A Hybrid Approach for MRI Based Statistical Feature Extraction to Detect Brain Tumor
Journal Title: IOSR journal of VLSI and Signal Processing - Year 2018, Vol 8, Issue 2
Abstract
Undesired abnormal growth of cells in the brain is a serious neurological problem which is known as brain tumor. Early detection and diagnosis of tumor from an MRI image is a primary task. It is tedious and time consuming task which requires expertise of radiologists. To overcome this limitation, an automated state-of-the-art system is required which can give an estimate about whether the brain MRI image has tumor or not. The literature reveals that feature extraction is a significant step for detection of tumor. In this study, feature extraction is incorporated with significant feature identification so as to minimize the time required for processing the data and to improve the accuracy. This paper presents a technique for statistical feature extraction by hybridization of analytical and algorithmic means and Gray Level Cooccurrence Matrix (GLCM) properties. This gives a contribution in feature extraction which is fundamental for any decision algorithm to give better and accurate results. The experimental results have been evaluated for axial, coronal and sagittal views of brain MRI image. The results show that the proposed method is effective for extracting the significant features. Index Terms—Brain tumor, Magnetic resonance imaging (MRI), feature extraction
Authors and Affiliations
Narbada Jhalwa1 ,, Payal Shah2 ,, Rajendra Sutar3
Keywords
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