Home   >   CSC-OpenAccess Library   >    Manuscript Information
Statistical Feature-based Neural Network Approach for the Detection of Lung Cancer
K.A.G. Udeshani, R.G.N. Meegama, T.G.I. Fernando
Pages - 425 - 434     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Lung Nodule, Computer Assisted Diagnostic, Chest Radiography
Lung cancer, if successfully detected at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. This paper presents a novel approach to detect lung cancer from raw chest X-ray images. At the first stage, we use a pipeline of image processing routines to remove noise and segment the lung from other anatomical structures in the chest X-ray and extract regions that exhibit shape characteristics of lung nodules. Subsequently, first and second order statistical texture features are considered as the inputs to train a neural network to verify whether a region extracted in the first stage is a nodule or not . The proposed approach detected nodules in the diseased area of the lung with an accuracy of 96% using the pixel-based technique while the feature-based technique produced an accuracy of 88%.
CITED BY (6)  
1 Savitha, S. K., & Naveen, N. C. Study for Assessing the Advancement of Imaging Techniques in Chest Radiographic Images.
2 Kausar, N., Samir, B. B., & Kuleev, R. (2006). Lung cancer detection using supervised classification with cluster variability on radiographs data. women, 25, 19-04.
3 Punithavathy, K., Ramya, M. M., & Poobal, S. Analysis of Statistical Texture Features for Automatic Lung Cancer Detection in PET/CT Images.
4 Bhuvaneswari, P., & Therese, A. B. (2015). Detection of Cancer in Lung with K-NN Classification Using Genetic Algorithm. Procedia Materials Science, 10, 433-440.
5 Kumar, T. S., Narasimhan, G., & Umamaheswari, R. (2014). Texture Pattern Based Lung Nodule Detection (TPLND) Technique in CT Images. International Review on Computers and Software (IRECOS), 9(3), 415-426.
6 Babu, S. S., Subrahmanyam, K. V., & Tech, M. Lung Cancer Diagnosis using PET/CT Images based on preprocessing methods.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A.M.R. Schilham, B.V. Ginneken and M. Loog, A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database, Medical Image Analysis, vol. 10, N 2, pp. 247-258, 2006.
B.V. Ginneken, B.M.T.H. Romeny and M.A. Viergever, Computer Aided Diagnosis in chest radiography: A survey, IEEE Trans. on Medical Imaging, vol. 20, pp. 1228-1241, 2001.
D.H. Ballard and J. Sklansky, A ladder-structured decision tree for recognizing tumors in chest radiographs, IEEE Trans. on Computers, vol. C-25, pp. 503-513, 1976.
G. Coppini, S. Diciotti, M. Falchini, N. Villari and G. Valli, Neural networks for computer aided diagnosis: detection of lung nodules in chest radiograms, IEEE Trans. on Information Technology in Biomedicine, vol. 4, pp. 344-357, 2003.
G.S. Cox, F.J. Hoare and G. de Jager, Experiments in lung cancer nodule detection using texture analysis and neural network classifiers, Third South African Workshop on Pattern Recognition, 1992.
J. Wei, Y. Hagihara, A. Shimizu and H. Kobatake, Optimal image feature set for detecting lung nodules on chest X-ray images, Proc. Int. Workshop on Computer-Aided Diagnosis, 2002.
J.S. Lin, S.B. Lo, A. Hasegawa, M.T. Freedman and S.K. Mun, Reduction of false positives in lung nodule detection using a two-level neural classi_cation, IEEE Trans. on Medical Imaging, vol. 15, pp. 206-216, 1996.
J.W. Lee, H.W. Lee, J.H. Lee, I.T. Kang and G.K. Lee, A study on lung nodule detection using neural networks, Proc. IEEE Region 10 Conference, pp. 1150 - 1153, 1999.
Japanese Society of Radiological Technology, http://www.jsrt.or.jp
K. Le, Automated detection of early lung cancer and tuberculosis based on X-ray image analysis, Proc. WSEAS International Conference on Signal, Speech and Image Processing, pp. 1-6, 2006.
