Skip to main content
SpringerLink
Log in

Statistical Approach for Brain Cancer Classification Using a Region Growing Threshold

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In brain cancer, a biopsy as an invasive procedure is needed in order to differentiate between malignant and benign brain tumor. However, in some cases, it is difficult or harmful to perform such a procedure, to the brain. The aim of this study is to investigate a new method in maximizing the probability of brain cancer type detection without actual biopsy procedure. The proposed method combines both image and statistical analysis for tumor type detection. It employed image filtration and segmentation of the target region of interest with MRI to assure an accurate statistical interpretation of the results. Statistical analysis was based on utilizing the mean, range, box plot, and testing of hypothesis techniques to reach acceptable and accurate results in differentiating between those two types. This method was performed, examined and compared on actual patients with brain tumors. The results showed that the proposed method was quite successful in distinguishing between malignant and benign brain tumor with 95% confident that the results are correct based on statistical testing of hypothesis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. National Cancer Institute, USA. http://www.cancer.gov.

  2. King Hussein Cancer Centre, Jordan. http://www.khcc.jo.

  3. Jemal, A., Murray, T., Ward, E., et al., Cancer statistics 2005, CA. Cancer J. Clin. 55:10–30, 2005.

    Article  Google Scholar 

  4. Taheri, S., Sim Heng O., Chong, V., Threshold-based 3D tumor segmentation using level set (TSL). IEEE Workshop on Applications of Computer Vision, pp. 45–45, 2007.

  5. 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 

  6. Prastawa, M., Bullitt, E., and Gerig, G., Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal. 13 (2)297–311, 2009.

    Article  Google Scholar 

  7. Fletcher-Heath, L., Dmitry, O., Goldgof, B., and Reed Murtagh, F., Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif. Intell. Med. 21 (1–3)43–63, 2001.

    Article  Google Scholar 

  8. Clark, M. C., Hall, L. O., Goldgof, D. B., Velthuizen, R., Murtagh, F. R., and Silbiger, M., Automatic tumor-segmentation using knowledge-based techniques. IEEE TMI. 117:187–201, 1998.

    Google Scholar 

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

    Google Scholar 

  10. Leemput, K., Maes, F., Vandermeulen, D., and Suetens, P., Automated model-based tissue classification of MR images of the brain. IEEE TMI. 18:897–908, 1999.

    Google Scholar 

  11. Justin, M., and Iftekharuddin, K., Statistical analysis of fractal-based brain tumor detection algorithms. Magn. Reson. Imaging. 23 (5)671–678, 2005.

    Article  Google Scholar 

  12. Ashraf, M., Evangelia, I., Dinggang, S., and Christos, D., Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. Med. Image Anal. 10 (5)752–763, 2006.

    Article  Google Scholar 

  13. Xiao, X., and Qingmin, L., Statistical structure analysis in MRI brain tumor segmentation. Image and Graphics, ICIG 2007. Fourth International Conference, pp. 421–426, 2007.

  14. Mancas, M., Gosselin, B., and Macq, B., Segmentation using a region growing thresholding. Proceedings of the Electronic Imaging Conference of the International Society for Optical Imaging SPIE. 5672:388–398, 2005.

    Google Scholar 

  15. Sezgin, M., and Sankur, B., Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging. 13 (1)146–165, 2004.

    Article  Google Scholar 

  16. Chowdhury, M., and Little, W., Image thresholding techniques. IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing. 17–19:585–589, 1995.

    Article  Google Scholar 

  17. Pan, Zhigeng, P., and Jianfeng, L., A Bayes-based region-growing algorithm for medical image segmentation. Comput. Sci. Eng. 9 (4)32–38, 2007.

    Article  Google Scholar 

  18. Chuang, C. -L., and Chen, C. -M., A novel region-based approach for extracting brain tumor in CT images with precision. World Congress on Medical Physics and Biomedical Engineering. IFMBE Proceedings. 14 (4)2488–2492, 2006.

    Google Scholar 

  19. Chunyan, J., Xinhua, Z., Wanjun, H., and Meinel, C., Segmentation and quantification of brain tumor, virtual environments, human–computer interfaces and measurement systems. IEEE Symp. 12–14:61–66, 2004.

    Google Scholar 

  20. Velthuizen, R., Clarke, L., Phuphianich, S., Hall, L., Bensaid, A., Arrington, J., Greenberg, H., and Siblinger, M., Unsupervised measurement of brain tumor volume on MR images. J. Magn. Reson. Imaging. 5 (5)594–605, 1995.

    Article  Google Scholar 

  21. Vinitski, S., Gonzales, C., Mohamed, F., Iwanaga, T., Knobler, R., Khalili, K., and Mack, J., Improved intracranial lesion characterization by tissue segmentation based on a 3D feature map. Magn. Reson. Med. 37 (3)457–469, 1997.

    Article  Google Scholar 

  22. Gonzalez, R. C., Woods, R. E., and Eddins, S. L., Digital image processing, 2nd edition. Prentice Hall, Upper Saddle River, 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Hashemite University, P.O. Box 150459, Zarqa, 13115, Jordan

    Bassam Al-Naami, Hani Amasha & Jamal Al-Nabulsi

  2. Department of Industrial Engineering, Hashemite University, P.O. Box 150459, Zarqa, 13115, Jordan

    Adnan Bashir

  3. Physical Therapy Department, Hashemite University, P.O Box 330018, Zarqa, 13133, Jordan

    Abdul-Majeed Almalty

Authors
  1. Bassam Al-Naami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adnan Bashir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hani Amasha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jamal Al-Nabulsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdul-Majeed Almalty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bassam Al-Naami.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Al-Naami, B., Bashir, A., Amasha, H. et al. Statistical Approach for Brain Cancer Classification Using a Region Growing Threshold. J Med Syst 35, 463–471 (2011). https://doi.org/10.1007/s10916-009-9382-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9382-6

Keywords

Search

Navigation