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Computer Assisted Diagnostic System in Tumor Radiography

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Abstract

An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46 %, which is a significant improvement than that of the existing results.

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References

  1. Shen, S., Sandham, W., Granat, M., and Sterr, A., MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural network optimization. IEEE Trans. Inf. Technol. Biomed. 9(3):459–467, 2005.

    Article  Google Scholar 

  2. Diaz, I., Boulanger, P., Griener, R., and Murtha A., A critical review of the effects of denoising algorithms on MRI brain tumor segmentation. 33rd Annual International Conference of the IEEE EMBS, Boston, Massachusetts, USA, pp. 3934–3937, 2011.

  3. Prashanta, H. S., Shahidhara, H. L., Murthy, K. N. B., and Madhavi, L. G., Medical image segmentation. Int. J. Comput. Sci. Eng. 2(4):1209–1218, 2010.

    Google Scholar 

  4. Krajsek, K., and Mester, R., The edge preserving winner filter for scalar and tensor valued image. DAGIM, pp. 91–100, 2006.

  5. Lysaker, M., Lundervold, A., and Tai, X.-C., Noise removal using fourth order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image. Proc. 12(12):1579–1590, 2003.

    Article  Google Scholar 

  6. Leung, C. C., Chen, W. F., Kwok, P. C. K., and Chan, F. H. Y., Brain tumor boundary detection in MR image with generalized fuzzy operator. Proceedings of the 2003 International Conference on Image Processing. Vol. 2, pp. II - 1057–60, Sept. 2003.

  7. Rajeswari, R., and Anandhakumar, P., Segmentation and identification of brain tumor MRI image with Radix4 FFT techniques. Eur. J. Sci. Res. 52(1):100–109, 2011.

    Google Scholar 

  8. Koley, S., and Majumder, A., Brain MRI segmentation for tumor detection using cohesion based self merging algorithm. 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 781–785, May 2011.

  9. Zhu, Y., and Yan, H., Computerized tumor boundary detection using a Hopfield neural network. IEEE Trans. Med. Imaging 16(1):55–67, 1997.

    Article  Google Scholar 

  10. Prastawa, M., Bullitt, E., Ho, S., and Gerig, G., A brain tumor segmentation framework based on outlier detection. Med. Image. Anal. 8(3):275–283, 2004.

    Article  Google Scholar 

  11. Kharrat, A., Gasmi, K., Messaoud, M. B., Benamrane, N., and Abid, M., A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo J. Sci. 9(17):71–82, 2004.

    Google Scholar 

  12. Gonzalez, R. C., and Woods, R. E., Digital image processing. Prentice Hall, Upper Saddle River, NJ, 2004.

    Google Scholar 

  13. You, Y.-L., Xu, W., Tannenbaum, A., and Kaveh, M., Behavioral analysis of anisotropic diffusion in image processing. IEEE Trans. Image. Proc. 5(11):1539–1553, 1996.

    Article  Google Scholar 

  14. Kaus, M. R., Warfield, S. K., Nabavi, A., Black, P. M., Jolsez, F. A., and Kikinis, R., Automated segmentation of MR images of brain tumors. Radiology 218:586–591, 2001.

    Google Scholar 

  15. Lysaker, M., Osher, S., and Tai, X.-C., Noise removal using smoothed normals and surface fitting. IEEE Trans. Image Process. 13(10):1345–1357, 2004.

    Article  MathSciNet  Google Scholar 

  16. Abdou, I. E., and Pratt, W. K., Quantitative design and evaluation of enhancement/thresholding edge detectors. Proc. IEEE 67(5):753–763, 1979.

    Article  Google Scholar 

  17. Rai, G. N. H., and Nair, T. R. G., Gradient based seeded region grow method for CT angiographic image segmentation. InterJRI Comput. Sci. Netw. 1(1):1–6, 2009.

    Google Scholar 

  18. Beghdadi, A., and Negrate, A. L., Contrast enhancement technique based on the local detection of edges. Comp. Vision Graph. Image Process. 46(2):162–174, 1989.

    Article  Google Scholar 

  19. Chen, B., Cai, J.-L., Chen, W.-S., and Li, Y., A multiplicative noise removal approach based on partial differential equation model. Mathematical Problems in Engineering. Vol. 2012, Article ID: 242043, March 2012 (Accepted).

  20. Reza, A. W., and Eswaran, C., A decision support system for automatic screening of non-proliferative diabetic retinopathy. J. Med. Syst. 35(1):17–24, 2011.

    Article  Google Scholar 

  21. Reza, A. W., Eswaran, C., and Hati, S., Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds. J. Med. Syst. 33:73–80, 2009.

    Article  Google Scholar 

  22. Reza, A. W., Eswaran, C., and Dimyati, K., Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation. J. Med. Syst. 35:1491–1501, 2011.

    Article  Google Scholar 

  23. Liu, J., Udupa, J. K., Odhner, D., Hackney, D., and Moonis, G., A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Comput. Med. Imaging Graph. 29(no. 1):21–34, 2005 [22].

    Article  Google Scholar 

  24. Xuan, X., and Liao, Q., Statistical structure analysis in MRI brain tumor segmentation. Fourth International Conference on Image and Graphics, pp. 421–426, August 2007.

  25. Clark, M. C., Hall, L. O., Goldgof, D. B., Velthuizen, R., Murtagh, R., and Silbiger, M. S., Automatic tumor segmentation using knowledge-based techniques. IEEE Trans. Med. Imaging 17(2):187–201, 1998.

    Article  Google Scholar 

  26. Prastawa, M., Bullitt, E., Ho, S., and Gerig, G., A brain tumor segmentation framework based on outlier detection. Med. Image Anal. J. 8(3):275–283, 2004.

    Article  Google Scholar 

  27. Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., and Reed Murtagh, F., Automatic segmentation of nonenhancing brain tumors in magnetic resonance images. Artif. Intell. Med. 21(1–3):43–63, 2001.

    Article  Google Scholar 

  28. Ghanavati, S., Junning, Li, Liu, T., Babyn, P. S., Doda, W., and Lampropoulos, G., Automatic brain tumor detection in magnetic resonance images. 9th IEEE International Symposium on Biomedical Imaging, pp. 574–577, May 2012 .

  29. Akram, M. U., and Usman, A., Computer aided system for brain tumor detection and segmentation. 2011 International Conference on Computer Networks and Information Technology, pp. 299–302, July 2011.

  30. Faisal, A., Parveen, S., Badsha, S., and Sarwar, H., An improved image denoising and segmentation approach for detecting tumor from 2-D MRI brian images. International Conference on Advanced Computer Science Applications and Technologies, Kuala Lumpur, November 2012.

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Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Ahmed Faisal & Sharmin Parveen

  2. Faculty of Engineering, Department of Electrical Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahriar Badsha & Ahmed Wasif Reza

  3. Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh

    Hasan Sarwar

Authors
  1. Ahmed Faisal
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  2. Sharmin Parveen
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  3. Shahriar Badsha
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  4. Hasan Sarwar
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  5. Ahmed Wasif Reza
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Correspondence to Ahmed Wasif Reza.

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Faisal, A., Parveen, S., Badsha, S. et al. Computer Assisted Diagnostic System in Tumor Radiography. J Med Syst 37, 9938 (2013). https://doi.org/10.1007/s10916-013-9938-3

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  • DOI: https://doi.org/10.1007/s10916-013-9938-3

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