TY - GEN
T1 - Fuzzy multi-channel clustering with individualized spatial priors for segmenting brain lesions and infarcts
AU - Zacharaki, Evangelia I.
AU - Erus, Guray
AU - Bezerianos, Anastasios
AU - Davatzikos, Christos
PY - 2012
Y1 - 2012
N2 - Quantitative analysis of brain lesions and ischemic infarcts is becoming very important due to their association with cardiovascular disease and normal aging. In this paper, we present a semi-supervised segmentation methodology that detects and classifies cerebrovascular disease in multi-channel magnetic resonance (MR) images. The method combines intensity based fuzzy c-means (FCM) segmentation with spatial probability maps calculated from a normative set of images from healthy individuals. Unlike common FCM-based methods which segment only healthy tissue, we have extended the fuzzy segmentation to include patient-specific spatial priors for both pathological conditions (lesions and infarcts). These priors are calculated by estimating the statistical voxel-wise variation of the healthy anatomy, and identifying abnormalities as deviations from normality. False detection is reduced by knowledge-based rules. Assessment on a population of 47 patients from different imaging sites illustrates the potential of the proposed method in segmenting both hyperintense lesions and necrotic infarcts.
AB - Quantitative analysis of brain lesions and ischemic infarcts is becoming very important due to their association with cardiovascular disease and normal aging. In this paper, we present a semi-supervised segmentation methodology that detects and classifies cerebrovascular disease in multi-channel magnetic resonance (MR) images. The method combines intensity based fuzzy c-means (FCM) segmentation with spatial probability maps calculated from a normative set of images from healthy individuals. Unlike common FCM-based methods which segment only healthy tissue, we have extended the fuzzy segmentation to include patient-specific spatial priors for both pathological conditions (lesions and infarcts). These priors are calculated by estimating the statistical voxel-wise variation of the healthy anatomy, and identifying abnormalities as deviations from normality. False detection is reduced by knowledge-based rules. Assessment on a population of 47 patients from different imaging sites illustrates the potential of the proposed method in segmenting both hyperintense lesions and necrotic infarcts.
KW - MRI
KW - brain tissue segmentation
KW - fuzzy clustering
KW - infarcts
KW - lesions
KW - outlier detection
UR - http://www.scopus.com/inward/record.url?scp=84870868779&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870868779&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33412-2_8
DO - 10.1007/978-3-642-33412-2_8
M3 - Conference contribution
AN - SCOPUS:84870868779
SN - 9783642334115
T3 - IFIP Advances in Information and Communication Technology
SP - 76
EP - 85
BT - Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops
T2 - 8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB
Y2 - 27 September 2012 through 30 September 2012
ER -