Preprint Article Version 1 This version is not peer-reviewed

Designing a Feature Vector for Statistical Texture Analysis of Brain Tumor

Version 1 : Received: 14 June 2019 / Approved: 18 June 2019 / Online: 18 June 2019 (05:36:29 CEST)

How to cite: Prithiviraj, K.; Prabakaran, S. Designing a Feature Vector for Statistical Texture Analysis of Brain Tumor. Preprints 2019, 2019060166 (doi: 10.20944/preprints201906.0166.v1). Prithiviraj, K.; Prabakaran, S. Designing a Feature Vector for Statistical Texture Analysis of Brain Tumor. Preprints 2019, 2019060166 (doi: 10.20944/preprints201906.0166.v1).

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.

Subject Areas

MRI image; Texture Features; GLCM

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