MARTINŮ, Jiří, Jan NOVOTNÝ, Karel ADÁMEK, Petr ČERMÁK, Jiri KOZEL and David SKOLOUDIK. A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging. Biomedical Signal Processing and Control. Elsevier, 2023, vol. 82, April 2023, p. "104611-1"-"104611-15", 15 pp. ISSN 1746-8094. Available from: https://dx.doi.org/10.1016/j.bspc.2023.104611.
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Basic information
Original name A survey of feature detection methods for localisation of plain sections of axial brain magnetic resonance imaging
Authors MARTINŮ, Jiří (203 Czech Republic), Jan NOVOTNÝ (203 Czech Republic, belonging to the institution), Karel ADÁMEK (203 Czech Republic, belonging to the institution), Petr ČERMÁK (203 Czech Republic, belonging to the institution), Jiri KOZEL and David SKOLOUDIK.
Edition Biomedical Signal Processing and Control, Elsevier, 2023, 1746-8094.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/47813059:19630/23:A0000302
Organization unit Institute of physics in Opava
Doi http://dx.doi.org/10.1016/j.bspc.2023.104611
UT WoS 000925845400001
Keywords in English Image processing;Medical imaging;Magnetic resonance imaging;Computer visions;Feature detection
Tags RIV24, UF
Tags International impact, Reviewed
Changed by Changed by: Mgr. Pavlína Jalůvková, učo 25213. Changed: 14/2/2024 11:55.
Abstract
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.
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