Image processing is a technique to improve the quality of unprocessed images obtained from cameras. These images were placed on satellites, aircrafts or pictures taken in day to day life for various applications as well as for different purpose. In recent years, image processing techniques were widely used in many medical areas for improving earlier diagnosis and stages for detecting any type of diseases. Due to high extensiveness allied with the inconvenient treatment, lung cancer has being pulling the attention of the medical communities in the latest years. Lung nodule classification and detection can be developed with the help of image processing techniques. This paper deals with the well organized method for classification of four categories of lung nodules, which includes: Well-Circumscribed (W), Juxta Pleural (J), Pleural-Tail (P) and Vascularized (V).

1. Fan Zhang, Yang Song, Weidong Cai, Member, Min-Zhao Lee, Yun Zhou, Heng Huang, Shimin Shan, Michael J Fulham, and Dagan D. Feng, "Lung Nodule Classi?cation With Multilevel Patch-Based Context Analysis", IEEE Transactions On Biomedical Engineering, Vol. 61, No. 4, April 2014.
2. R. Harini Karthika, P. Thirugnanam," Multilevel Pattern Recognition and Semantic Of LDA Analysis", Journal of Electronics and Computer Science, ISSN- 3967-0867, Vol 2 Issue 3 March 2015.
3. Amal Farag, Shireen Elhabian, James Graham, Aly Farag,Salwa Elshazly, Robert Falk, Hani Mahdi, Hossam Abdelmunim,Sahar Al Ghaafary," Modelling of the Lung Nodules for Detection in LDCT Scans",32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, 2010
4. Ravivarman. R,sasirekha. N2 ,"Survey On Lung Nodule Classifications", International Journal Of Research In Engineering And Technology ,sins: 2319-1163 | Pissn: 2321-7308,volume: 04 Issue: 01, Jan-2015
5. William J. Kostis, Anthony P. Reeves, David F. Yankelevitz, and Claudia I. Henschke, " Three-Dimensional Segmentation and Growth-Rate Estimation of Small Pulmonary Nodules in Helical CT Images", IEEE Transactions On Medical Imaging, Vol. 22, No. 10, October 2003
6. Yang Song, Weidong Cai, Yun Zhou, David Dagan Feng," Feature-Based Image Patch Approximation for Lung Tissue Classi?cation", IEEE Transactions On Medical Imaging, Vol. 32, No. 4, April 2013
7. Prashant Naresh, Dr. Rajashree shettar," Image Processing and Classification Techniques for Early Detection of Lung Cancer for Preventive Health Care: A Survey", Int. J. of Recent Trends in Engineering & Technology, Vol. 11, June 2014.
8. Stefano Diciotti,Giulia Picozzi, Massimo Falchini, Mario Mascalchi, Natale Villari, and Guido Valli," 3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images", IEEE Transactions On Information Technology In Biomedicine, Vol. 12, No. 1, January 2008
9. Asem M. Ali and Aly A. Farag,"Automatic Lung Segmentation of Volumetric Low-Dose CT Scans Using Graph Cuts ", : ISVC 2008,Part I, LNCS 5358, pp. 258267, 2008.
10. Sridhar. R, Saravanakumar. S, "Detection and Classification of Lung Nodules Using multi resolution MTANN in Chest Radiography Images ",The International Journal Of Engineering And Science (IJESISSN (e): 2319 1813 ISSN (p): 2319 1805 Pages:98-104,March – 2015
11. Dasu Vaman, Ravi prasad,"Lung cancer detection using image processing techniques", Interenational Journal of Latest Trends in Engineering and Technology ISSN: 2278-621X, Pages 373-378, Vol. 3 Issue 1 september2013.
12. Disha Sharma, Gagandeep Jindal," Identifying Lung Cancer Using Image Processing Techniques", International Conference on Computational Techniques and Artificial Intelligence (ICCTAI'2011)
13. Anil M. Yametkar, R. D. Patane," lung cancer detection and classification by using Bayesian classifier", Proceedings of IRF International Conference, ISBN: 978-93-82702-56-6, 5th 6th February 2014, Pune, India.

Lung Cancer Computerized Tomography Classification Detection