TY - JOUR
T1 - Abnormality segmentation in brain images via distributed estimation
AU - Zacharaki, Evangelia I.
AU - Bezerianos, Anastasios
N1 - Funding Information:
Manuscript received April 18, 2011; revised September 19, 2011; accepted November 17, 2011. Date of publication December 7, 2011; date of current version May 4, 2012. This work was supported by the 7th European Community Framework Programme under Grant Marie Curie International Reintegration. The authors are with the School of Medicine, University of Patras, Rio 26504, Achaia, Greece (e-mail: ezachar@upatras.gr; bezer@upatras.gr). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TITB.2011.2178422 Fig. 1. Axial MR slices of normal brain illustrating the remaining variability after spatial normalization.
PY - 2012
Y1 - 2012
N2 - The aim of this paper is to introduce a novel semisupervised scheme for abnormality detection and segmentation in medical images. Semisupervised learning does not require pathology modeling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The method is applied for automatically segmenting brain pathologies, such as simulated brain infarction and dysplasia, as well as real lesions in diabetes patients. The assessment of the method using receiver operating characteristic analysis demonstrates improvement in image segmentation over two-group analysis performed with Statistical Parametric Mapping (SPM).
AB - The aim of this paper is to introduce a novel semisupervised scheme for abnormality detection and segmentation in medical images. Semisupervised learning does not require pathology modeling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The method is applied for automatically segmenting brain pathologies, such as simulated brain infarction and dysplasia, as well as real lesions in diabetes patients. The assessment of the method using receiver operating characteristic analysis demonstrates improvement in image segmentation over two-group analysis performed with Statistical Parametric Mapping (SPM).
KW - Abnormality detection
KW - brain pathology
KW - distributed estimation
KW - image segmentation
KW - statistical modeling
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U2 - 10.1109/TITB.2011.2178422
DO - 10.1109/TITB.2011.2178422
M3 - Article
C2 - 22157062
AN - SCOPUS:84860656360
VL - 16
SP - 330
EP - 338
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 3
M1 - 6096414
ER -