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Atrophy Measure of Brain Cortex to Detect Alzheimer’s Disease from Magnetic Resonance Images

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Advances in Electronics, Communication and Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 443))

Abstract

In medical science, diagnosis of Alzheimer’s disease is mainly done manually by expert radiologist. In this paper, an automatic approach to detect Alzheimer’s disease using cortex thickness is analyzed. The cortex of brain is extracted and thickness is measured from magnetic resonance images. For experiment, 20 images of control subjects, 20 with mild cognitive impairment and 20 with Alzheimer’s disease, are taken from Alzheimer’s Disease Neuroimaging Initiative database. Initially, segmentation of cortex, from T1-weighted coronal magnetic resonance image, is done using genetic algorithm-based region growing technique. Then the thickness of the cortex is measured using distance transform. The experiment gives 100, 80, and 85% recognition accuracy for normal, mild cognitive impairment, and Alzheimer’s disease, respectively.

First author receives Inspire Fellowship from Department of Science and Technology, New Delhi.

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Authors and Affiliations

  1. Department of Computer Science, Gauhati University, Guwahati, Assam, India

    Dulumani Das & Sanjib Kumar Kalita

Authors
  1. Dulumani Das
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  2. Sanjib Kumar Kalita
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Correspondence to Dulumani Das .

Editor information

Editors and Affiliations

  1. Smart Energy Research Unit, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia

    Akhtar Kalam

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Kalpana Sharma

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Das, D., Kalita, S.K. (2018). Atrophy Measure of Brain Cortex to Detect Alzheimer’s Disease from Magnetic Resonance Images. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_43

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  • DOI: https://doi.org/10.1007/978-981-10-4765-7_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4764-0

  • Online ISBN: 978-981-10-4765-7

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