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Record Nr. |
UNINA9910484013903321 |
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Autore |
Shehab Mohammad |
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Titolo |
Artificial Intelligence in Diffusion MRI : Enhanced Cuckoo Search Algorithm with Metaheuristic Components for Extracting the Maxima of the Orientation Distribution Function / / by Mohammad Shehab |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (170 pages) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 877 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Biomedical engineering |
Artificial intelligence |
Computational Intelligence |
Biomedical Engineering and Bioengineering |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction Of Diffusion MRI and Cuckoo Search Algorithm -- Background Of Diffusion MRI -- Cuckoo Search Algorithm -- Methodology Of Extracting The Odf Maxima Using Csa. |
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Sommario/riassunto |
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This book focuses on the use of artificial intelligence to address a specific problem in the brain – the orientation distribution function. It discusses three aspects: (i) Preparing, enhancing and evaluating one of the cuckoo search algorithms (CSA); (ii) Describing the problem: Diffusion-weighted magnetic resonance imaging (DW-MRI) is used for non-invasive investigations of anatomical connectivity in the human brain, while Q-ball imaging (QBI) is a diffusion MRI reconstruction technique based on the orientation distribution function (ODF), which detects the dominant fiber orientations; however, ODF lacks local estimation accuracy along the path. (iii) Evaluating the performance of the CSA versions in solving the ODF problem using synthetic and real-world data. This book appeals to both postgraduates and researchers who are interested in the fields of medicine and computer science. . |
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