A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17110%2F23%3AA2402J4U" target="_blank" >RIV/61988987:17110/23:A2402J4U - isvavai.cz</a>
Alternative codes found
RIV/47813059:19240/23:A0001149 RIV/47813059:19630/23:A0000302
Result on the web
<a href="https://reader.elsevier.com/reader/sd/pii/S1746809423000447?token=A38677752C832EB428A31490D5A03342A77F0A614FFA9528634483BB1DC565081FA71891304D1BDC6EB7ADA29B16E96B&originRegion=eu-west-1&originCreation=20230222121155" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S1746809423000447?token=A38677752C832EB428A31490D5A03342A77F0A614FFA9528634483BB1DC565081FA71891304D1BDC6EB7ADA29B16E96B&originRegion=eu-west-1&originCreation=20230222121155</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.bspc.2023.104611" target="_blank" >10.1016/j.bspc.2023.104611</a>
Alternative languages
Result language
angličtina
Original language name
A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging
Original language description
Matching MRI brain images between patients or mapping patients’ MRI slices to the simulated atlas of a brain is key to the automatic registration of MRI of a brain. The ability to match MRI images would also enable such applications as indexing and searching MRI images among multiple patients or selecting images from the region of interest. In this work, we have introduced robustness, accuracy and cumulative distance metrics and methodology that allows us to compare different techniques and approaches in matching brain MRI of different patients or matching MRI brain slice to a position in the brain atlas. To that end, we have used feature detection methods AGAST, AKAZE, BRISK, GFTT, HardNet, and ORB, which are established methods in image processing, and compared them on their resistance to image degradation and their ability to match the same brain MRI slice of different patients. We have demonstrated that some of these techniques can correctly match most of the brain MRI slices of different patients. When matching is performed with the atlas of the human brain, their performance is significantly lower. The best performing feature detection method was a combination of SIFT detector and HardNet descriptor that achieved 93% accuracy in matching images with other patients and only 52% accurately matched images when compared to atlas. © 2023 Elsevier Ltd
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biomedical Signal Processing and Control
ISSN
1746-8094
e-ISSN
1746-8108
Volume of the periodical
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Issue of the periodical within the volume
duben 2023
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
24
Pages from-to
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UT code for WoS article
000925845400001
EID of the result in the Scopus database
2-s2.0-85146707007