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An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure

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Abstract

The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

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References

  1. Marcela, X. R., Pedro, H. B., Caetano, T. J., Paulo, M. A. M., Natalia, A. R., and Agma, J. M., Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques. Data and Knowledge Engineering 68(12):1370–1382, 2009.

    Article  Google Scholar 

  2. Tourassi, G. D., Journey toward computer-aided diagnosis role of image texture analysis. Radiology 213:317–320, 1999.

    Google Scholar 

  3. Erickson, B. J., and Bartholmai, B., Computer-aided detection and diagnosis at the start of the third millennium. Journal of Digit. Imaging 15:59–68, 2002.

    Article  Google Scholar 

  4. Daniela, S. R., Mining Knowledge in computer tomography image databases. multimedia data mining and knowledge discovery. Springer, London, 2007.

    Google Scholar 

  5. Pan, H., Jianzhong, L., and Zhang, W., Incorporating domain knowledge into medical image clustering. Journal of Applied Mathematics and Computation 185:844–856, 2007.

    Article  MATH  Google Scholar 

  6. Kotsiantis, S., and Kanellopoulos, D., Association Rules Mining: A recent overview. GESTS International Transactions on Computer Science and Engineering 32(1):71–82, 2006.

    Google Scholar 

  7. Ordonez C., Omiecinski E., Image mining: A new approach for data mining. Technical Report GITCC-98-12, Georgia Institute of Technology, College of Computing, pp 1–21, 1998.

  8. Abraham R., Simha J.B., Iyengar S.S., Medical datamining with a new algorithm for feature selection and naive bayesian classifier. In Proc: 10th International Conference on Information Technology (ICIT), pp. 44–49, 2007.

  9. Wynne, H., Mong, L. L., and Zhang, J., Image mining: trends and developments. Journal of intelligent information systems 19(1):7–23, 2002.

    Article  Google Scholar 

  10. Stanchev P., Flint M., Using image mining For image retrieval. In. Proc: IASTED conf. Computer Science and Technology, pp. 214–218, 2003.

  11. Thomas, B., Analyzing and mining image databases. Reviews drug discovery today: Biosilico 10(11):795–802, 2005.

    Google Scholar 

  12. Ordonez C., Omiecinski E., Discovering association rules based on image content. In Proc: IEEE Forum ADL, pp. 38–49, 1999.

  13. Felipe J.C., Traina A.J.M., Traina C., Retrieval by content of medical images using texture for tissue identification. In Proc: 16th IEEE Symp. computer-based med. systems. CBMS 2003, pp. 175–180, 2003.

  14. Megalooikonomou V., For J., Shen., Makedon F., Data mining in brain imaging. Statistical Methods in Medical Research, pp. 359–394, 2000.

  15. Pluim, J. P. W., Maintz, J. B. A., and Viergever, M., Mutual-information-based registration of medical images: A survey. IEEE transactions on medical imaging 22(8):986–1004, 2003.

    Article  Google Scholar 

  16. Antonie M.L., Zaiane O.R., Coman A., Associative classifiers for medical images. Revised Papers from MDM/KDD and PAKDD/KDMCD, pp. 68–83, 2002.

  17. Dollar P., Zhuowen T., Hai T., Belongie S., Feature Mining for image classification. In Proc: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6, 2007.

  18. Dougherty J., Kohavi R., sahami M., Supervised and Unsupervised discretization of continuous features. In Proc: 12th International Conference Machine Learning, pp.56–69 ,1995.

  19. Agrawal R., Imielinski T., Swami A.N., Mining association rules between sets of items in large databases. In Proc: ACMSIGMOD Int. Conf. Manage, Washington, DC, pp. 207–216, 1993.

  20. Santhiyakumari, N., and Madheswaran, M., Non-invasive evaluation of carotid artery wall thickness using improved dynamic programming technique. International journal of Signal, Image and Video Processing, Springer London 2(2):183–193, 2008.

    Article  Google Scholar 

  21. Alejandro, P. A., and Gonzalo, P., Ultrasound Image segmentation with shape priors: Application to Automatic Cattle Rib-Eye Area Estimation. IEEE Transaction on image processing 16(6):1637–1645, 2007.

    Article  Google Scholar 

  22. Santhiyakumari N., Madheswaran M., Ultrasound carotid artery image segmentation using shape prior technique, International journal of Medical engineering and informatics, Inderscience, Accepted for publication, (Sep) 2010.

  23. Cancela P., Reyes F., Rodriguez P., Randall G., Fernanadez A., Automatic object detection using shape information in ultrasound images, In Proc. 3rd Int. Conf. Image Process, pp. 417–420, 2003.

  24. Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K. S., Briggs, R. W., and Geiser, E. A., Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vis 50(3):315–328, 2002.

    Article  MATH  Google Scholar 

  25. Charpiat, G., Faugeras, O., and Keriven, R., Approximation of shape metrics and application to shape warping and empirical shape statistics. Found. Comput. Math. 5(1):1–58, 2005.

    Article  MathSciNet  MATH  Google Scholar 

  26. Klassen, E., Srivastava, A., Mio, W., and Joshi, S., Analysis of planar shapes using geodesic paths on shape spaces. IEEE Trans. pattern Anal. mach. intell. 26(3):372–383, 2003.

    Article  Google Scholar 

  27. Christophe C., JeanS.G., Gael L.M., Michel K., Efficient data structures and parallel algorithms for association rules discovery. In Proc: Fifth Mexican International Conference in Computer Science (ENC), pp. 399–406, 2004.