K. Suzuki, J. Shiraishi, H. Abe, H. MacMahon and K. Doi, False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network, Academic Radiology, vol. 12, N 2, pp. 191-201, 2003.
L. Zhang and X. Gao, Research on translation invariant wavelet transform for classification in mammograms, Proc. 3rd international conference on natural computation, 2007.
M. Park, J. S. Jin and L. S. Wilson, Detection of abnormal texture in chest X-rays with reduction of ribs, Proc. Pan-Sydney area workshop on visual information processing, 2004.
M.G. Penedo, M.J. Carreira, A. Mosquera and D. Cabello, Computer aided diagnosis: A neural network based approach to lung nodule detection, IEEE Trans. on Medical Imaging, vol. 17, N 6. pp. 872-880, 1998.
M.N. Gurcan, B. Sahiner, N. Petrick, H.P. Chan, E.A. Kazerooni, P.N. Cascade and L. Hadjiiski, Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system, Medical Physics, vol. 29, N 11, pp. 2552, 2002.
O. Ludwig and U. Nunes, Novel maximum- margin training algorithms for supervised neural networks, IEEE Trans. on Neural Networks, vol. 21, N 6, pp. 972-983, 2010.
P. Campadelli, E. Casiraghi and D. Artioli, A fully automated method for lung nodule detection from postero-anterior chest radiographs, IEEE Trans. on Medical Imaging, vol. 25, N 12, pp. 1588-1602, 2006.
P.R. Snoeren, G.J.S. Litjens, B.V. Ginneken and N. Karssemeijer, Training a computer aided detection system with simulated lung nodules in chest radiographs, Proc. 3rd International Workshop on Pulmonary Image Analysis, Beijing, 2010.
R. C. Gonzalez , R.E. Woods and S. L. Eddins. Digital Image Processing, Addison-Wesley, 2002.
R. Hayashibe, N. Asano, H. Hirohata, K. Okumura, S. Kondo, S. Handa, M. Takizawa, S. Sone and S. Oshita, An automatic lung cancer detection from X-ray images obtained through yearly serial mass survey, Proc. International conference on image processing, pp. 343 - 346, 1996.
R. N. Strickland, Tumor detection in nonstationary backgrounds, IEEE Trans. on Medical Imaging, vol. 13, pp. 491-499, 1994.
S. A. Patil and V. R. Udupi, Chest X-rays features extraction for lung cancer classification, Journal of Scientific and Industrial Research, vol. 69, pp. 271-277, 2010.
S.B. Lo, S.L. Lou, J.S. Lin, M.T. Freedman and S.K. Mun, Arti_cial convolution neural network techniques and applications for lung nodule detection, IEEE Trans. on Medical Imaging, vol. 14, pp. 711-718, 1995.
T. Rasheed, B. Ahmad, M. A. U. Khan, M. Bettayeb, S. Lee and T. S. Kim, Rib suppression in frontal chest radiographs: A blind source separation approach, Proc. Signal Processing and its Applications, pp. 1 - 4, 2007.
Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, Nodule detection on chest helical CT scans by using a genetic algorithm, Proc. Intelligent Information Systems, pp. 67 - 70, 1997.
Y.S.P. Chiou, Y.M.F. Lure and P.A. Ligomenides, Neural networks image analysis and classification in hybrid lung nodule detection (HLND) system, IEEE Workshop on Neural Networks for Signal Processing, pp. 517-526, 1993.
Z.H. Zhou, Y. Jiang, Y.B. Yang and S.F. Chen, Lung cancer cell identification based on artificial neural network ensembles, Artificial Intelligence in Medicine, vol. 24, N 1, pp. 25-36, 2002.
Dr. K.A.G. Udeshani
Excel Technology Lanka Ltd - Sri Lanka
Dr. R.G.N. Meegama
University of Sri Jayewardenepura - Sri Lanka
Dr. T.G.I. Fernando
University of Sri Jayewardenepura - Sri Lanka