Early diagnosis of dementia based on intersubject whole-brain dissimilarities
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
Early diagnosis of dementia based on intersubject whole-brain dissimilarities. / Klein, S.; Loog, M.; Lijn, F. van der; Heijer, T. den; Hammers, A. ; de Bruijne, Marleen; Lugt, A. van der; Duin, R. P. W.; Breteler, M. M. B.; Niessen, W. J.
2010 IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE, 2010. p. 249-252.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Early diagnosis of dementia based on intersubject whole-brain dissimilarities
AU - Klein, S.
AU - Loog, M.
AU - Lijn, F. van der
AU - Heijer, T. den
AU - Hammers, A.
AU - de Bruijne, Marleen
AU - Lugt, A. van der
AU - Duin, R. P. W.
AU - Breteler, M. M. B.
AU - Niessen, W. J.
N1 - Conference code: 7
PY - 2010
Y1 - 2010
N2 - This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81% was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia.
AB - This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81% was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia.
U2 - 10.1109/ISBI.2010.5490366
DO - 10.1109/ISBI.2010.5490366
M3 - Article in proceedings
SN - 978-1-4244-4125-9
SP - 249
EP - 252
BT - 2010 IEEE International Symposium on Biomedical Imaging
PB - IEEE
T2 - 7th IEEE International Symposium on Biomedical Imaging
Y2 - 14 April 2010 through 17 April 2010
ER -
ID: 19119982