Automatic Brain Tumor Segmentation byVariational Minimax Optimization Technique

The brain tumors are the mass of undifferentiated cells which undergoes uncontrolled proliferation of cells in the brain. Segmentation of these tumors is more difficult than natural. Because their functional sensitivity is higher than other images. Many different algorithms have been proposed for segmentation of these type of tumors in brain images. In this paper, we propose an approach in order to improve efficiency of the brain tumor segmentation through the minimax optimization that applies to the thresholding of the MRI brain image to segment tumor. The tumor segmentation is performed by the implementation of an optimistic technique called variational minimax optimization. The proposed system uses search of optimum threshold with an iterative line search technique with faster execution time of 15 seconds.

[1]  Fei Liu,et al.  Active surface model-based adaptive thresholding algorithm by repulsive external force , 2003, J. Electronic Imaging.

[2]  Bahari Belaton,et al.  A K-means Based Generic Segmentation System , 2009, 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization.

[3]  T. Arivoli,et al.  Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).

[4]  I.E. Abdou,et al.  Quantitative design and evaluation of enhancement/thresholding edge detectors , 1979, Proceedings of the IEEE.

[5]  T. Kathirvalavakumar,et al.  Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF , 2013 .

[6]  M. Karnan,et al.  Improved implementation of brain MRI image segmentation using Ant Colony System , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[7]  Jian-Wei Ma,et al.  An improved artificial fish-swarm algorithm and its application in feed-forward neural networks , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  Rahul Ramachandran,et al.  Comparing different thresholding algorithms for segmenting auroras , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[9]  M. M. Ahmed,et al.  Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model , 2008 .