Vote for your favorite mineral in #MinCup25! - Carpathite vs. Leucite
Brace for oddness in a match between one of the very few hydrocarbon minerals carpathite and the temperature-flipping mineral leucite.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Jeon, Byunghwan, Jang, Yeonggul, Shim, Hackjoon, Chang, Hyuk-Jae (2019) Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks. Pattern Recognition, 96. 106958pp. doi:10.1016/j.patcog.2019.07.003

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleIdentification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks
JournalPattern Recognition
AuthorsJeon, ByunghwanAuthor
Jang, YeonggulAuthor
Shim, HackjoonAuthor
Chang, Hyuk-JaeAuthor
Year2019 (December)Volume96
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2019.07.003Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367917Long-form Identifiermindat:1:5:9367917:0
GUID0
Full ReferenceJeon, Byunghwan, Jang, Yeonggul, Shim, Hackjoon, Chang, Hyuk-Jae (2019) Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks. Pattern Recognition, 96. 106958pp. doi:10.1016/j.patcog.2019.07.003
Plain TextJeon, Byunghwan, Jang, Yeonggul, Shim, Hackjoon, Chang, Hyuk-Jae (2019) Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks. Pattern Recognition, 96. 106958pp. doi:10.1016/j.patcog.2019.07.003
In(2019) Pattern Recognition Vol. 96. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Jeon, Byunghwan, Hong, Yoonmi, Han, Dongjin, Jang, Yeonggul, Jung, Sunghee, Hong, Youngtaek, Ha, Seongmin, Shim, Hackjoon, Chang, Hyuk-Jae (2017) Maximum a posteriori estimation method for aorta localization and coronary seed identification. Pattern Recognition, 68. 222-232 doi:10.1016/j.patcog.2017.03.005
Jeon, Byunghwan, Jung, Sunghee, Shim, Hackjoon, Chang, Hyuk-Jae (2021) Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images. Entropy, 23 (1) 64pp. doi:10.3390/e23010064
Choi, Seungpil, Jang, Seoyeon, Jung, Sunghee, Cho, Heon Jae, Jeon, Byunghwan (2025) Deep reinforcement learning for efficient registration between intraoral-scan meshes and CT images. Pattern Recognition, 164. doi:10.1016/j.patcog.2025.111502
Moreno, A., Takemura, C.M., Colliot, O., Camara, O., Bloch, I. (2008) Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images. Pattern Recognition, 41 (8). 2525-2540 doi:10.1016/j.patcog.2008.01.020
Jeon, Younghwan, Hwang, Ganguk (2022) Bayesian mixture of gaussian processes for data association problem. Pattern Recognition, 127. 108592pp. doi:10.1016/j.patcog.2022.108592
Rohr, K., Cathier, P., Wörz, S. (2004) Elastic registration of electrophoresis images using intensity information and point landmarks. Pattern Recognition, 37 (5). 1035-1048 doi:10.1016/j.patcog.2003.10.004
Dong, Ganggang, Liu, Hongwei, Kuang, Gangyao, Chanussot, Jocelyn (2019) Target recognition in SAR images via sparse representation in the frequency domain. Pattern Recognition, 96. 106972pp. doi:10.1016/j.patcog.2019.106972
Liu, Chang, Hu, Weiduo (2019) Real-time geometric fitting and pose estimation for surface of revolution. Pattern Recognition, 85. 90-108 doi:10.1016/j.patcog.2018.08.002
(2019) Publisher’s Note: Introducing article numbering to Pattern Recognition. Pattern Recognition, 96. 106974pp. doi:10.1016/j.patcog.2019.106974
Xie, Hongtao, Yang, Dongbao, Sun, Nannan, Chen, Zhineng, Zhang, Yongdong (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognition, 85. 109-119 doi:10.1016/j.patcog.2018.07.031
Zhong, Zhuoyao, Sun, Lei, Huo, Qiang (2019) Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene images. Pattern Recognition, 96. 106986pp. doi:10.1016/j.patcog.2019.106986
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 6, 2025 00:39:08
Go to top of page