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Brain Tumor Detection Using Depth-First Search Tree Segmentation

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

With the advent of image processing technologies, the in-depth portion of human body can be epitomized visually to perceive abnormalities in human anatomy. Image processing is a tool for identifying the substances and obtaining information from them. Medical image processing is a stimulating area to diagnose diseases specifically, brain cancer, breast cancer, liver cancer, neuro- and cardio-diseases, etc. Image segmentation is an act of segregating the images into various parts to identify a particular substance and its margins. Brain tumor is the irregular and intense growth of tissues causing cancer. The most used technique to diagnose brain tumor is Magnetic Resonance Imaging (MRI). Precise information about the affected area is crucial for the appropriate treatment. As numerous data are created in MRI diagnosis, an automated segmentation technique is necessary to obtain precise information of tumor. In this paper we presented Depth-First Search (DFS) segmentation algorithm based on graph theory. Here the image pixels are arranged into a tree like structure based on their proximity in the image. The experimental results are compared with other existing systems. Also performance measures of ANFIS classifier and SVM classifier are compared. It distinguishes healthy cells from the cells affected by brain tumors. In the proposed method, the computational complexity is reduced and accuracy is enhanced.

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References

  1. Aparna, M., and Nichat, S. A., Ladhake brain tumor segmentation and classification using modified FCM and SVM classifier. Int. J. Adv. Res. Comput. Commun. Eng. 5, 4, 2016.

  2. Prema, V., Sivasubramanian, M., and Meenakshi, S., Brain cancer feature extraction using Otsu's thresholding segmentation. International Journal of computer application (2250-1797) 6– No.3, 2016.

  3. M.Latha, R. Surya ‘Brain tumour detection using neural network classifier and k-means clustering algorithm for classification and segmentation. IIR J. Soft Comput. 01 Issue: 01, June 2016, Pages: 27-32

  4. BalajiKanade, P., and Gumaste, P. P. P., Brain tumor detection using MRI images. INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING 3, (2), 2015.

  5. BalajiKanade, P., and Gumaste, P. P. P., Brain tumor detection using MRI images. Int J. Innov. Res. Electric, Electron. Instrum. Contrl. Eng. 3, (2), 2015.

  6. Joshi, M. A., and Shah, D. H., Survey of brain tumor detection techniques through MRI images. AIJRFANS, ISSN: 2328-3785. 09, 2015.

  7. Nagalkar, V. J., and Asole, S. S., Brain tumor detection using digital image processing based on soft computing. J. Sign. Image Process. 3(3):102–105, 2015.

    Google Scholar 

  8. Nazmy, T.M., and El-messiry, H.B., Al-bokhityadaptiveneuro-fuzzy inference system for classification of ecg signals. J. Theor. Appl. Inform. Technol. 2015.

  9. Azhari, E., MudzakkirMohd, M. Hatta, Z. and Win, S. L., Brain tumor detection and localization in Magnetic Resonance Imaging. Int. J. Inform. Technol. Converg. Serv. (IJITCS) 4, (1), 2014.

  10. General Information about Adult Brain Tumors. NCI. 2014-04-14. Retrieved 8 June 2014.

  11. Oscar, E., Gert, W., Subrahmanyam, G., María-J, L.-C., Jean-Philippe, T., Andrés, S. et al., Multivariate bayesian image segmentation tool. Comput. Method Prog. Biomed. 115:76–94, 2014.

    Article  Google Scholar 

  12. Salman, Y.L., Assal, M.A., Badawi, A.M., Alian, S.M. and Bayome, M.-E., Validation techniques for quantitative brain tumors measurements. IEEE Conf., 7048-7051, 2005. doi:https://doi.org/10.1109/IEMBS.2005.1616129.

  13. Sudharani, K., Sarma, T.C., and Satya Prasad, K., Advanced morphological technique for automatic brain tumor detection and evaluation of statistical parameters. Int. Conf. Emerg. Trends Eng. Sci. Technol. ICETEST, 2015.

  14. Pereira, S., Pinto, A., Alves, V. and Silva, C. A., Brain tumor segmentation using convolutional neural networks in MRI images 0278-0062 (c) 2015 IEEE transactions on medical imaging.

  15. Shanthakumar, P., and Ganeshkumar, P., Performance analysis of classifier for brain tumor detection and diagnosis. Comput. Electr. Eng., 2015.

  16. Vipin Y. Borole, Sunil S. Nimbhore, Dr. Seema S. Kawthekar ‘Image processing techniques for brain tumor detection: A review’ Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) Volume 4, Issue 5(2), September - October 2015.

  17. Sharma, M., and Mukharjee, S., Artificial neural network fuzzy inference system (ANFIS) for brain tumor detection. Adv. Intell. Syst. Comput. Adv. Comput. Inform. Technol. 177, 2014.

