International Journal of Recent Trends in Engineering & Research

online ISSN


Publication Date : 15/05/2020

DOI : 10.23883/IJRTER.CONF.20200315.033.SNADA

Author(s) :

Mr.K.Mahadevan , S.parkavi.

Conference Name :
2nd International Conference on New Scientific Creations in Engineering and Technology - 2020

Abstract :

Brain MRI segmentation is an essential task in many clinical applications because it influences the outcome of the entire analysis. This is because different processing steps rely on accurate segmentation of anatomical regions. Enormous progress in accessing brain injury and exploring brain anatomy has been made using magnetic resonance imaging (MRI). The advances in brain MR imaging have also provided large amount of data with an increasingly high level of quality. This manual analysis is often time-consuming and prone to errors due to various inter- or intra operator variability studies. These difficulties in brain MRI data analysis required inventions in computerized methods to improve disease diagnosis and testing. The images produced by MRI are high in tissue contrast and have fewer artifacts. It has several advantages over other imaging techniques, providing high contrast between soft tissues. However, the amount of data is far too much for manual analysis, which has been one of the biggest obstacles in the effective use of MRI. The detection of tumor requires several processes on MRI images which includes image pre-processing, feature extraction, image segmentation and classification. In this project, we can implement various image segmentation methods such as K-means clustering, Fuzzy K- means clustering and Adaptive fuzzy K means clustering with various distances measures that includes Euclidean distance. The final classification process such as deep learning approach concludes that a person is diseased or not. Although numerous efforts and promising results are obtained in medical

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