LUNG CANCER DETECTION USING MARKER- CONTROLLED WATERSHED WITH SVM
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Abstract
Lung cancer is one of the most dangerous diseases and prediction of it, is the most challenging problem nowadays. Most of the cancer cells are overlapped with each other. It is hard to detect the cells but also essential to identify the presence of cancer cells in the early stage. Early detection of lung cancer may reduce the death rate. In this study, we used the Grey Level Co-occurrence Matrix (GLCM) to extract the feature of cancer affected lung image and then Support Vector Machine (SVM) has been used to detect normal and abnormal lung cells after implementing the features. Our experimental evaluation using MATLAB demonstrates the efficient performance of the proposed system and in the result.
##plugins.themes.bootstrap3.article.details##
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
A. Staff, “Cancer Facts & Figures 2018,” Atlanta: American Cancer Society, Cancer, pp. 19-20, 2018.
L. Sali, S. Delsanto, D. Sacchetto, L. Correale, M. Falchini, A. Ferraris, G. Gandini, G. Grazzini, F. Iafrate, G. Iussich, L. Morra, A. Laghi, M. Mascalchi and D. Regge, “Computerbased self-training for CT colonography with and without CAD”, European Radiology, 2018.
Q. Song, L. Zhao, X. Luo and X. Dou, “Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images”, Journal of Healthcare Engineering, vol. 2017, pp. 1-7, 2017.W.-K.Chen, Linear Networks and Systems. Belmont, CA: Wadsworth, 1993, pp. 123–135.
S. Avinash, K. Manjunath and S. Kumar, “An improved image processing analysis for the detection of lung cancer using Gabor filters and watershed segmentation technique”, 2016 International Conference on Inventive Computation Technologies (ICICT), 2016. Available: 10.1109/inventive.2016.7830084 [Accessed 24 January 2019].
[5] T. Jones, D. Duquette, M. Underhill, C. Ming, K. Mendelsohn-Victor, B. Anderson, K. Milliron, G. Copeland, N. Janz, L. Northouse, S. Duffy, S. Merajver and M. Katapodi, “Surveillance for cancer recurrence in long-term young breast cancer survivors randomly selected from a statewide cancer registry”, Breast Cancer Research and Treatment, vol. 169, no. 1, pp. 141-152, 2018.
P. Reid and S. Walter, “Yield from a fast track referal system for radiologists suspecting lung cancer”, Lung Cancer, vol. 115, p. S16, 2018.
F. Tuj Johora, M. Jony and P. Khatun, “A New Strategy to Detect Lung Cancer on CT Images”, International Research
Journal of Engineering and Technology (IRJET), vol. 05, no. 12, pp. 27-32, 2018.
I. Valente, P. Cortez, E. Neto, J. Soares, V. de Albuquerque and J. Tavares, “Automatic 3D pulmonary nodule detection in CT images: A survey”, Computer Methods and Programs in Biomedicine, vol. 124, pp. 91-107, 2016.
S. Masrochah, “Relationship between Slice Thickness to
Artery Coronary Diagnostic Information on the Reconstruction of Maximum Intensity Protection (MIP)”, Journal of Medical Science and clinical Research, vol. 05, no. 06, pp. 23140-23145, 2017.
F. Prior et al., "The public cancer radiology imaging collections of The Cancer Imaging Archive", Scientific Data, vol. 4, p. 170124, 2017. Available: 10.1038/sdata.2017.124.
H. Chien and D. Mackay, “How much complexity is needed to simulate watershed streamflow and water quality? A test combining time series and hydrological models”, Hydrological Processes, vol. 28, no. 22, pp. 5624-5636, 2013.
R. Lung CT, "The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki", Wiki.cancerimagingarchive.net, 2019. [Online]. Available: https://wiki.cancerimagingarchive.net/display/Public/RIDER+ Lung+CT#4097ffced7f64f32a18885ceffdf7b86. [Accessed: 23- Mar- 2019].
S. Makaju, P. Prasad, A. Alsadoon, A. Singh and A. Elchouemi, "Lung Cancer Detection using CT Scan Images", Procedia Computer Science, vol. 125, pp. 107-114, 2018. Available: 10.1016/j.procs.2017.12.016.
S. Avinash, K. Manjunath and S. Kumar, “An improved image processing analysis for the detection of lung cancer using Gabor filters and watershed segmentation technique”,
International Conference on Inventive Computation Technologies (ICICT), 2016.
M. Firmino, G. Angelo, H. Morais, M. Dantas and R. Valentim, “Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy”, BioMedical Engineering OnLine, vol. 15, no. 1, 2016.
H. Rutika r, “Automated detection and diagnosis from lungs CT scan images”, International Journal of Emerging Technologies and Innovative Research (www.jetir.org — UGC and issn Approved), ISSN:2349-5162, vol. 2, no. 3, pp. pp785-787, 2015.
H. Ren, Y. Zhou and M. Zhu, “Tree Image Segmentation Based on an Improved Two-Dimensional Otsu Algorithm”, International Journal of Hybrid Information Technology, vol. 9, no. 9, pp. 199-210, 2016.
Y. Zhang, H. Guo, F. Chen and H. Yang, “Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation”, Neurocomputing, vol. 249, pp. 1-18, 2017. [19] Rana HK., Azam MS. and Akhtar MR. “Iris Recognition System Using PCA Based on DWT”, SM Journal of Bio-metrics & Bio-statistics, vol. 2, no. 3, 2017.
Rana HK, Azam MS, Akhtar MR, Quinn JMW, Moni MA., ”A fast iris recognition system through optimum feature extraction”, PeerJ Preprints 7:e27363v2 https://doi.org/10.7287/peerj.preprints.27363v2, 2019.
Y. Yuan, J. Wang, B. Li and M. Meng, “Saliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis”, IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 20462057, 2015.
X. Jia and M. Meng, “A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images”, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016.
S. Suman, F. hussin, A. Malik, K. pogorelov, M. Riegler, S. Ho, I. Hilmi and K. Goh, “Detection and Classification of Bleeding Region in WCE Images using Color Feature”, Proceedings of the 15th International Workshop on ContentBased Multimedia Indexing - CBMI ’17, 2017.
Suzuki K., “False-positive Reduction in Computer-aided Diagnostic Scheme for Detecting Nodules in Chest Radiographs”, Academic Radiology, Volume 13, Number 10, pp.10-15, February 2005.
G. Aviram and M. Revel, “Misclassification of Lymph Nodes in Lung Cancer Staging”, Chest, vol. 151, no. 4, pp. 733-734, 2017.
