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Computer Aided Detection of Large Lung Nodules using Chest Computer Tomography Images

Mai Mabrouk, Ayat Karrar, Amr Sharawy Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
Info Co-published with IJCA
10.5120/ijais12-450560
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  1. Mai Mabrouk, Ayat Karrar and Amr Sharawy. Article: Computer Aided Detection of Large Lung Nodules using Chest Computer Tomography Images. International Journal of Applied Information Systems 3(9):12-18, August 2012. BibTeX

    @article{key:article,
    	author = "Mai Mabrouk and Ayat Karrar and Amr Sharawy",
    	title = "Article: Computer Aided Detection of Large Lung Nodules using Chest Computer Tomography Images",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 9,
    	pages = "12-18",
    	month = "August",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Lung cancer is the most common cancer which leads to death for both women and men, so the early detection of lung cancer increases the therapy success. Different techniques are used to provide the early detection such as Computer Aided Detection (CAD) system. In this paper, we present an automatic Computer Aided Detection (CAD) system to detect a large lung nodule from lateral Chest Radiographs of computed tomography (CT) images to reduce false positive rates. Basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, radon transform and edge detection are applied to the CT scan images in order to detect the lung region. A total of 22 image features were extracted from the enhanced image based on statistical features such as standard deviation, average and mean. A fisher score ranking method is used as a feature selection method to select best ten features (standard deviation, variance, range, maximum grey level, seven invariant moments except the second, sixth and seventh invariant moments and 5th percentile, 9th percentile). Thus optimal screening modalities have both high sensitivity and specificity based on artificial neural network (ANN) significantly more accurate than using K-Nearest Neighborhood (KNN) classifier with accuracy 98% and 96% respectively in detecting large lung nodule with equivalent diameter ranging from 22. 65 mm to 41. 62 mm.

