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A Review on MR Brain Image Segmentation Based on Different Techniques

Praveen Kumar Prajapati, Poonam Sharma


In past few years, the growth in Magnetic Resonance Imaging (MRI) provided a new way to detect and diagnose the brain related problems such as Alzheimer, schizophrenia and brain tumor. Many supervised and unsupervised techniques are available for image segmentation. In medical field supervised and unsupervised segmentation both are available but unsupervised is in more demand then supervised because it requires external assistance. Whereas unsupervised segmentation reflects better results. In this paper we present a survey on MRI segmentation using SOM (Self Organizing Map), SOM is based on unsupervised clustering technique. Also present a review of various researchers in the field of MRI Segmentation.

Keywords: Feature extraction, self organizing map, MR image segmentation, unsupervised segmentation


Cite this Article
Praveen Kumar Prajapati, Poonam Sharma. A Review on MR Brain Image Segmentation Based on Different Techniques. Journal of Operating Systems Development & Trends. 2015; 2(2): 9–14p.

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Kohonen T. Self Organizing Maps. Springer. 2001.

Haralick RM, Shanmugam K, Dinstein I. Textural Feature for Image Classification. IEEE Trans. Syst., Man Cybern. 1973; 3: 610–621p.

Li Y, Chi Z. MR Brain Image Segmentation Based on Self organizing Map Network. Int. J. Inf. Technol. 2005; 11.

Ortiz A, Gorriz JM, Ramirez J, et al. Two Fully Unsupervised Method for MR Brain Image Segmentation Using SOM Based Strategies. Appl. Soft Comput. 2013; 2668–2682p.

Guler I, Demirhan A. Interpretation of MR Images Using Self Organizing Maps and Knowledge Based Expert System. Digit. Signal Process. 2009; 668–677p.

Demirhan A, Guler I. Combining Stationary Wavelet Transform and Self Organizing Map for Brain MR Image Segmentation. Eng. Appl. Artif. Intel. 2011; 358–364p.

Ortiz A, Palacio AA, Gorriz JM, et al. Segmentation of Brain MRI Using SOM-FCM Based Method and 3D Stastical Descriptors. Comput. Math. Methods Med. 2013.

Jesna M, Kumudha Raimond. MR Brain Image Segmentation Based on Principle Component Analysis and Self Organizing Map. International Journal for Research Applied Science and Engineering Technology (IJRASET). 2014; 2(3).

Salas-Gonzalez D, Ramirez J, Gorriz JM, et al. Improving MRI Segmentation with Probabilistic GHSOM and Multiobjective Optimization. Neurocomputing. 2013; 118–131p.

Zhang J, Dai D. An Adaptive Spatial Method for Automatic Brain MR Image segmentation. Prog. Nat. Sci. 2009; 1373–1382p.


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