Lung cancer is a type of lung disease characterized by uncontrolled cell growth in lung tissue. Whereas nodules (benign cancer) are small, round or egg-shaped lesions in the lungs. The current method used to diagnose lung cancer from CT scan images is by observing a data set of 2-D CT Scan images using naked eye, then interpreting data one by one. This procedure is certainly not effective. Research conducted aims to extract texture features to improve accuracy of malignant and benign cancers detection in CT scans. This research covers 5 (five) main points. First is pre-processing CT-Scan images. The second is automatic segmentation of lung area using Active Appearance Model (AAM) method. Third is segmentation of candidates who are considered cancer using morphological mathematics. Fourth, process of detecting benign and malignant lung cancer using Support Vector Machine (SVM). Fifth is visualization of malignant and benign lung cancer using Volume Rendering. Accuracy of malignant and benign cancers detection is 79.7%
U.S. Cancer Statistics Working Group, United States Cancer Statistics: 1999–2012 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute, 2015.
Messay T, Hardie R, Rogers S, A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3): 390–406, 2010.
Kustanto, Sri Widodo, Sri Tomo, (2015 ), Software Development To Detect Lung Nodules In Computed Tomography Scan Image Using Support Vector Machine, International Research Journal of Engineering and Technology (IRJET), Volume 02 Issue 06 Sep-2015. Pp. 354-360.
Kustanto, Sri Widodo, Sri Tomo, (2015 ), Lung Nodule Segmentation Candidates Using Active Shape Model and Morphological Mathematics, Seminar Nasional Sistem Informasi Indonesia (SESINDO 2015), 02-04 Nopember 2015.
Bhagyashri G. Patil ,2014, Cancer Cells Detection Using Digital Image Processing Methods, International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 3 Issue 4 March 2014, pp. 45-49.
Mr.Vijay A.Gajdhane, Prof. Deshpande L.M., 2014, Detection of Lung Cancer Stages on CT scan Images by Using Various Image Processing Techniques, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 16, Issue 5, Ver. III (Sep – Oct. 2014), PP 28-35
Disha Sharma, Gagandeep Jindal, 2011, Identifying Lung Cancer Using Image Processing Techniques, International Conference on Computational Techniques and Artificial Intelligence (ICCTAI'2011).
Mokhled S. AL-TARAWNEH, 2012, Lung Cancer Detection Using Image Processing Techniques, Leonardo Electronic Journal of Practices and Technologies, Issue 20, January-June 2012 p. 147-158
T.F. Cootes and C.J Taylor. Statistical models of appearance for computer vision, 2004.
N. Babaii Rizvandi, A.Pizurica, W.Philips, “Deformable Shape Description Using Active Shape Model”, Department of Telecommunications and Information Processing(TELIN), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium, 2007.
M. Syamsa Ardisasmita, Segmentation and Reconstruction of Organ Images in Three Dimensions Using Morphological Mathematics and Triangulation, Pusat Pengembangan Teknologi Informasi Dan Komputasi BATAN.
Yu-qian, Zhao, etc, “Medical Images Edge Detection Based on Mathematical Morphology”, Proceedings : IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
Alexandros Karargyris, Jenifer Siegelman, Dimitris Tzortzis, 2015, Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays, Int J CARS, DOI 10.1007/s11548-015-1242-x, 20 Juni 2015.
Nugroho, A.S., Witarto, B.A., Handoko, D., (2003), Support Vector Machine - Theory and Applications in Bioinformatics, KuliahUmumIlmu Komputer.com
Cristianini N, Shawe-Taylor J (2000), An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge.
Burges CJC, Tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2): 121– 167, 1998.
Widodo Sri, Ratnasari Nur Rohmah, Bana Handaga, Liss Dyah Dewi Arini, Lung Diseases Detection Caused By Smoking Using Support Vector Machine, TELKOMNIKA, Vol.17, No.3, June 2019, pp. 1256~1266.
Drebin, R., L. Carpenter, (1988). "Volume Rendering." SIGGRAPH '88: 665-674.
Hohne, K. H., M. Bomans, et al. (1990). "Rendering Tomographic Volume Data: Adequacy of Methods for Different Modalities and Organs." 3-D Imaging in Medicine F60: 197-215.