Fuzzy Deformable Based Fusion Approach for Tumor Segmentation and Classification in Brain MRI Images

  • Authors

    • Sharan Kumar
    • Dr. D.Jayadevappa
    • Mamata V Shetty
    2018-09-27
    https://doi.org/10.14419/ijet.v7i4.7.20538
  • MRI image, Tumor region, segmentation, classification, BRATS database.

  • Abstract

    In recent years, the automatic identification and classification of tumor regions have gained more interest due to accuracy and reduced time complexity. One of the important strategies in tumor identification is segmenting the image as tumor and nontumor region, and this helps the researchers more significantly, as the MRI image comes in different modalities. This work introduces novel optimization based strategy for segmenting and classifying the image. Initially, the MRI images in the database are subjected to pre-processing and given to the segmentation process. For segmentation, this work utilizes the deformable model, and Fuzzy C Means (FCM) algorithm and the resultant segmented images are hybridized through proposed Dolphin based Sine Cosine Algorithm, preferred to be Dolphin-SCA. After segmentation, the tumor and non tumor-related features are extracted using the power LBP operator. The extracted features are subjected to Fuzzy Naive Bayes classifier for the classification, and finally, the classifier finds the suitable tumor class labels. Here, the entire experimentation is done by taking the MRI images from the BRATS database, and evaluated based on sensitivity, specificity, accuracy and ROC metrics. The simulation results reveal the dominance of proposed scheme over other comparative models, and the proposed scheme achieved 95.249% accuracy.  

     

     

  • References

    1. [1] Zhengwang Wu, Yanrong Guo, Sang Hyun Park, Yaozong Gao, Pei Dong, Seong-Whan Lee, and Dinggang Shen, "Robust Brain ROI Segmentation by Deformation Regression and Deformable Shape Model," Medical Image Analysis, vol. 43, pp. 198-213, 2017.

      [2] Shoaib Amin Banday, and Ajaz Hussain Mir, "Statistical textural feature and deformable model based brain tumor segmentation and volume estimation," Multimedia Tools Application, vol. 76, no. 3, pp. 3809-3828, 2017.

      [3] Hala Ali, Mohammed Elmogy, Eman El-Daydamony, and Ahmed Atwan, "Multi-Resolution MRI Brain Image Segmentation Based On Morphological Pyramid and Fuzzy C-Mean Clustering," Arabian Journal for Science and Engineering, vol. 40, no. 11, pp. 3173-3185, November 2015.

      [4] Ali Ahmadvand, Mohammad Reza Daliri, and Sayyed Mohammadreza Zahiri, "Segmentation of brain MR images using a proper combination of DCS based method with MRF," Multimedia Tools and Applications, pp. 1-18, 2017.

      [5] Sriparna Saha, Abhay Kumar Alok, and Asif Ekbal, "Brain Image Segmentation using Semi-Supervised Clustering," Expert Systems with Applications, vol. 52, pp. 50-63, 2016.

      [6] Pim Moeskops, Max A. Viergever, Adriënne M. Mendrik, Linda S. de Vries, Manon JNL Benders, and Ivana Isgum, "Automatic segmentation of MR brain images with a convolutional neural network," IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1252-1261, 2016.

      [7] Abdenour Mekhmoukh, and Karim Mokrani, "Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation," Computer methods and programs in biomedicine, vol. 122, no. 2, pp. 266-281, 2015.

      [8] Jeetashree Aparajeeta, Pradipta Kumar Nanda, and Niva Das, "Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image," Applied Soft Computing, vol. 41, pp. 104-119, 2016.

      [9] Ali Ahmadvand, Mohammad Sharififar, and Mohammad Reza Daliri, "Supervised segmentation of MRI brain images using combination of multiple classifiers," Australasian physical and engineering sciences in medicine, vol. 38, no. 2, pp. 241-253, 2015.

