging vol 23 No 26, June 2004. |
| [7] | Jyh-Shyan, Shih-Chung, Akira, Mattew and Seong: IEEE Transactions on Medical Iamaging vol 15, No 2, April 1996. |
| [8] | M. Freedman, “Improved small volume lung cancer detection with computer-aided detection: Database characteristics and imaging of response to breast cancer risk reduction strategies,” Ann. NY Acad. Sci., vol. 1020, pp. 175–89, 2004. |
| [9] | R. MacRedmond et al., “Screening for lung cancer using low dose CT scanning,” Thorax, vol. 59, pp. 237–41, 2004. |
| [10] | Z. Liu and M. Tan, “ROC-based utility function maximization for feature selection and classification with applications to high-dimensional protease data, biometrics,” J. Int. Biometr. Soc., vol. 64, no. 4, pp. 1155–1161, 2008. |
| [11] | Basavanna et. al, “Tumor Cell Identification in Medical Images using Image Processing Techniques” October, 2016. |
| [12] | Fatma Taher, Naoufel Werghi and Hussain Al-Ahmed, “Bayesian Classification Artificial Neural Network Methods for Lung Cancer Early Diagnosis”, IEEE, pp: 773-776, 2012. |
| [13] | Prasshanth Naresh, Rajashree Shettar, “Image Processing and classification Techniques for early Detection of Lung Cancer for Preventive Healthcare: October, 2016. |
| [14] | Ruchika, Ashima “Detection of Lung cancer in CT Images using Mean Shift Algorithm”, volume 5, issue 5, May 2015. |
| [15] | Bhagyasri G. Patil, Prof Sanjeev “Cancer cells Detection using Digital Image Processing Methods”, Vol 3 Issue 4 March 2014. |
| [16] | S. Sivakumar, Dr. C. Chandrasekar, “Lung Nodule Detection Using Fuzzy Clustering and Support Vector Machines”, International Journal of Engineering and Technology (IJET), Vol 5 No 1, pp: 179-185, Feb-Mar 2013. |
| [17] | Anam Tariq, M. Usman Akram and M. Younus Javed, “Lung Nodule Detection in CT Images using Neuro Fuzzy Classifier”, Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI), pp: 49-53, 2013. |
| [18] | Dansheng Song, Tatyana A. Zhukov, Olga Markov, Wei Qian, Melvyn S. Tockman, “Prognosis of stage i lung cancer patients through quantitative analysis of centrosomal features”, ie, pp: 1607-1610, 3012. |
| [19] | S. K. Vijai Anand, “Segmentation coupled Textural Feature Classification for Lung Tumor Prediction”, ICCCCT, pp: 518-524, 2010. |
| [20] | Atiyeh Hashemi, Abdol Hamid Pilevar, Reza Rafeh, “Mass Detection in Lung CT Images Using Region Growing Segmentation and Decision Making Based on Fuzzy Inference System and Artificial Neural Network”, I. J. Image, Graphics and Signal Processing, 6, pp: 16-24, 2013. |
| [21] | Kesav Kancherla, Srinivas Mukkamala, “Early Lung Cancer Detection using Nucleus Segmentation based Features”, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp: 91-95, 2013. |
| [22] | M. Aoyama, Q. Li, S. Katsuragawa, H. MacMahon, and K. Doi, “Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images,” Med. Phys., vol. 29, pp. 701–8, 2002. |
| [23] | J. A. Swets, “ROC analysis applied to the evaluation of medical imaging techniques,” Invest. Radiol., vol. 14, pp. 109–21, 1979. |
| [24] | R. Sah et al., “Results of surgical treatment of stage I and II lung cancer,” J. Cardiovasc. Surg., vol. 37, pp. 169–172, 1996. |
| [25] | S. Sone et al., “Mass screening for lung cancer with mobile spiral computed tomography scanner,” Lancet, vol. 351, pp. 1242–5, 1998. |
| [26] | C. I. Henschke et al., “Early lung cancer action project: Overall design and findings from baseline screening,” Lancet, vol. 354, pp. 99–105, 1999. |
