Automated MRI (Magnetic resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this paper, a statistical structure analysis based tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. The basic concept is that local textures in the images can reveal the typical ‘regularities’ of biological structures. Thus, textural features have been extracted using co-occurrence matrix approach. By the analysis of level of correlation we can reduce the number of features to the only significant component .An artificial neural network and fuzzy c-means are used for classification. This approach is designed to investigate the differences of texture features among macroscopic lesion white matter (LWM), normal appearing white matter (NAWM) in magnetic resonance images (MRI) from patients with tumor and normal white matter (NWM).
Joshi, Jayashri and Phadke, .A. C.
"Feature Extraction and Texture Classification in MRI,"
International Journal of Computer and Communication Technology: Vol. 3
, Article 1.
Available at: https://www.interscience.in/ijcct/vol3/iss2/1