The week's pick
Reseach Article
Computer Aided Lung Cancer Detection and Tumor Staging in CT image using Image Processing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 128 - Number 7 |
| Year of Publication: 2015 |
| Authors: Sruthi Ignatious, Robin Joseph, Jisha John, Anil Prahladan |
10.5120/ijca2015906607
|
Sruthi Ignatious, Robin Joseph, Jisha John, Anil Prahladan . Computer Aided Lung Cancer Detection and Tumor Staging in CT image using Image Processing. International Journal of Computer Applications. 128, 7 ( October 2015), 29-33. DOI=10.5120/ijca2015906607
Abstract
Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer. Nowadays the lung cancer is staged according to the TNM staging method where T means Tumor, N means Nodule and M means Metastates. The existing lung cancer detection algorithms cannot stage cancer according to the TNM staging method. The proposed system can identify the T stage of the cancer accurately. The proposed system includes different stages such as pre-processing, segmentation, feature extraction, tumor detection and tumor stage identification. The proposed system promises better result than the existing systems, which would be beneficial for the radiologist for the accurate and early detection of cancer. The method has been tested on 200 slices of CT images of various stages of cancer obtained from Regional Cancer Centre Trivandrum and is found to give good results. The accuracy of the proposed method in this dataset is 94.4%
References
- Anam Tariq, M. Usman Akra and M. Younus JavedS, ‟Lung Nodule Detection in CT image using Neuro Fuzzy classifier”, IEEE Transactions (2013).
- R. Prasad, “Cancer killed 5.56 lakh in India in 2010”,The Hindu News paper, March 30, 2012.
- http://www.cancer.gov/about-cancer/diagnosis-staging/staging/staging-fact-sheet
- Sruthi Ignatious, Robin Joseph, “Computer Aided Lung cancer Detection System”, IEEE, Global Conference on communication and Technologies, (2015)
- Anitha Chaudary, Sonit.S.Singh, ‟Lung cancer Detection on CT images by using image processing”, IEEE, International Conference on Computer Sciences, (2012).
- Henri Kivinen,‟Automatic Image Enhancement Methods for reader reporters image”,WP3 hyper local content d3.2.2.1.
- Rafael C., Gonzalez & Woods R.E., “Digital Image Processing”, Pearson Education.
- Imzad Rizvi, B.K. Mohan, “Wavelet based Marker-Controlled Watershed Segmentation Technique for High Resolution Satellite Images”, 2nd International Conference and workshop on Emerging Trends in Technology (ICWET) 2011
- S.Shaik Parveen, Dr.C.Kavitha, ‟ Review on Computer Aided Detection and Diagnosis of lung cancer nodules”, International Journal of Computers & Technology, Volume 3 No. 3, Nov-Dec (2012).
- D.S. Elizabeth, H.K. Nehemiah, C.S. Retmin Raj, A. Kanna, “Computer Aided diagonosis of lung cancer based on analysis of significant slice of chest computed tomography image”, IET Image processing. Published on (2010) and revised on (2011)
- http://dali.feld.cvut.cz/ucebna/matlab/toolbox/images/fspecial.html
- http://www.cs.uu.nl/docs/vakken/ibv/reader/chapter10.pdf
- http://in.mathworks.com/help/images/examples/marker-controlled-watershed-segmentation.html
- http://www.support-vector-machines.org/
- R. Kohavi, “Scaling up the accuracy of Naïve-Bayes Classifiers: a Decision-Tree Hybrid”, Proceedings of the second International Conference on knowledge Discovery and Data Minning,1996
- http://docs.opencv.org/modules/ml/doc/randomtmlrees.html
- Fatma Taher, Rachid Sammouda,‟Identification of Lung cancer based on Shapeand color",IEEE transactions on medical images (2008).
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