TY - GEN
T1 - Segmentation of pathology by statistical modeling and distributed estimation
AU - Zacharaki, Evangelia I.
AU - Bezerianos, Anastasios
PY - 2011/12/15
Y1 - 2011/12/15
N2 - The aim of this study is to detect pathology, such as cerebrovascular disease, in brain images by assuming that the pathology is beyond the expected morphological variability of normal images. However the construction of a statistical model of the inter-subject variability over the whole high resolution image is especially challenging due to large dimensionality. For this reason, we apply image partitioning and formulate a strictly concave likelihood function estimating pathology for each local partition. We apply a distributed estimation algorithm in order to fuse the local estimates of each overlapping partition into a globally optimal estimate that satisfies consistency constraints. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The assessment of the method on FLAIR brain images by receiver operating characteristic (ROC) analysis demonstrates improvement in image segmentation over two-group analysis performed with SPM.
AB - The aim of this study is to detect pathology, such as cerebrovascular disease, in brain images by assuming that the pathology is beyond the expected morphological variability of normal images. However the construction of a statistical model of the inter-subject variability over the whole high resolution image is especially challenging due to large dimensionality. For this reason, we apply image partitioning and formulate a strictly concave likelihood function estimating pathology for each local partition. We apply a distributed estimation algorithm in order to fuse the local estimates of each overlapping partition into a globally optimal estimate that satisfies consistency constraints. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The assessment of the method on FLAIR brain images by receiver operating characteristic (ROC) analysis demonstrates improvement in image segmentation over two-group analysis performed with SPM.
UR - http://www.scopus.com/inward/record.url?scp=83255189762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83255189762&partnerID=8YFLogxK
U2 - 10.1109/IWBE.2011.6079015
DO - 10.1109/IWBE.2011.6079015
M3 - Conference contribution
AN - SCOPUS:83255189762
SN - 9781457705526
T3 - 10th International Workshop on Biomedical Engineering, BioEng 2011
BT - 10th International Workshop on Biomedical Engineering, BioEng 2011
T2 - 10th IEEE International Workshop on Biomedical Engineering, BioEng 2011
Y2 - 5 October 2011 through 7 October 2011
ER -