ROBUST AND AUTOMATED LUNG NODULE DETECTION USING IMAGE PROCESSING TECHNIQUES

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M. Premchander
Dr. M. Venkateshwara Rao
Dr. T. V. Rajini Kanth

Abstract

In the medical field among all the types of cancers, lung cancer is more serious disease. Detection of lung cancer in the beginning will recover the lifetime of the patient. Using image processing techniques, Computed tomography scan images are very useful to find lung cancer nodule. The image pre-processing methods are feature extraction, image enhancement and image segmentation. Watershed transformation and based on Gabor filter to find lung cancer nodule. This research paper aim is to find more precise results using different segmentation and enhancement techniques.

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M. Premchander, Dr.T. V. Rajinikanth, and Dr. M . Venkateshwara Rao , “Detection of Lung Cancerin Medical ImagesUsing Image Processing Techniques “International Journal of Emerging

Technology and Advanced Engineering (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 7, Issue 7, July 2017).

M. Premchander, Dr.T. V. Rajinikanth, and Dr. M . Venkateshwara Rao ,“Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study†International Journal of

Medical Imaging, 2017; 5(5): 58-62,doi: 10.11648/j.ijmi.20170505.12, ISSN: 2330-8303 (Print); ISSN: 2330-832X (Online).

Al Tarawneh MS. Lung cancer detection using image processing techniques. Leonardo Electronic Journal of Practices and Technologies. 2012.

Gindi,A. M., Al Attiatalla, T. A., & Sami, M.M. “A Comparative Study for Comparing Two Feature

Extraction Methods and Two Classifiers in Classification of Early stage Lung Cancer Diagnosis of chest x-ray images.†Journal of American Science, 2014.

Ajil MV, Sreeram S. Lung cancer detection from CT images using various image processing techniques. International Journal of Advance Research in Computer Science and Management Studies.

May; 3(5), 249–54.

Xiuhua,G., Tao, S., &Zhigang, L, “Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image.†In Theory and Applications of CT Imaging and Analysis. 2011.

Chaudhary A, Singh SS. Lung cancer detection on CT images by using image processing. International Conference on Computing Science, Phagwara. 2012.

Goswami A. For image enhancement and segmentation by using evaluation of gabor filter parameters. International Journal of Advanced Technology and Engineering Research. 2012.

Ruchika Kalra A. Detection of lung cancer in CT images using mean shift algorithm. International Journal of Advanced Research in Computer Science and Software Engineering. 2015.

Roy, T., Sirohi, N., &Patle, A. (2015) “Classification of lung image and nodule detection using fuzzy inference system.†International Conference On Computing, Communication & Automation., 2015.

Ignatious, S., & Joseph, R. (2015) “Computer aided lung cancer detection system.†2015 Global Conference On Communication Technologies (GCCT).

Rendon-Gonzalez, E., &Ponomaryov, V. (2016) “Automatic Lung nodule segmentation and classification in CT images based on SVM.†2016 9Th International Kharkiv Symposium On Physics And Engineering Of Microwaves, Millimeter And Submillimeter Waves (MSMW).

Ng HP, Huang S, Ong SH, Foong KWC, Goh PS, Nowinski WL. Medical image segmentation using watershedsegmentation with texture merging. IEEE Engineering in medicine and biology Society Conference, Canada. 2008.

Patela SVK, Shrivastavab P. Implementation of medical image enhancement technique using gabor filter. International Journal of Current Engineering and Technology. 2012.

Tanaka K, Sakuma T. Geographical difference of chromosome aberrations between Japanese and American small cell lung cancer cell lines. Indian Journal of Science and Technology. 2012.

SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset. The Cancer Imaging Archive; 2015.

Hollings N., Shaw P. Diagnostic imaging of lung cancer. European Respiratory Journal. 2002.

Li W., Nie S. D., Cheng J.Berlin, A fast automatic method of lung segmentation in CT images using mathematical morphology; pp. 2419–2422. (IFMBE Proceedings) Springer; 2007.

Hu S., Hoffman E. A., Reinhardt J. M. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Transactions on Medical imaging, 2001.

Shah S. Automatic cell images segmentation using a shape-classification model. IEICE Transactions on Information and Systems. 2007.

Prasad M. N., Brown M. S., Ahmad S., et al. Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs. Academic Radiology. 2008.

Wang J., Li F., Li Q. Automated segmentation of lungs with severe interstitial lung disease in CT. Medical Physics. 2009.

Armato S., MacMahon H. Automated lung segmentation and computer-aided diagnosis for thoracic CT scans. Internationa l Congress Series. 2003.

Sudha V, Jayashree P. Lung Nodule Detection in CT Images Using Thresholding and Morphological Operations. International Journal of Emerging Science and Engineering. 2012.

Guo Y., Feng Y., Sun J., et al. Automatic lung tumor segmentation on PET/CT images using fuzzy markov random field model. Computational and Mathematical Methods in Medicine. 2014.

Lam M., Disney T., Pham M., Raicu D., Furst J., Susomboon R. Content-based image retrieval for pulmonary computed tomography nodule images. Medical Imaging 2007: PACS and Imaging Informatics; March 2007.

Dhara A. K., Chama C. K., Mukhopadhyay S., Khandelwal N. Content-based image retrieval system for differential diagnosis of lung cancer. Indian Journal of Medical Informatics. 2012.

Nirmala J. B., Gowri S. A content based CT lung image retrieval by DCT matrix and feature vector technique. International Journal of Computer Science Issues. 2012.

Huang Q, Gao W, Cai W. Thresholding technique with adaptive window selection for uneven lighting image. Pattern Recognition Letters. 2005 May.

Liu C.-T., Tai P.-L., Chen A. Y.-J., Peng C.-H., Lee T., Wang J.-S. A content-based CT lung image retrieval system for assisting differential diagnosis images collection. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME 2001), 2001.

Nguyen worring HT, Van de Boomgaard R. Watersnakes: Energy-driven watershed segmentation. Pattern Analysis and Machine Intelligence. 2003.

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