  18. Senthilkumaran, N., and Thimmiaraja, J., Histogram equalization for image enhancement using MRI brain images. IEEE CPS, WCCCT 45, 2014.

  19. Joseph, R. P., Senthil Singh, C., and Manikandan, M., Brain tumor MRI image segmentation and detection in image processing IJRET. International Journal of Research in Engineering and technology volume: 03 special issue: 01 | NC-WiCOMET-2014, 2014.

  20. Lakshmi, A., and Arivoli, T., Computer aided diagnosis system for brain tumor detection and segmentation. J. Theor. Appl. Inform. Technol. 64:561–567, 2014.

    Google Scholar 

  21. Patil, R. C., and Bhalchandra, A.S., Brain tumor extraction from MRI images using MatLab. IJECSCSE, ISSN: 2277-9477, 2(1), 2013.

  22. Karuna, M., and Joshi, A., Automatic detection and severity analysis of brain tumors using gui in matlab. IJRET: Int. J. Res. Eng. Technol. ISSN: 2319-1163, 02(10), 2013.

    Article  Google Scholar 

  23. Loganathan, C., and Girija, K.V., Cancer classification using adaptive neuro fuzzy inference system with Runge Kutta learning. Int. J. Comput. Applic. (0975 – 8887) 79(4), 2013.

    Article  Google Scholar 

  24. Al-Badarnech, A., Najadat, H., and Alraziqi, A. M., A classifier to detect tumor disease in MRI brain images. IEEE Computer Society, ASONAM. 2012, 142 tumor.

  25. Al-Tarawneh, M. S., “Brain cancer detection using image processing techniques. Leonardo Electronic Journal of Practices and Technologies, Issue 20. 147- 158, 2012. ISSN 1583-1078.

  26. Karimaghaloo, Z., Mohak, S., Francis, S. J., Arnold, D. L., Collins, D. L., and Arbel, T., Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields. IEEE Trans. Med. Imaging 31:1181–1193, 2012.

    Article  Google Scholar 

  27. Amutha, A., and Wahidabanu, R. S. D., A novel method for brain tumor diagnosis and segmentation using level set- active contour modelling. Eur. J. Sci. Res. 90(2):175–187, 2012.

    Google Scholar 

  28. Harati, V., Khayati, R., and Farzan, A., Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images. Comput. Biol. Med. 41(7):483–492, 2011.

    Article  Google Scholar 

  29. Dubey, R. B., Hanmandlu, M., and Vasikarla, S., Evaluation of three methods for MRI brain tumor segmentation. IEEE Comput Soc, ITNG, 2011.

  30. Vasuda, P., and Satheesh, S., Improved fuzzy C-means algorithm for MR brain image segmentation. Int. J. Comput. Sci. Eng. 02(05):1713–1715, 2010.

    Google Scholar 

  31. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., and Zhu, Y., Multi-Kernel SVM based classification for brain tumor segmentation of MRI multi-sequence. 16th IEEE international conference on image processing ICIP 2009.

  32. Zacharaki, E.I., Wang, S., Chawla, S., Soo Yoo, D., Wolf, R., Melhem, E. R., and Davatzikos, C., MRI-based classification of brain tumor type and grade using SVM-RFE IEEE 978-1-4244-3932-4/09, 2009.

  33. Acharya, T., and Ray, A. K., Image processing: Principles and applications. Wiley-Interscience, 2005.

  34. Li, G.-Z., Yang, J., Yeb, C.-Z., and Geng, D.-Y., Degree prediction of malignancy in brain glioma using supportvectormachines. Comput. Biol. Med. 36:313–325, 2004.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  36. Peck, D., Windham, J., Emery, L., Soltanian-Zadeh, H., Hearshen, D., and Mikkelsen, T., Cerebral tumor volume calculations using planimetric and eigen image analysis. Med. Phys. 23(12):2035–2042, 1996.

    Article  CAS  Google Scholar 

  37. Shing, J. and Jang, R., ANFIS: AdaptiveNetwork-Based Fuzzy Inference System,”computer methods and programs in biomedicine. IEEE Trans. Syst. University of California, 1993.

  38. Haralick, R. M., and Shanmugam, K., Its’HakDinstein, ‘texture features for image classification’. IEEE Trans. Syst. Man Cybernet. SMC-3(6):610–621, 1973.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, AURCM, Madurai, Tamil Nadu, India

    S. Janardhanaprabhu

  2. Department of Electrical Electronics Engineering, AURCM, Madurai, Tamil Nadu, India

    V. Malathi

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  1. S. Janardhanaprabhu
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  2. V. Malathi
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Correspondence to S. Janardhanaprabhu.

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Janardhanaprabhu, S., Malathi, V. Brain Tumor Detection Using Depth-First Search Tree Segmentation. J Med Syst 43, 254 (2019). https://doi.org/10.1007/s10916-019-1366-6

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