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Detection of Alzheimer’s Disease in Brain MR Images Using Hybrid Local Graph Structure

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • A. Srinivasan

    (CSIR-Central Mechanical Engineering Research Institute)

  • I. Ananda Prasad

    (Indian Institute of Information Technology)

  • V. Mounya

    (International Institute of Information Technology)

  • P. Bhattacharjee

    (CSIR-Central Mechanical Engineering Research Institute)

  • G. Sanyal

    (National Institute of Technology)

Abstract

Alzheimer’s Disease (AD) is a serious neurodegenerative and progressive disease. It annihilates memory power and other mental functionalities. Development of Computer aided diagnosis will provide a second opinion to the radiologist in the detection and diagnosis processes. So, herein a novel method has been developed to study the brain MR image to classify the normal and Alzheimer’s. The proposed method consists of two stages. In the first stage image pre-processing like image enhancement, skull stripping and region of interest extraction has been performed. In the later stage, different graph structure based methods like local graph structure, extended local graph structure and hybrid local graph structure has been experimented and reported. The proposed method is experimented using publically available database OASIS and the average accuracy is observed to be 74.84%. This proposed method will floor the approach for developing devices for detection of AD in the future. Alzheimer’s Disease (AD) is an progressive neuro degenerative disease.

Suggested Citation

  • A. Srinivasan & I. Ananda Prasad & V. Mounya & P. Bhattacharjee & G. Sanyal, 2020. "Detection of Alzheimer’s Disease in Brain MR Images Using Hybrid Local Graph Structure," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 899-907, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_91
    DOI: 10.1007/978-3-030-41862-5_91
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