Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method

Segmentation is an important aspect of medical image processing. For improving the accuracy in the detection of tumour and improving the speed of execution in segmentation, a new genetic-based genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method with back propagation neural network (BPNN) is proposed and presented in this paper. The proposed system consists of four steps: pre-processing, segmentation, feature extraction and classification. The GFSMRG method and its components, feature extraction and classification are explained in detail. The performance analysis of the GFSMRG method with respect to accuracy and time complexity are also discussed. The performance of this method has been validated both quantitatively and qualitatively by using the performance metrics such as Similarity Index, Jaccard Index, Sensitivity, Specificity and Accuracy.

[1]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[2]  Wang Zhiping,et al.  An Improved FCM algorithm for Color Image Segmentation , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[3]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[6]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[7]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[8]  Eric A. Wan,et al.  Neural network classification: a Bayesian interpretation , 1990, IEEE Trans. Neural Networks.

[9]  Mazani Manaf,et al.  Seed-Based Region Growing (SBRG) vs Adaptive Network-Based Inference System (ANFIS) vs Fuzzyc-Means (FCM): Brain Abnormalities Segmentation , 2010 .

[10]  Ali N. Akansu,et al.  A class of fast Gaussian binomial filters for speech and image processing , 1991, IEEE Trans. Signal Process..

[11]  J. Sahambi,et al.  Wavelet domain non-linear filtering for MRI denoising. , 2010, Magnetic resonance imaging.

[12]  George Baciu,et al.  Detecting, Grouping, and Structure Inference for Invariant Repetitive Patterns in Images , 2013, IEEE Transactions on Image Processing.

[13]  D. Selvathi,et al.  Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation , 2009, 2009 IEEE International Advance Computing Conference.

[14]  Kalyanmoy Deb,et al.  Self-Adaptive Genetic Algorithms with Simulated Binary Crossover , 2001, Evolutionary Computation.

[15]  Ahmed S. Ghiduk,et al.  A novel approach based on genetic algorithms and region growing for magnetic resonance image (MRI) segmentation , 2013, Comput. Sci. Inf. Syst..

[16]  K. Thanushkodi,et al.  Tracking Algorithm For De-Noishing of MR Brain Images , 2009 .

[17]  Ujjwal Maulik,et al.  Medical Image Segmentation Using Genetic Algorithms , 2009, IEEE Transactions on Information Technology in Biomedicine.

[18]  F. Albregtsen.  Statistical Texture Measures Computed from Gray Level Coocurrence Matrices , 2008 .

[19]  C. Chellamuthu,et al.  Detection of brain tumour from MRI image using modified region growing and neural network , 2013 .

[20]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[21]  Liang Cheng,et al.  Robust Affine Invariant Feature Extraction for Image Matching , 2008, IEEE Geoscience and Remote Sensing Letters.

[22]  Jing Zhang,et al.  Image Segmentation Method Based on Improved Genetic Algorithm and Fuzzy Clustering , 2010 .