Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

Full Text (PDF, 804KB), PP.9-17

Views: 0 Downloads: 0


Amal Q. Alyahya 1,* Ahmad A. Abu-Shareha 1

1. Middle East University/Faculty of Information Technology, Amman, Jordan

* Corresponding author.


Received: 22 Oct. 2017 / Revised: 1 Jan. 2018 / Accepted: 8 Jan. 2018 / Published: 8 Mar. 2018

Index Terms

Renyi Entropy, Tsallis Entropy, Maximum Entropy, Minimum Entropy, Image Segmentation, Thresholding, Brain Tumor Detection


In this paper, the accuracy of the entropy-based thresholding approaches in brain tumor detection framework is investigated. Entropies are information gain methods that have been used for image thresholding with various application and different image modalities. The accuracy of the existing entropies for image thresholding has been studied in general domain (e.g.: natural images) and were not compared thoroughly. Thus, a framework for brain tumor segmentation is proposed with the core process of the image thresholding, in order to evaluate the accuracy of the entropies. Five entropies, namely, Renyi, Maximum, Minimum, Tsallis and Kapur are evaluated. Moreover, the aggregation of entropies was implemented and evaluated. The results show that the maximum entropy is the best for brain tumor detection. Moreover, it was shown that aggregation of entropies output does not enhance the result, however, it works as automatic selection of the best result and produces the results with the highest accuracy.

Cite This Paper

Amal Q. Alyahya, Ahmad A. Abu-Shareha, "Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.3, pp.9-17, 2018. DOI:10.5815/ijitcs.2018.03.02


[1]M.-N. Wu, C.-C. Lin, and C.-C. Chang, "Brain tumor detection using color-based k-means clustering segmentation," in Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007. Third International Conference on, 2007, vol. 2, pp. 245-250: IEEE.

[2]S. J. Sangwine and R. E. Horne, The colour image processing handbook. Springer Science & Business Media, 2012.

[3]Z. Ye, M. Wang, H. Jin, W. Liu, and X. Lai, "An Image Thresholding Approach Based on Ant Colony Optimization Algorithm Combined with Genetic Algorithm," International Journal of Intelligent Systems and Applications, vol. 7, no. 5, p. 8, 2015.

[4]A. A. A. Shareha, M. Rajeswari, and D. Ramachandram, "Textured Renyi Entropy for Image Thresholding," in Computer Graphics, Imaging and Visualisation, 2008. CGIV'08. Fifth International Conference on, 2008, pp. 185-192: IEEE.

[5]M. A. El-Sayed, "A new algorithm based entropic threshold for edge detection in images," arXiv preprint arXiv:1211.2500, 2012.

[6]L. Wang and Y. Chen, "Diversity based on entropy: A novel evaluation criterion in multi-objective optimization algorithm," International Journal of Intelligent Systems and Applications, vol. 4, no. 10, p. 113, 2012.

[7]D. Zhao, Y. Chan, and W. Gao, "Low-complexity and low-memory entropy coder for image compression," IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 10, pp. 1140-1145, 2001.

[8]M. Khalil, "Medical Image Steganography: Study of Medical Image Quality Degradation when Embedding Data in the Frequency Domain," International Journal of Computer Network and Information Security, vol. 9, no. 2, p. 22, 2017.

[9]M. Prastawa, E. Bullitt, and G. Gerig, "Simulation of brain tumors in MR images for evaluation of segmentation efficacy," Medical image analysis, vol. 13, no. 2, pp. 297-311, 2009.

[10]M. M. Ahmed and D. B. Mohamad, "Segmentation of brain MR images for tumor extraction by combining kmeans clustering and perona-malik anisotropic diffusion model," International Journal of Image Processing, vol. 2, no. 1, pp. 27-34, 2008.

[11]A. Mustaqeem, A. Javed, and T. Fatima, "An efficient brain tumor detection algorithm using watershed & thresholding based segmentation," International Journal of Image, Graphics and Signal Processing, vol. 4, no. 10, p. 34, 2012.

[12]S. Roy and S. K. Bandyopadhyay, "Detection and Quantification of Brain Tumor from MRI of Brain and it’s Symmetric Analysis," International Journal of Information and Communication Technology Research, vol. 2, no. 6, 2012.

[13]D. Cobzas, N. Birkbeck, M. Schmidt, M. Jagersand, and A. Murtha, "3D variational brain tumor segmentation using a high dimensional feature set," in Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 2007, pp. 1-8: IEEE.

[14]A. Kharrat, N. Benamrane, M. B. Messaoud, and M. Abid, "Detection of brain tumor in medical images," in Signals, Circuits and Systems (SCS), 2009 3rd International Conference on, 2009, pp. 1-6: IEEE.

[15]M. U. Akram and A. Usman, "Computer aided system for brain tumor detection and segmentation," in Computer Networks and Information Technology (ICCNIT), 2011 International Conference on, 2011, pp. 299-302: IEEE.

[16]S. Xavierarockiaraj, K. Nithya, and R. M. Devi, "Brain tumor detection using modified histogram thresholding-quadrant approach," Journal of Computer Applications (JCA), vol. 5, no. 1, pp. 21-25, 2012.

[17]S. Bauer, R. Wiest, L.-P. Nolte, and M. Reyes, "A survey of MRI-based medical image analysis for brain tumor studies," Physics in medicine and biology, vol. 58, no. 13, p. R97, 2013.

[18]A. K. Bhandari, V. K. Singh, A. Kumar, and G. K. Singh, "Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy," Expert Systems with Applications, vol. 41, no. 7, pp. 3538-3560, 2014.

[19]B. H. Menze et al., "The multimodal brain tumor image segmentation benchmark (BRATS)," IEEE transactions on medical imaging, vol. 34, no. 10, pp. 1993-2024, 2015.

[20]P. K. Sahoo and G. Arora, "Image thresholding using two-dimensional Tsallis–Havrda–Charvát entropy," Pattern recognition letters, vol. 27, no. 6, pp. 520-528, 2006.

[21]P.-Y. Yin, "Multilevel minimum cross entropy threshold selection based on particle swarm optimization," Applied mathematics and computation, vol. 184, no. 2, pp. 503-513, 2007.

[22]Y. Zhang and L. Wu, "Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach," Entropy, vol. 13, no. 4, pp. 841-859, 2011.

[23]M. A. El-Sayed, S. Abdel-Khalek, and E. Abdel-Aziz, "Study of efficient technique based on 2D tsallis entropy for image thresholding," arXiv preprint arXiv:1401.5098, 2014.

[24]C.-I. Chang, K. Chen, J. Wang, and M. L. Althouse, "A relative entropy-based approach to image thresholding," Pattern recognition, vol. 27, no. 9, pp. 1275-1289, 1994.

[25]S. J. Phillips, R. P. Anderson, and R. E. Schapire, "Maximum entropy modeling of species geographic distributions," Ecological modelling, vol. 190, no. 3, pp. 231-259, 2006.