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Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 13 No. 2 (2023)

DOI
10.11591/ijece.v13i2.pp1491-1498
ISSN
2722-2578, 2088-8708
Published
2023-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.

Collaboration network of the authors

2 authors · 1 co-authorship link · 1 across different organisations

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  • Amrita Vishwa Vidyapeetham
  • R.V. College of Engineering
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis</dc:title><dc:creator>Valsala, Bhavana</dc:creator><dc:creator>Honnamachanahalli Kariputtaiah, Krishnappa</dc:creator><dc:description>&lt;jats:p&gt;In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2023-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v13i2.pp1491-1498</dc:identifier><dc:identifier>10.11591/ijece.v13i2.pp1491-1498</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 13 No 2 (2023); 1491</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/27500/16457</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>