Reference

  1. World Health Organization (WHO), February 2006.
  2. Minna. JD and Schiller JH," Harrison's Principles of Internal Medicine (17th ed. )", McGraw-Hill, pp. 551–562, 2008.
  3. Http//www3. cancer. gov/bip/lidc_comm. htm.
  4. Rachid Sammouda1. Jamal Abu Hassan1,. Mohamed Sammouda2, Abdulridha Al-Zuhairy 3 and Hatem abou ElAbbas," Computer Aided Diagnosis System for Early Detection of Lung Cancer Using Chest Computer Tomography Images", GVIP 05 Conference, CICC, Cairo, Egypt, 19-21 December 2005.
  5. M. Gomathi and P. Thangaraj, "A Computer Aided Diagnosis System for Lung Cancer Detection using Machine Learning Technique", European Journal of Scientific Research, Vol. 51 No. 2, pp. 260-275, 2011.
  6. Volkan Vural1. Glenn Fung2. Balaji Krishnapuram2. Jennifer Dy1 and Bharat Rao2," Batch Classification with Applications in Computer Aided Diagnosis", European Conference on Machine Learning (ECML), vol. 4212, p. 449-460, 2006.
  7. R Bharat Rao. Jinbo Bi. Glenn Fung, Marcos Salganicoff. Nancy Obuchowski and David Naidich, "LungCAD: A Clinically Approved, Machine Learning System for Lung Cancer Detection ", Knowledge Discovery and data mining conference (KDD), p. 1033-1037, August 12-15, 2007.
  8. P. Korfiatis. C. Kalogeropoulou. L. Costaridou," Computer Aided Detection of Lung Nodules in Multislice Computed Tomography", the International Special Topic Conference on Information Technology in Biomedicine IEEEITAB, 2006.
  9. Alessandro Riccardi. Todor Sergueev Petkova. Gianluca Ferri. Matteo Masotti and Renato Campanini," Computer-Aided Detection of Lung Nodules via 3D Fast Radial Transform, Scale Space Representation, and Zernike MIP Classification ",the international journal of medical physics research & practice,vol. 38, 1962 ,2010.
  10. M. Gomathi and Dr. P. Thangaraj," Lung Nodule Detection using a Neural Classifier", IACSIT International Journal of Engineering and Technology, Vol. 2, No. 3, June 2010.
  11. (2002) Cornell university website. [Online]. Available: http://www. via. cornell. edu/databases.
  12. H. Selvaraj1, S. Thamarai Selvi2, D. Selvathi3 and L. Gewali1,"Brain MRI Slices Classification Using Least Squares Support Vector Machine", Vol. 1, No. 1, Issue 1, P. 21 - 33, 2007.
  13. Mohamed A. Alolfe, Abo-Bakr M. Youssef, Yasser M. Kadah, and A. S. Mohamed "Development of a Computer-Aided Diagnostic System for Cancer Detection from Digital Mammograms", 25th National Radio Science Conference, March 18-20, 2008.
  14. Yasser M. Kadah, Aly A farag, Ahmed M. badawy and Abou-Baker M. Youssef, "Classification algorithm for quantitative tissue characterization of diffuse liver disease from ultrasound," IEEE Trans. Med. Imag. , vol. 15, pp. 466-478, August 1996.
  15. Rafael Gonzalez and Richard woods," Digital Images Processing". Prentice Hall, 2002.
  16. CHAP T. LE, Introductory Biostatistics. A John Wiley & Sons Publication, April 2003.
  17. Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, and Yuval Elovici" Improving Worm Detection with Artificial Neural Networks through Feature Selection and Temporal Analysis ", International Journal of Applied Mathematics and Computer Sciences, Vol. 1, Number1, August 2006.
  18. J. A. Freeman and D. M. Skapura, "Neural Networks, Algorithms, Applications and Programming Tech-niques", Addison- Wesley Publishing Company, (2002).
  19. S. Haykin, "Neural networks: A comprehensive Foundation", 2nd ed. Englewood Cliffs, NJ: Prentice Hall, 1999.
  20. D. G. Altman and J. M. Bland, "Diagnostic tests 1: Sensitivity and specificity," Br. Med. J. , vol. 308, pp. 1552–1552, 1994.
  21. José Silvestre Silva, Augusto Silva and Beatriz Sousa Santos," Lung Segmentation Methods in X-ray CT Images", 5th Iberoamerican Symposium on Pattern Recognition, vol. 15,pp. 583-598, 2000.
  22. M. Gomathi and P. Thangaraj," A Computer Aided Diagnosis System for Detection of Lung Cancer Nodules Using Extreme Learning Machine", International Journal of Engineering Science and Technology, Vol. 2(10), 2010.
  23. Ted W. Way, Berkman Sahiner, Heang-Ping Chan, Lubomir Hadjiiski, Philip N. Cascade, Aamer Chughtai, Naama Bogot, and Ella Kazerooni, "Computer-aided diagnosis of pulmonary nodules on CT scans: Improvement of classification performance with nodule surface features", the international journal of medical physics research & practice, vol. 36, 2009 Jul;36(7):3086-98.
  24. Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang,Zhang Mengxia,Guo Aimin1 and Wang Wei1," Support Vector Machine Prediction Model of Early-stage Lung Cancer Based on Curvelet. Transform to Extract Texture Features of CT Image", World Academy of Science, Engineering and Technology 71, vol 17, 2010
  25. Hui Chen, Wenfang Wu, Hong Xia, Jing Du, Miao Yang and Binrong Ma, "Classification on pulmonary nodules using neural network ensemble", lecture notes in computer science, vol. 6677, 2011.
  26. Ayman El-Baz, Matthew Nitzken, Fahmi Khalifa, Ahmed Elnakib, Georgy Gimel'farb, Robert Falk and Mohammed Abo El-Ghar, " 3D Shape Analysis for Early Diagnosis of Malignant Lung Nodules ", Lecture Notes in Computer Science, Vol. 6801, 2011.
  27. R. Nithya, B. Santhi, "Mammogram Classification Using Maximum Difference Feature Selection Method", Journal of Theoretical and Applied Information Technology, Vol. 33, pp 197 - 204, 2011.

Keywords

Computer Aided Diagnosis (CAD), Computed tomography (CT), Radon transform, Artificial Neural Network (ANN), K-Nearest Neighborhood (KNN)