  28. Dogu B.A., Markus H., Tuukka A., Prasun D., Jari H., Texture based classification and segmentation of tissues Using DT-CWT feature extraction methods. In Proc: 21st IEEE International Symposium on Computer-Based Medical Systems, pp.614–619, 2008.

  29. Foschi P.G., Kolippakkam D., Liu H., Mandvikar A., Feature extraction for image mining. In Proc: 8th Int. Workshop Multimedia Inf. Syst, Tempe, AZ, pp. 103–109, 2002.

  30. Hui L., Hanhu W., Mei C., Ten W., Clustering ensemble technique Applied in the discovery and diagnosis of brain lesions. In Proc: Sixth International Conference on Intelligent Systems Design and Applications (ISDA) , vol. 2: pp. 512–520, 2006.

  31. Joaquim C.F., Marcela X.R, Elaine P.M.S., Agma J.M.T., Caetano T.J., Effective shape-based retrieval and classification of mammograms. In Proc: ACM symposium on Applied computing, pp. 250–255, 2006.

  32. Mudigonda, N. R., and Rangayyan, R. M., Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imag. 20(12):1215–1227, 2001.

    Article  Google Scholar 

  33. Murat, K., and Cevdet, I. M., An expert system for detection of breast cancer based on association rules and neural network. An International Journal expert systems with applications 36:3465–3469, 2009.

    Article  Google Scholar 

  34. Lukasz K., Krzysztof W., Image classification with customized associative classifiers. In Proc: International conference on Computer Science and Information Technology, pp. 85–91, 2006.

  35. Agrawal R., Srikant R., Fast algorithms for mining association rules. In Proc: Int. Conf. VLDB, Santiago, Chile , pp. 487–499, 1994.

  36. Laila E., Walid A.A., mining medical databases using Proposed Incremental Association Rules Algorithm (PIA). In Proc: IEEE Second International Conference on the Digital Society, pp 88–92, 2008.

  37. Olukunle A., Ehikioya S.A., A fast algorithm for mining association rules in medical image data. In Proc: IEEE Canadian Conf. Electr. Comput. Eng. Conf, pp. 1181–1187, 2002.

  38. Maria L.A., Osmar R.Z., Alexandru C., Associative classifiers for medical images Mining. Lecture Notes in Computer Science, multimedia and complex data, Springer Berlin Heidelberg, pp.68–83, 2003.

  39. Wang X., Smith M., Rangayyan R., Mammographic information analysis through association-rule mining. In Proc: IEEE CCGEI, pp. 1495–1498, 2004.

  40. Liu B., Hsu W., Ma Y., Pruning and Summarizing the discovered associations. In Proc: ACM SIGKDD International conference on knowledge discovery & data mining , pp. 81–105, 1999.

  41. Osmar R.Z., Maria L.A., On Pruning and tuning rules for associative classifiers. In Proc: 9th International Conference(KES), Knowledge-Based Intelligent Information and Engineering Systems Melbourne, part III, 2005.

  42. Rajendran P., Madheswran M., Pruned associative classification technique for the medical image diagnosis system. In Proceedings of Second International Conference on Machine Vision, (ICMV December 2009) pp. 293–297, 2009.

  43. Vincent S.T., Ming-Hsiang W., Ja-Hwung S., A New method for image classification by using multilevel association rules. In Proc: 21st International Conference on Data Engineering Workshops (ICDEW), pp.1180–1188, 2005.

  44. Maryellen, L., Giger, N. K., and Armato, S. G., Computer-aided diagnosis in medical imaging. IEEE Trans. Med. Imag. 20(12):1205–1208, 2001.

    Article  Google Scholar 

  45. Rajendran, P., and Madheswran, M., An Improved Image mining technique for brain tumor classification using efficient classifier. International Journal of Computer Science and Information Security 6(3):107–116, 2009.

    Google Scholar 

  46. Haralick, R. M., Shanmugam, K., and Distein, I., Textural features for image classification. IEEE Trans. Syst, Man, Cybern SMC-3:610–621, 1973.

    Article  Google Scholar 

  47. John G.H., Langley P., Estimating continuous distributions in bayesian classifiers. In Proc: 11th conference on uncertainty in artificial intelligence Morgan Kaufmann, pp. 338–345., 1995.

  48. Kazmierska, J., and Malicki, J., Apllication of the Navie Bayeian classifier to optimize treatment decisions. Journal of Radiotherapy and Oncology 86(2):211–216, 2008.

    Article  Google Scholar 

  49. Marcela, X. R., Agma, J. M. T., Caetano, T., and Paulo, M. A. M., An association rule-based method to support medical image diagnosis with efficiency. IEEE transactions on multimedia 10(2):277–285, 2008.

    Article  Google Scholar 

  50. Ranjit A., Jay B.S., Iyengar S.S., Medical data mining with a new Algorithm for feature selection and naive bayesian classifier. In Proc: 10th International Conference on Information Technology (ICIT), pp.44–49, 2007.

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Acknowledgements

The authors would like to express their gratitude to Dr. D. Elangovan, Pandima CT scan centre, Dindigul for providing the necessary images for this study.

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Correspondence to M. Madheswaran.

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Rajendran, P., Madheswaran, M. An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure. J Med Syst 36, 747–764 (2012). https://doi.org/10.1007/s10916-010-9542-8

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