The week's pick
Random Articles
Reseach Article
Detection and Segmentation of Brain Tumor by Thresholding and Bounding-Box using K-Means as a Seed
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 173 - Number 5 |
| Year of Publication: 2017 |
| Authors: Chaitra G., Sarika Tale |
10.5120/ijca2017915243
|
Chaitra G., Sarika Tale . Detection and Segmentation of Brain Tumor by Thresholding and Bounding-Box using K-Means as a Seed. International Journal of Computer Applications. 173, 5 ( Sep 2017), 33-35. DOI=10.5120/ijca2017915243
Abstract
Brain tumor is a mass of abnormal cells replicating in an uncontrolled manner. It affects the growth and function of normal cells in brain and occupies the space in brain. It causes interruption of brain cell function and cause damage to life. Accurate detection and segmentation of brain tumor is challenging task due to several reasons like complex brain structure, increasing data flow, inhomogeneity etc. This paper presents a novel method to segment brain tumor in T1-weighted MRI images by employing K-means followed by a thresholding technique and bounding box method by combing these different techniques accuracy of detecting the tumor portion can be increased and finally all the features of the detected tumor such as centroid, solidity, perimeter, area and segmented area and extent are extracted.
References
- S. Aswathy, G. Glan Deva Dhas, and S. Kumar A survey on detection of brain tumor from MRI brain images,” in Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on, July 2014, pp. 871–877.
- S.Bauer, R. Wiest, L.-P. Nolte, and M. Reyes, “A survey of MRI-based medical image analysis for brain tumor studies,” Physics in Medicine and Biology, vol. 58, no. 13, pp. 97 – 129, 2013.
- Swapnil R. Telrandhe1 Amit Pimpalkar Ankita Kendhe “Detection of Brain Tumor from MRI images by using Segmentation” 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (WCFTR’16)
- Pooja Thakur, Dr. Kuldip Pahwa and Dr. Rajat Gupta ]“Brain Tumor Detection, Segmentation Using Watershed Segmentation And Morphological operation” international Journal of Computer Applications (0975 – 8887) Volume 58– No.16, November 2012
- S.Charutha and M. Jayashree, “An efficient brain tumor detection by texture based region growing and cellular automata edge detection,” in Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on, July 2014, pp. 1193–1199.
- Pratibha Sharma ,Manoj Diwakar ,Sangam Choudhary “Application of Edge Detection for Brain Tumor Detection” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume4, Issue 6, June 2015
- R. Preetha and G. Suresh, “Performance analysis of fuzzy c means algorithm in automated detection of brain tumor,” in Computing and Communication Technologies (WCCCT), 2014 World Congress on, Feb 2014, pp.30–33.
- Alan Jose1, S. Ravi, M. Sambath “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. Vol. 2, Issue 3, March 2014
- Benson C. C.1, Lajish V. L, Kumar Rajamani “Brain Tumor Extraction from MRI Brain Images Using Marker Based Watershed Algorithm”3189 78-1-4799-8792-4/15/$31.00 c 2015 IEEE.
- J. Vijay and J. Subhashini, “An efficient brain tumor detection methodology using k-means clustering algorithm in Communications and Signal Processing (ICCSP), 2013 International Conference on, April 2013, pp. 653–657.
- Natarajan P and sushmitha G ]“Brain tumor detection using threshold operation” international journal of pharmacy and technology
- T. E. M. A. Bianchi A, Miller JV, “Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests,” in Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro IEEE International Symposium on Biomedical Imaging., 2013, pp. 748 – 751.
Index Terms
Keywords