Study on Capabilities of Different Segmentation Algorithms in Detecting and Reducing Brain Tumor Size in Magnetic Resonance Imaging for Effective Telemedicine Services

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  •   Jide Julius Popoola

  •   Thompson Emeka Godson

  •   Yekeen Olajide Olasoji

  •   Michael Rotimi Adu

Abstract

Over the past decade, different image segmentation algorithms have been developed and employed in segmenting or analyzing brain magnetic resonance imaging (MRI) scans in the clinical applications for the detection of brain tumor. However, accurate detection, compression and transmission of brain tumor data remain parts of the challenging tasks militating against brain tumor telemedicine services due to the complex nature of brain tumor MRI scans. In overcoming this challenge, five different brain tumor segmentation algorithms were developed for this study. The algorithms were developed using MATLAB scripts. The developed algorithms were evaluated using patients’ data retrieved from Mayfield website. The result of the comparative performance compression rate efficiency evaluation test carried out shows that the developed hybrid threshold-watershed segmentation algorithm outperforms others in terms of compression efficiency. The result implies that the usage of the developed hybrid threshold-watershed segmentation algorithm in transmitting brain tumor patients’ data transmission over wireless communication system will require limited bandwidth resource.


Keywords: Tumor, Brain Tumor, Telemedicine, Medical Imaging Techniques, Segmentation Algorithms

References

K. S. A. Viji, and J. Jayakumari, “Automatic Detection of Brain tumor based on Magnetic Resonance Image using CADd System with Watershed Segmentation,” in Proc. of IEEE 2011 International Conf. on Signal Processing, Communication, Computing and Networking Technologies, pp. 145-150, Thuckafay, India, 21-22 July 2011.

D. Bhattacharyya, and T. Kim, “Brain Tumor Detection using MRI Image Analysis,” in Proc. of 2nd International Conf. on Ubiquitous Computing and Multimedia Applications, pp. 307-314, Daejeon, Korea, 13-15 April 2011. doi:10.1007/978-3-642-20998-7_38.

N. V. Shree, T. N. R. Kumar, “Identification and Classification of Brain Tumor MRI Images with Feature Extraction using DWT and Probabilistic Neural Network,” Brain Informatics, vol. 5, no. 1, pp. 23-30, 2018.

A. Flowers, “Brain Tumor in the Older Person,” Cancer Control, vol. 7, no. 6, pp. 523-538, November/December 2000.

M. Serper, and M. L. Volk, Current and Future Applications of Telemedicine to Optimize the Delivery of Care in Chronic Liver Disease, Clinical Gastroenterology and Hepatology, vol. 16, no. 2, pp. 157-161, 2018.

B. M. Welch, J. Harvey, N. S. O’Connell, and, J. T. McElligott, “Patient Preferences for Direct-to- Consumer Telemedicine Services: A Nationwide Survey,” BMC Health Services Research, vol. 17, no. 1, pp. 1-7, 2017. doi:10.1186/s12913-017-2744-8.

P. Kakria, N. K. Tripathi, and P. Kitipawang, “A Real-Time Health Monitoring System for Remote Cardiac Patients using Smartphone and Wearable Sensors,” International Journal of Telemedicine and Applications, vol. 2015, pp. 1–11, 2015. doi:10.1155/2015/373474.

E. N. Mupela, P. Mustarde, and H. L. C. Jones, “Telemedicine in Primary Health: The Virtual Doctor Project Zambia,” Philosophy, Ethics and Humanities in Medicine, vol. 6, no. 9, pp. 1-8, 2011.

T. Takahashi, “The Present and Future of Telemedicine in Japan,” International Journal of Medical Informatics, vol. 61, pp. 131-137, 2001.

J. Grigsby, J. “Current Status of Domestic Telemedicine,” Journal of Medical Systems, vol. 19, no. 1, pp. 19-27, 1995.

F. Gonzalez, A. F. and Castro, “Publication Output in Telemedicine in Spain, Journal of Telemedicine and Telecare, vol. 11, no. 1, pp.23-28, 2005.

S. Mukhtar, S. and M. ul-Amin, “Cognitive Radio Technology— A Smarter Approach,” International Journal of Science, Engineering and Technology Research, vol. 5, no. 2, pp. 607-612, 2016.

M. Singh, P. Kumar, D. Anusheetal, S. K. Paruthi, “Techniques for Spectrum Sensing in Cognitive Radio Networks: Issues and Challenges,” International Research Journal of Engineering and Technology, vol. 3, no. 5, pp. 153-159, 2016.

J. J. Popoola, J.J. and R. van Olst, “A Survey on Dynamic Spectrum Access via Cognitive Radio: Taxonomy, Requirements, and Benefits, Universal Journal of Communications and Network, vol. 2, no. 4, pp. 70-85, 2014.

R. N. Clarke, “Expanding Mobile Wireless Capacity: The Challenges presented by Technology and Economics,” Telecommunications Policy, vol. 38, no. 8, pp. 693-708, 2014.

N. B. Bahadure, A. K. Ray, and H. P. Thethi, “Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction using Biologically Inspired BWT and SVM, International Journal of Biomedical Imaging, vol. 2017, pp. 1-12, 2017. http://doi.org/10.1155/2017/9749108.

