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Performance Analysis for Efficient Brain Tumor Segmentation by using Clustering Algorithm
Objective: Normally MRI scan or CT helps to view the biology of brain. The segmentation methods are used to identify the tumor size and location. Methods/Analysis: Some of the segmentation methods are the Histogram-based segmentation and the Region-based segmentation (e.g.: Edge Detection method) which have the drawbacks in detection of size of the tumor and region. We are using the clustering based segmentation algorithms in this project. The run time and efficiency are the parameters used for comparison. Findings: These clustering algorithms like K-means, Fuzzy C and Pillar means are compared to each other for better performance by calculating the run time and efficiency of algorithms. This attempt improves the efficiency and computing time. Application/Improvements: It may help pathologists to identify the exact size and region easily
Fuzzy C, K-means, Pathologists, Pillar means, Segmentation
- Janani V, Meena P. Image Segmentation for Tumor Detection using Fuzzy Inference System, Int. J. Comput.Sci. Mobile Comput. (IJCSMC). 2013; 2(5):244–8.
- Aslam HA, Ramashri T, Ahsan MIA. A New Approach for Image Segmentation for Brain Tumor Detection using Pillar- Kmeans Algorithm, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE). 2013; 2(3):1429−36.
- Rohit M, Kabade S, Gaikwad MS. Segmentation of Brain Tumour and its Area Calculation in Brain MRI Images using K-mean Clustering and Fuzzy C-mean Algorithm, Int. J. Comput. Sci. Eng. Technol. (IJCSET). 2013; 4(5):524–31.
- Harati V, Khayati R, Farzan A. Fully Automated Tumor Segmentation Based on an Improved Fuzzy Connectedness Algorithm in Brain MR Images, Elsevier Ltd. 2011 May; 7:483−92.
- Baraiya N, Modi H. Comparative Study of Different Methods for Brain Tumor Extraction from MRI Images using Image Processing, Indian Journal of Science and Technology. 2016 Jan; 9(4):1−5. Crossref6. Salt-and-Pepper Noise. https://en.wikipedia.org/wiki/Saltandpepper_noise. Date accessed: 22/09/2015.
- Sheshasayee A, Sharmila P. Comparative Study of Fuzzy C Means and K Means Algorithm for Requirements Clustering, Indian Journal of Science and Technology. 2014 Jan, 7(6):1−5.
- Pavani M, Balaji S. An Approach for Segmentation of Medical Images using Pillar K-means Algorithm, International Journal of Computer Trends and Technology (IJCTT). 2015; 4(4):636−41.
- Saini R, Dutta M. Image Segmentation for Uneven Lighting Images using Adaptive Thresholding and Dynamic Window Based on Incremental Window Growing Approach, Int. J. Comput. Appl. 2012; 56(13):31–6. Crossref
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