      [10] G. Vishnuvarthanan, M. Pallikonda Rajasekaran, P. Subbaraj, and Anitha Vishnuvarthanan, "An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images," Applied Soft Computing, vol. 38, pp. 190-212, 2015.

      [11] Lin G-C, Wang WJ, Kang CC, Wang CM, "Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing," Magnetic Resonance Imaging, vol. 30, no. 2, pp. 230–246, 2012.

      [12] Seyedali Mirjalili, "SCA: A Sine Cosine Algorithm for solving optimization problems", Knowledge-Based Systems, Vol. 96, pp. 120-133, March 2016.

      [13] Gautam M. Borkar, A. R. Mahajan, "A secure and trust based on-demand multipath routing scheme for self-organized mobile ad-hoc networks", Wireless Networks, Vol. 23, No. 8, pp. 2455–2472, November 2017.

      [14] Bogdan Smolka, Karolina Nurzynska, "Power LBP: A Novel Texture Operator for Smiling and Neutral Facial Display Classification", in proceeding of Computer Science, Vol. 51, pp. 1555-1564, 2015.

      [15] Anders Bjorholm Dahl, and Vedrana Andersen Dahl., "Dictionary snakes," in proceedings of 22nd IEEE International Conference on Pattern Recognition (ICPR), pp. 142-147, 2014.

      [16] Bjoern H. Menze et al., "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging, Vol. PP, no. 99, pp.1, 2014.

      [17] Hans-Peter Storr, Y. Xu, and J. Choi, "A compact fuzzy extension of the Naive Bayesian classification algorithm," In Proceedings on technology/VJFuzzy, pp. 172-177, 2002.

      [18] C. Li, D.B. Goldgof, L.O. Hall, "Knowledge-based classification and tissue labeling of MR images of human brain," IEEE Transaction Medical Imaging, vol. 12, no. 4, pp. 740–750, 1993.

      [19] S. Krinidis, V. Chatzis, "A robust fuzzy local information C-means clustering algorithm," IEEE Transaction on Image Processing, vol. 19, no. 5, pp. 1328–1337, May 2010.

      [20] Madhulatha, T.S, "An overview on clustering methods," IOSR Journal Engineering, vol. 2, no. 4, pp. 719–725, 2012.

      [21] Acharya, J.Gadhiya, S., and Raviya, K., "Segmentation techniques for image analysis: a review," International Journal Computer Science Management Research, vol. 2, no. 1, pp. 1218–1221, 2013.

      [22] Muhammad Ali Qadar, and Yan Zhaowen, "Brain Tumor Segmentation: A Comparative Analysis," arXiv preprint arXiv:1503.02466, 2015.

      [23] Mahendran R, and Dekson DE, "A Survey of Brain Tumour Segmentation and Classification for fMRI Data", Journal of Chemical and Pharmaceutical Sciences, vol. 9, no. 4, pp. 2173-2179, 2016.

      [24] BRATSdatabase,http://www2.imm.dtu.dk/projects/BRATS2012/data.html, accessed on April 2018.

      [25] Hari Babu Nandpuru, S. S. Salankar and V. R. Bora, "MRI brain cancer classification using Support Vector Machine," in proceedings of IEEE Students' Conference on Electrical, Electronics and Computer Science, Bhopal, pp. 1-6, 2014.

      [26] M. Havaei, P. Jodoin and H. Larochelle, "Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification," in proceedings of 22nd International Conference on Pattern Recognition, Stockholm, pp. 556-561, 2014.

  • Downloads

  • How to Cite

    Kumar, S., D.Jayadevappa, D., & V Shetty, M. (2018). Fuzzy Deformable Based Fusion Approach for Tumor Segmentation and Classification in Brain MRI Images. International Journal of Engineering & Technology, 7(4.7), 171-179. https://doi.org/10.14419/ijet.v7i4.7.20538

    Received date: 2018-09-29

    Accepted date: 2018-09-29

    Published date: 2018-09-27