IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 10    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 10,October 2012
Soft Set Based Feature Selection Approach for Lung Cancer Images
Full Text(PDF, )  PP.1293-1299  
Author(s)
Jothi. G and Hannah Inbarani. H
KEYWORDS
Clustering Techniques, CT Lung Cancer Images, Feature Extraction, Lung Segmentation, Soft Set Theory, Soft Set Based Quick Reduct, Unsupervised Feature Selection.
ABSTRACT
Lung cancer is the deadliest type of cancer for both men and women. Feature selection plays a vital role in cancer classification. This paper investigates the feature selection process in Computed Tomographic (CT) lung cancer images using soft set theory.
References
[1] J.L Ma, “The Peripheral Non-small Cell Lung Cancer Subtype Diagnosis Model Research Based On Ct Image”Master Degree Thesis.

[2] Pabitra Mitra, C.A. Murthy and Sankar K. Pal, “Unsupervised Feature Selection Using Feature Similarity”, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol 24, 2002.

[3] X. Fu, F .Tan, H.Wang, Y-Q.Zhang, R. Harrison, “Feature Similarity Based Redundancy Reduction for Gene Selection”, Conference on Data Mining, 2006.

[4] Molodtsov, D.: “Soft set theory-first results”, Computers and Mathematics with Applications vol. 37, pp. 19–31, 1999.

[5] In-Hee Lee, Gerald H Lushington and Mahesh Visvanathan, “A filter-based feature selection approach for identifying potential biomarkers for lung cancer”, Journal of Clinical Bioinformatics, 2011.

[6] Hanan M. Amer, Fatma E.Z. Abou-Chadi, and Marwa I. Obayya, “A Computer-Aided System for Classifying Computed Tomographic (CT) Lung Images Using Artificial Neural Network and Data Fusion”, International Journal of Computer Science and Network Security (IJCSNS), vol.11, pp. 70-75, 2011.

[7] C.F. Aliferis, I. Tsamardinos, P.P. Massion, A. Statnikov, N. Fananapazir, D. Hardin, “Machine Learning Models For Classification Of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data”, American Association for Artificial Intelligence, 2002.

[8] Maji, P.K., Biswas, R., Roy, and A.R.: “Soft set theory. Computers and Mathematics with Applications”, vol. 45, pp. 555–562, 2003.

[9] Tutut Herawan, Rozaida Ghazali, Mustafa Mat Deris, “Soft Set Theoretic Approach for Dimensionality Reduction”, International Journal of Database Theory and Application, Vol. 3, No. 2, pp. 171–178, 2010.

[10] Norhalina Senan, Rosziati Ibrahim, Nazri Mohd Nawi, Iwan Tri Riyadi Yanto, and Tutut Herawan, “Soft Set Theory for Feature Selection of Traditional Malay Musical Instrument Sounds”, ICICA 2010, LNCS 6377, pp. 253–260, 2010.

[11] J. Han and M. Kamber. “Data Mining: Concepts and Techniques”, Morgan Kaufman, 2001.

[12] H. Liu and H. Motoda, editors. “Computational Methods of Feature Selection”, Chapman and Hall/CRC Press, 2007.

[13] Herawan, T., Mustafa, M.D.: “On Multi-soft Sets Construction in Information Systems”, LNCS, Springer, vol. 5755, pp. 101–110, 2009.

[14] Xingbo Sun, Xiuhua Tang, Huanglin Zeng, and Shunyong Zhou, “A Heuristic Algorithm Based on Attribute Importance for Feature Selection”, RSKT, pp. 189-196, 2008.

[15] Gonzales R.C. and Woods R.E., “Digital Image Processing”, 2nd Edition, New Jersey, Prentice Hall, 2004.

[16] David Jakobsson and Fredrik Olofsson, “Decision Support System for Lung Cancer using PET/CT Images” ,Ph. D Thesis, 2004.

[17] R. M. Haralick, K. Shanmugan, I. Dinstein, “Textural features for image classification”, IEEE Trans. Syst., vol. 3, pp. 610–621, 1973.

[18] C. Velayutham and K. Thangavel, “Unsupervised Quick Reduct Algorithm Using Rough Set Theory”, Journal of Electronic Science And Technology, vol. 9, pp. 163-168, 2011.

[19] http://cancerimagingarchive.net/

[20] Palaniappan, S., Hong, T.K, “Discretization of Continuous Valued Dimensions in OLAP Data Cubes”, International Journal of Computer Science and Network Security vol. 8, pp. 116–126, 2008.

Untitled Page