Brain Tumor Detection Using Hard and Soft Computing Techniques

Medical image segmentation has great significance in the research in the research field of image segmentation. It is the one way to take out important information from medical images. There are many soft computing and hard computing techniques are there which are used for segment the interested area in medical images. Both has its own advantages and disadvantages. In this paper we provide a new approach to medical image segmentation. we use three approaches with the combination of each other. That approaches are K-mean, Fuzzy C-mean and genetic algorithm. So we divide this paper in three phases. In first phase we apply the K-mean algorithm on the image which segment the interested are but this algorithm not gave the satisfactory result. So on the result of this algorithm we apply FCM after this optimization is done with the help of GA. The experimental result is calculated on the basis of classification accuracy. The result is 90.3%.

[1]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.

[2]  D. Louis Collins,et al.  Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images , 1995, IEEE Trans. Medical Imaging.

[3]  J. Bezdek.  Cluster Validity with Fuzzy Sets , 1973 .

[4]  Ayman A. Aly,et al.  Edge Detection in Digital Images Using Fuzzy Logic Technique , 2009 .

[5]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[6]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[7]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[8]  Ponnada A. Narayana,et al.  Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images , 2008, Comput. Medical Imaging Graph..

[9]  Nitesh Sinha,et al.  A fully automated algorithm under modified FCM framework for improved brain MR image segmentation. , 2009, Magnetic resonance imaging.

[10]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[11]  Yogesh Rathi,et al.  A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Sachin C. Patwardhan,et al.  A possibilistic clustering approach to novel fault detection and isolation , 2006 .

[13]  Colin Studholme,et al.  Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets , 2005, Comput. Vis. Image Underst..

[14]  Driss Aboutajdine,et al.  Textural feature selection by joint mutual information based on Gaussian mixture model for multispectral image classification , 2010, Pattern Recognit. Lett..

[15]  William E. Higgins,et al.  Symmetric region growing , 2003, IEEE Trans. Image Process..

[16]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .