Standard
A pattern classification approach to aorta calcium scoring in radiographs. / de Bruijne, Marleen.
Computer Vision for Biomedical Image Applications: ICCV workshop: Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends. <Forlag uden navn>, 2005. s. 170-177 (Lecture notes in computer science, Bind 3765/2005).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
de Bruijne, M 2005,
A pattern classification approach to aorta calcium scoring in radiographs. i
Computer Vision for Biomedical Image Applications: ICCV workshop: Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends. <Forlag uden navn>, Lecture notes in computer science, bind 3765/2005, s. 170-177, First International Workshop Computer Vision for Biomedical Image Applications (CVBIA), Beijing, Kina,
29/11/2010.
https://doi.org/10.1007/11569541
APA
de Bruijne, M. (2005).
A pattern classification approach to aorta calcium scoring in radiographs. I
Computer Vision for Biomedical Image Applications: ICCV workshop: Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends (s. 170-177). <Forlag uden navn>. Lecture notes in computer science Bind 3765/2005
https://doi.org/10.1007/11569541
Vancouver
de Bruijne M.
A pattern classification approach to aorta calcium scoring in radiographs. I Computer Vision for Biomedical Image Applications: ICCV workshop: Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends. <Forlag uden navn>. 2005. s. 170-177. (Lecture notes in computer science, Bind 3765/2005).
https://doi.org/10.1007/11569541
Author
de Bruijne, Marleen. / A pattern classification approach to aorta calcium scoring in radiographs. Computer Vision for Biomedical Image Applications: ICCV workshop: Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends. <Forlag uden navn>, 2005. s. 170-177 (Lecture notes in computer science, Bind 3765/2005).
Bibtex
@inproceedings{93c1c3004c1b11dd8d9f000ea68e967b,
title = "A pattern classification approach to aorta calcium scoring in radiographs",
abstract = "A method for automated detection of calcifications in the abdominal aorta from standard X-ray images is presented. Pixel classification based on local image structure is combined with a spatially varying prior that is derived from a statistical model of the combined shape variation in aorta and spine. Leave-one-out experiments were performed on 87 standard lateral lumbar spine X-rays, resulting in on average 93.7% of the pixels within the aorta being correctly classified. ",
author = "{de Bruijne}, Marleen",
year = "2005",
doi = "10.1007/11569541",
language = "English",
isbn = "978-3-540-29411-5",
series = "Lecture notes in computer science",
publisher = "<Forlag uden navn>",
pages = "170--177",
booktitle = "Computer Vision for Biomedical Image Applications",
note = "null ; Conference date: 29-11-2010",
}
RIS
TY - GEN
T1 - A pattern classification approach to aorta calcium scoring in radiographs
AU - de Bruijne, Marleen
N1 - Conference code: 1
PY - 2005
Y1 - 2005
N2 - A method for automated detection of calcifications in the abdominal aorta from standard X-ray images is presented. Pixel classification based on local image structure is combined with a spatially varying prior that is derived from a statistical model of the combined shape variation in aorta and spine. Leave-one-out experiments were performed on 87 standard lateral lumbar spine X-rays, resulting in on average 93.7% of the pixels within the aorta being correctly classified.
AB - A method for automated detection of calcifications in the abdominal aorta from standard X-ray images is presented. Pixel classification based on local image structure is combined with a spatially varying prior that is derived from a statistical model of the combined shape variation in aorta and spine. Leave-one-out experiments were performed on 87 standard lateral lumbar spine X-rays, resulting in on average 93.7% of the pixels within the aorta being correctly classified.
U2 - 10.1007/11569541
DO - 10.1007/11569541
M3 - Article in proceedings
SN - 978-3-540-29411-5
T3 - Lecture notes in computer science
SP - 170
EP - 177
BT - Computer Vision for Biomedical Image Applications
PB - <Forlag uden navn>
Y2 - 29 November 2010
ER -
