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Comparative Studies on Brain Tumor Extraction of MR Images
Brain image segmentation and analysis of different parts of brain is very important research issue. In this paper we proposed a method for segmenting brain tumors by combining Fuzzy C-Means thresholding with watershed segmentation method. We also present a comparative study of different existing segmentation approaches like global thresholding using Otsu method, histogram thresholding and watershed method with addition of some pre- and post-processing methods. Among all the methods Fuzzy C-Means clustering with watershed method gives the best result. These methods allow the segmentation of tumor tissue with accuracy and efficiency as compared to manual segmentation. The tumor is extracted from the brain image and its exact position is also determined. This process calculates the area of tumor for each of the algorithm used. A performance wise comparative study using four number of defected 2D MRI brain images are considered. Several cases under hazy and bad imaging conditions are also studied and good result is obtained.
Keywords
Edge Detection Method, Fuzzy C-Means Clustering, Thresholding Method, Tumor Extraction, Watershed Method.
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- Gonzalez RC, Woods RE. Digital image processing. Ed. 3, Pearson, Twelfth Impression. 2008.
- Kabade RS, Gaikwad MS. Segmentation of brain tumor and its area calculation in Brain MR Images using K-mean clustering and Fuzzy C-Mean algorithm. IJCSET;2013 May; 4(5):524–31. ISSN: 2229-3345.
- Christe SA, Malathy K, Kandaswamy A. Improved hybrid segmentation of brain MRI tissue and tumor using statistical features. ICTACT. 2010 Aug; 1:43–9.
- Rakesh M, Ravi T. Image segmentation and detection of tumor objects in MR brain images using Fuzzy C-Means algorithm. IJERA. 2012 May-Jun; 2(3):2088–94.
- Christe SA, Malathy K, Kandaswamy A. An enhanced implementation of brain tumor detection using segmentation based on soft computing. ICTACT Journal on Image and Video Processing. 2010 Aug; 01.
- Bindu CH, Satya Prasad K.An efficient medical image segmentation using conventional OTSU method. International Journal of Advanced Science and Technology. 2012 Jan; 38:67–74.
- Singh S, Soni M, Mishra RS. Segmentation of underwater objects using CLAHE enhancement and thresholding with 3-class Fuzzy C-Means Clustering. IJETAE. 2014 Apr; 4(4):798–805.
- Karuna M, Joshi A. Automatic detection and severity analysis of brain tumors using GUI in Matlab. IJRET. 2013 Oct; 02(10):586–94. ISSN: 23217308.
- Kowar MK, Yadav S. Brain tumor detection and segmentation using histogram thresholding. IJEAT. 2012 Apr; 1(4):16–20.
- Dahab DA, Ghoniemy SSA, Selim GM. Automated brain tumor detection and identification using image processing and probabilistic neural network techniques. International Journal of Image Processing and Visual communication. 2012 Oct; 1(2):1–8.
- Kandwal R, Kumar A. An automated system for brain tumor detection and segmentation. International Journal of Advanced Research in Computer Science and Software Engineering. 2004 Mar; 4(3):97–100.
- Kanimozhi M, Bindu CHH, Brain MR image segmentation using self organizing map. International Journal of Advanced Research in Computer and Communication Engineering. 2013 Oct; 2(10):3968–73.
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