Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images
This paper offers a bird’s eye perception of how bipartite graph modeling could help to comprehend the progression of Alzheimer Disease (AD). We will also discuss the role of the various software tools available in the literature to identify the bipartite structure in AD affected patient brain netwo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Amaltea Medical Publishing House
2021-09-01
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| Series: | Romanian Journal of Neurology |
| Subjects: | |
| Online Access: | https://rjn.com.ro/articles/2021.3/RJN_2021_3_Art-15.pdf |
| _version_ | 1828214606789607424 |
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| author | V. Yegnanarayanan M. Anisha T. Arun Prasath |
| author_facet | V. Yegnanarayanan M. Anisha T. Arun Prasath |
| author_sort | V. Yegnanarayanan |
| collection | DOAJ |
| description | This paper offers a bird’s eye perception of how bipartite graph modeling could help to comprehend the progression of Alzheimer Disease (AD). We will also discuss the role of the various software tools available in the literature to identify the bipartite structure in AD affected patient brain networks and a general procedure to generate a graph from the AD brain network. Further, as AD is a minacious disorder that leads to the progressive decline of memory and physical ability we resort to Computer-Aided Diagnosis. It has a vital part in the preliminary estimation and finding of AD. We propose an approach to become aware of AD particularly in its beginning phase by analyzing the measurable variations in the hippocampus, grey matter, cerebrospinal fluid and white matter of the brain from Magnetic resonance images. Hence an appropriate segmentation and categorization methods are projected to detect the presence of AD. The trials were carried out on Magnetic resonance images to distinguish from the section of interest. The effectiveness of the CAD system was experimentally evaluated from the images considered from publicly available databases. Obtained findings recommend that the established CAD system has boundless prospective and great guarantee for the prognosis of AD. |
| first_indexed | 2024-04-12T15:07:11Z |
| format | Article |
| id | doaj.art-02879690bac14b55b390bee08d0c7c52 |
| institution | Directory Open Access Journal |
| issn | 1843-8148 2069-6094 |
| language | English |
| last_indexed | 2024-04-12T15:07:11Z |
| publishDate | 2021-09-01 |
| publisher | Amaltea Medical Publishing House |
| record_format | Article |
| series | Romanian Journal of Neurology |
| spelling | doaj.art-02879690bac14b55b390bee08d0c7c522022-12-22T03:27:52ZengAmaltea Medical Publishing HouseRomanian Journal of Neurology1843-81482069-60942021-09-0120335636310.37897/RJN.2021.3.15Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain imagesV. Yegnanarayanan0M. Anisha1T. Arun Prasath2Department of Mathematics, Kalasalingam Academy of Research and Education (KARE), Tamilnadu, IndiaDepartment of Biomedical Engineering, Kalasalingam Academy of Research and Education (KARE), Tamilnadu, IndiaDepartment of Biomedical Engineering, Kalasalingam Academy of Research and Education (KARE), Tamilnadu, IndiaThis paper offers a bird’s eye perception of how bipartite graph modeling could help to comprehend the progression of Alzheimer Disease (AD). We will also discuss the role of the various software tools available in the literature to identify the bipartite structure in AD affected patient brain networks and a general procedure to generate a graph from the AD brain network. Further, as AD is a minacious disorder that leads to the progressive decline of memory and physical ability we resort to Computer-Aided Diagnosis. It has a vital part in the preliminary estimation and finding of AD. We propose an approach to become aware of AD particularly in its beginning phase by analyzing the measurable variations in the hippocampus, grey matter, cerebrospinal fluid and white matter of the brain from Magnetic resonance images. Hence an appropriate segmentation and categorization methods are projected to detect the presence of AD. The trials were carried out on Magnetic resonance images to distinguish from the section of interest. The effectiveness of the CAD system was experimentally evaluated from the images considered from publicly available databases. Obtained findings recommend that the established CAD system has boundless prospective and great guarantee for the prognosis of AD.https://rjn.com.ro/articles/2021.3/RJN_2021_3_Art-15.pdfbrain networksbipartite graphsmriregion based level set algorithmgray level co-occurrence matrixalzheimer disease |
| spellingShingle | V. Yegnanarayanan M. Anisha T. Arun Prasath Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images Romanian Journal of Neurology brain networks bipartite graphs mri region based level set algorithm gray level co-occurrence matrix alzheimer disease |
| title | Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images |
| title_full | Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images |
| title_fullStr | Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images |
| title_full_unstemmed | Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images |
| title_short | Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images |
| title_sort | bipartite graph modeling of alzheimer s disease and its early automated discrimination through region based level set algorithm and support vector machine in magnetic resonance brain images |
| topic | brain networks bipartite graphs mri region based level set algorithm gray level co-occurrence matrix alzheimer disease |
| url | https://rjn.com.ro/articles/2021.3/RJN_2021_3_Art-15.pdf |
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