Designing a Feature Vector for Statistical Texture Analysis of Brain Tumor
P Kavitha1, S Prabakaran2
1P.Kavitha, Department of Information Technology, Bharath Institute of Higher Education and Research, Channai (Tamil Nadu), India.
2S.Prabakaran, Department of Computer Science and Engineering, SRM University, Kattankulathur (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1228-1230 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7087068519©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this paper, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o , 45o , 90o and 135o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed a different types of images using past years. So the algorithm proposed statistical texture features are calculated for iterative image segmentation. These results show that MRI image can be implemented in a system of brain cancer detection.
Keywords: MRI Image, Texture Features, GLCM
Scope of the Article: Image analysis and Processing