A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/47813059:19240/23:A0001149 RIV/47813059:19630/23:A0000302

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

—

Číslo periodika v rámci svazku

duben 2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

24

Strana od-do

—

Kód UT WoS článku

000925845400001

EID výsledku v databázi Scopus

2-s2.0-85146707007