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Analysis of Segmentation Algorithms for Detection of Anomalies in MR Brain Images

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 656)

Abstract

The role of segmentation is vital in image processing for the extraction of region of interest. In the perspective of medical images, the region of interest corresponds to anatomical organs or anomalies such as tumor, cyst. This work analyses various algorithms for the analysis of MR brain images. The clustering-based segmentation technique was found to be efficient and for the validation of results, performance metrics like Jaccard Index, rand index, false positive, and false negative are used. The segmentation algorithms are tested on real-time MR brain and brain web database images. The algorithms are developed in Matlab 2010a and the goal of this research work is to guide the researchers for choosing appropriate algorithm for the analysis of MR brain images.

Keywords

  • Clustering
  • Region of interest
  • Thresholding
  • Region growing

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Acknowledgements

The authors would like to acknowledge the support provided by Nanyang Technologıcal Unıversıty under NTU Ref: RCA-17/334 for providing the medical images and supporting us in the preparation of the manuscript.

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Correspondence to S. N. Kumar .

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Lenin Fred, A., Kumar, S.N., Padmanabhan, P., Gulyas, B., Kumar, H.A. (2020). Analysis of Segmentation Algorithms for Detection of Anomalies in MR Brain Images. In: Jayakumari, J., Karagiannidis, G., Ma, M., Hossain, S. (eds) Advances in Communication Systems and Networks . Lecture Notes in Electrical Engineering, vol 656. Springer, Singapore. https://doi.org/10.1007/978-981-15-3992-3_11

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  • DOI: https://doi.org/10.1007/978-981-15-3992-3_11

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