Discriminating Brain Tumor Segmentation Algorithms & Its Area Calculation
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Bhupendra T. Jagatap, Prof. Sanjeev N. Jain
|DOI : 10.14445/22315381/IJETT-V54P220|
Bhupendra T. Jagatap, Prof. Sanjeev N. Jain "Discriminating Brain Tumor Segmentation Algorithms & Its Area Calculation", International Journal of Engineering Trends and Technology (IJETT), V54(2),141-146 December 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Image Segmentation is the process of partitioning a significant information about the image could be retaken and various analysis could be performed on that segmented image. Brain is the most important and vibrant organ of the human body. The control and coordination of all the other vibrant structure is carried out by the brain. The tumor is made by the uncontrolled multiplication of cell division. Many techniques were developed to detect and segment the brain tumor using multiple segmentation algorithms such as 1) watershed algorithm, 2) k-means clustering, 3) Fuzzy c-means clustering is carried out. This is where the division of the tumor is carried out and the centralization, the perimeter and field is the efficient algorithm that divides its features as are calculated from tumors. To detect brain tumors, scanned MRI images are given as input. The work done here helps to locate the tumor in the medical field and help greatness the patient to the treatment plan. Besides, it also reduces the time for analysis. At the end of the process the tumor is separated from the MR image, its precise position and some features also determined. It is observed that the experimental results of the thresholding and morphological process is very promising in the field of brain tumor segmentation compare with clustering methods.
 P. S. N. J. Bhagyashri G. Patil, "Cancer Cells Detection Using Digital Image Processing Methods," International Journal of Latest Trends in Engineering and Technology (IJLTET), vol. 3, no. 4, pp. 45-49, March 2014.
 R. Mithali , "Brain Tumor Detection and Identification Using K-Means Clustering Technique," Special Issue Published in Int. Jnl. Of Advanced Networking and Applications (IJANA), pp. 14-18, March 2015.
 D. M. S. S. A. D. A. P. R. T. Kshitij Bhagwat, "Comparative Study of Brain Tumour Detection Using K means, Fuzzy C Means and Hierarchical Clustering Algorithms," International Journal of Scientific & Engineering Research, pp. 626-632, (ISSN) June-2013.
 H. V. K. Leela G A, "Morphological Approach for the Detection of Brain Tumour and Cancer Cells," (Quest Journals) Journal of Electronics and Communication Engineering Research, pp. 7-12, 2014.
 A. B. M. S. B. Meenakshi S R, "Morphological Image Processing Approach Using K-Means Clustering for Detection of Tumor in Brain," International Journal of Science and Research, pp. 24-29, (IJSR) August 2014.
 C. S. S. M. Rohini Paul Joseph, "BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSING," International Journal of Research in Engineering and Technology, pp. 1-5, (IJRET) Mar-2014.
 J. J.Vijay, "An Efficient Brain Tumor Detection Methodology Using K-MeansClusteringAlgorithm," International conference on Communication and Signal Processing, pp. 653-657, April 2013.
 R. D.SELVARAJ, "MRI BRAIN IMAGE SEGMENTATION TECHNIQUES - A REVIEW," Indian Journal of Computer Science and Engineering, pp. 364-381, (IJCSE) Oct-Nov 2013.
 R. T. a. S. S. K. Sailakshmi Parvathi, "Extraction of Tumor and Cancer Cells of Brain MRI Images by using different Morphological Operations," International Journal of Emerging Trends in Electrical and Electronics (IJETEE), vol. 4, no. 1, pp. 77-79, JUNE 2013.
 J.selvakumar, "Brain Tumor Segmentation and Its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm," IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM), pp. 186-190, March 2012.
 J. P. a. K. Doshi, "A Study of Segmentation Methods for Detection of Tumor in Brain MRI," Advance in Electronic and Electric Engineering, pp. 279-284, (ISSN) 2014.
 D. Kesavaraja, "ADVANCED CLUSTER BASED IMAGE SEGMENTATION," ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, vol. 2, no. 2, pp. 307-317, NOVEMBER 2011.
 S. M. Alan Jose, "Brain Tumor Segmentation Using K-Means Clustering And Fuzzy C-Means Algorithms And Its Area Calculation," International Journal of Innovative Research in Computer and Communication Engineering, pp. 3496-3501, (IJIRCCE) March 2014.
 P. K. Yogita Sharma, "Detection and Extraction of Brain Tumor from MRI Images Using K-Means Clustering and Watershed Algorithms," International Journal of Computer Science Trends and Technology (IJCST), pp. 32-38, (IJCST) Mar-Apr 2015.
Magnetic Resonance Image (MRI), Preprocessing and Segmentation (K-means, Fuzzy c-means, Watershed algorithm), Parameter analysis.