A. Isin, C. Direkoğlu, M. Şah, “Review of MRI-Based Brain Tumor Image Segmentation using Deep Learning methods,” Procedia Computer Science, vol. 102, pp. 317-324, 2016. doi: org/10.1016/j.proccs.2016.09.401.

A. Nandi, “Detection of Human Brain Tumor using MRI Image Segmentation and Morphological Operators,” in Proc. of IEEE 2011 International Conf. on Computer Graphics, Vision and Information Security, pp. 55-60, 2015.

S. Fouad, D. Randell, A. Galton, H. Mehanna, G. Landini, Unsupervised Morphological Segmentation of Tissue Compartments in Histopathological Images,” PLoS ONE, vol. 12, no. 11, pp. 1-25, 2017. https://doi.org/10.1371/journal. pone.0188717

N. Gordillo, E. Montseny, and P. Sobrevilla, “State of the Art Survey on MRI Brain Tumor Segmentation, Magnetic Resonance Imaging, vol. 31, no. 8, pp. 1426–1438, 2013. doi:10.1016/j.mri.2013.05.002.

N. M. Saad, S. A. R. Abu-Bakar, S. Muda, and M. Mokji, “Segmentation of Brain Lesions in Diffusion-weighted MRI using Thresholding Technique,” in Proc. of 2011 IEEE International Conference on Signal and Image Processing Applications, 2011. doi:10.1109/icsipa.2011.6144092.

M. Angulakshmi, G. G. Lakshmi-Priya, “Automated Brain Tumor Segmentation Techniques- A Review,” International Journal of Imaging Systems and Technology, vol. 27, no. 1, pp. 66–77, 2017. doi:10.1002/ima.22211.

A. Aslam, E. Khan, and M. M. S. Beg, “Improved Edge Detection Algorithm for Brain Tumor Segmentation,” Procedia Computer Science, vol. 58, pp. 430–437, 2015. doi:10.1016/j.procs.2015.08.057

D. Selvaraj, and R. Dhanasekaran, “MRI Brain Image Segmentation Techniques- A Review,” Indian Journal of Computer Science and Engineering, vol. 4, no. 5, pp. 364-381, 2013.

J. Liu, M. Li, J. Wang, F. Wu, T. Liu, and Y. Pan, “A Survey of MRI-Based Brain Tumor Segmentation Methods,” Tsinghua Science and Technology, vol. 19, no. 6, pp. 578-595, 2014.

D. Kaur, and Y. Kaur, “Various Image Segmentation Techniques: A Review,” International Journal of Computer Science and Mobile Computing, vol. 3, no. 5, pp. 809-814, 2014.

L. K. Abood, R. A. Ali, and M. Maliki, “Automatic Brain Tumor Segmentation from MRI Images using Region growing Algorithm,” International Journal of Science and Research, vol. 6, no. 5, pp. 1592-1595, 2015.

D. J. Withey, and Z. J. Koles, “Medical Image Segmentation: Methods and Software,” in Proc. of IEEE 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, pp. 140-143, Hangzhou, China, 12-14 October 2007. doi:10.1109/nfsi-icfbi.2007.4387709.

W-F. Kuo, C-Y. Lin, and Y-N. Sun, “Brain MR Images Segmentation using Statistical Ratio: Mapping between Watershed and Competitive Hopfield Clustering Network Algorithms, Computer Methods and Programs in Biomedicine, vol. 91, pp. 191-198, 2008.

W.E. Phillips, R. P. Velthuizen, S. Phuphanich, L. O. Hall, L. P. Clarke, and M. L. Silbiger, “Application of Fuzzy C-Means Segmentation Technique for Tissue Differentiation in MR Images of a Hemorrhagic Glioblastoma Multiforme,” Magnetic Resonance Imaging, vol. 13, no. 2, pp. 277–290, 1995. doi:10.1016/0730-725x(94)00093-i.

I. Despotović, B. Goossens, and W. Philips, W. “MRI Segmentation of Human Brain: Challenges, Methods, and Applications,” Computational and Mathematical Methods in Medicine, vol. 2015, pp. 1-23, 2015. doi.org/10.1155/2015/450341.

S. A. Taghanak, Y. Zheng, S. K. Zhou, B. Georgescu, P. Sharma, D. Xu, D. Comaniciu, and G. Hamerneh, G. “Comb Loss: Handling Input and Output Imbalance in Multi-organ Segmentation,” pp. 1-9, 2018. Online [Available]: https://arxiv.org/abs/1805.02798.

P. E. Ricci, J. P. Karis, J. E. Heiserman, E. K. Fram, A. N. Bice, and B. P. Drayer, “Differentiating Recurrent Tumor from Radiation Necrosis: Time for Re-evaluation of Positron Emission Tomography? American Journal of Neuroradiology, vol. 19, no. 3, pp. 407-413, 1998.

D. S. Prabha, and J. S. Kumar, “Performance Evaluation of Image Segmentation Objective Methods,” Indian Journal of Science and Technology, vol. 9, no. 8, pp. 1-8, 2016.

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How to Cite
[1]
Popoola, J., Godson, T., Olasoji, Y. and Adu, M. 2019. Study on Capabilities of Different Segmentation Algorithms in Detecting and Reducing Brain Tumor Size in Magnetic Resonance Imaging for Effective Telemedicine Services. European Journal of Engineering Research and Science. 4, 2 (Feb. 2019), 23-29. DOI:https://doi.org/10.24018/ejers.2019.4.2.1142.