Implementation of Brain Tumor Segmentation in brain MR Images using K-Means Clustering and Fuzzy C-Means Algorithm

Ms. Pritee Gupta, Ms Mrinalini Shringirishi, Dr.yashpal Singh

Abstract


This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this work uses computer aided method for segmentation (detection) of brain tumor based on the k.means and fuzzy c-means algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis.


Keywords


Abnormalities; Magnetic Resonance Imaging (MRI); Brain tumor; Pre-processing; K-means; fuzzy c-means; Thresholding

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References


[I]. M.H. Fazel Zarandia, M. Zarinbala, M. Izadi b(2011), "Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach," Applied soft computing 11,285-294

. S.Mary Praveena ,Dr.I1aVennila , June 2010, "Optimization Fusion Approach for [mage Segmentation Using K-Means Algorithm," International Journal of Computer Applications (0975 - 8887) Volume 2 - NO.7.

. M. Masroor Ahmed & Dzulkifli Bin Mohammad(2010), "Segmentation of Brain MR [mages for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model," International Journal of Image Processing, Volume (2) : Issue(I) 27

. Manisha Bhagwatl, R.K.Krishna& V.E.Pise July-December 2010, "Image Segmentation by Improved Watershed Transformation in Programming Environment MATLAB," International Journal of Computer Science & Communication Vol. I, No. 2, pp. 17/-/74

. Tse-Wei Chen , Yi-Ling Chen , Shao-Yi Chien (2010), "Fast Image Segmentation Based on K-Means Clustering with Histograms in HSV Color Space," Journal of Scientific Research ISSN I452-2I6X Vol. 44 No.2, pp.337-35 I

. Anil Z chaitade ( 2010 ). “colour based imagesegmentation using k-means clustering.” International journal of computer science and Technology Vol. 2(10),5319-5325

. S.Zulaikha BeeviM, Mohamad Sathik (2010),” An Effective Approach for segmentation of MRI images combining spatial Information with fuzzy c-means clustering,” European Journal of scientific Research, ISSN 1450-216X Vol.41 no.3 pp. 437-451

. K.S. Ravichandran and 2B Ananthi (2009) , “ color skin segmentation using k-means cluster,” International Journal of computational and applied mathematics ISSN 1819 Volume 4 Number 2, pp. 153-157

. A.Suman Tatiraju july-2008,” image segmentation using k-means clustering, EM and Normalized cuts,” Symposium of Discrete Algorithms


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