| [27] | D. S. Gierada, T. K. Pilgrim, M. Ford, R. M. Fagerstrom, T. R. Church, H. Nath, K. Garg, and D. C. Strollo, “Lung cancer: inter observer agreement on interpretation of pulmonary findings at low-dose CT screening,” Radiol., vol. 246, no. 1, pp. 265–272, 2008. |
| [28] | B. S. Kramer and O. W. Brawley, “Cancer screening,” Hematol. Oncol. Clin. North Amer., vol. 14, pp. 831–48, 2000. |
| [29] | L. Berlin, “Liability of performing CT screening for coronary artery disease and lung cancer,” Amer. J. Roentgenol., vol. 179, pp. 837–42, 2002. |
| [30] | S. J. Swensen et al., “Screening for lung cancer with low-dose spiral computed tomography,” Amer. J. Respir. Crit. Care Med., vol. 165, pp. 508–13, 2002. |
| [31] | J. C. Nesbitt et al., “Survival in early-stage lung cancer,” Ann. Thorac. Surg., vol. 60, pp. 466–472, 1995. |
| [32] | C. I. Henschke et al., “CT screening for lung cancer: Suspiciousness of nodules according to size on baseline scans,” Radiology, vol. 231, pp. 164–8, 2004. |
| [33] | L. L. Humphrey, S. Teutsch, and M. Johnson, “Lung cancer screening with sputum cytologic examination, chest radiography, and computed tomography: An update for the U.S. Preventive Services Task Force,” Ann. Intern. Med., vol. 140, pp. 740–53, 2004. |
| [34] | J. Gohagan et al., “Baseline findings of a randomized feasibility trial of lung cancer screening with spiral CT scan vs chest radiograph: The lung screening study of the National Cancer Institute,” Chest, vol. 126, pp. 114–21, 2004. |
| [35] | B. J. Flehinger, M. Kimmel, and M. R. Melamed, “Survival from early lung cancer: Implications for screening,” Chest, vol. 101, pp. 1013–1018, 1992. |
APA Style
Munimanda Prem Chander, M. Venkateshwara Rao, T. V. Rajinikanth. (2017). Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study. International Journal of Medical Imaging, 5(5), 58-62. https://doi.org/10.11648/j.ijmi.20170505.12
ACS Style
Munimanda Prem Chander; M. Venkateshwara Rao; T. V. Rajinikanth. Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study. Int. J. Med. Imaging 2017, 5(5), 58-62. doi: 10.11648/j.ijmi.20170505.12
AMA Style
Munimanda Prem Chander, M. Venkateshwara Rao, T. V. Rajinikanth. Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study. Int J Med Imaging. 2017;5(5):58-62. doi: 10.11648/j.ijmi.20170505.12
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Download@article{10.11648/j.ijmi.20170505.12,
author = {Munimanda Prem Chander and M. Venkateshwara Rao and T. V. Rajinikanth},
title = {Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study},
journal = {International Journal of Medical Imaging},
volume = {5},
number = {5},
pages = {58-62},
doi = {10.11648/j.ijmi.20170505.12},
url = {https://doi.org/10.11648/j.ijmi.20170505.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20170505.12},
abstract = {This paper focuses on early stage lung cancer detection. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed.},
year = {2017}
}
TY - JOUR T1 - Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study AU - Munimanda Prem Chander AU - M. Venkateshwara Rao AU - T. V. Rajinikanth Y1 - 2017/12/09 PY - 2017 N1 - https://doi.org/10.11648/j.ijmi.20170505.12 DO - 10.11648/j.ijmi.20170505.12 T2 - International Journal of Medical Imaging JF - International Journal of Medical Imaging JO - International Journal of Medical Imaging SP - 58 EP - 62 PB - Science Publishing Group SN - 2330-832X UR - https://doi.org/10.11648/j.ijmi.20170505.12 AB - This paper focuses on early stage lung cancer detection. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed. VL - 5 IS - 5 ER -
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