TY - GEN
T1 - Higher-order CRF tumor segmentation with discriminant manifold potentials
AU - Kadoury, Samuel
AU - Abi-Jaoudeh, Nadine
AU - Valdes, Pablo A.
PY - 2013
Y1 - 2013
N2 - The delineation of tumor boundaries in medical images is an essential task for the early detection, diagnosis and follow-up of cancer. However accurate segmentation remains challenging due to presence of noise, inhomogeneity and high appearance variability of malignant tissue. In this paper, we propose an automatic segmentation approach using fully-connected higher-order conditional random fields (HOCRF) where potentials are computed within a discriminant Grassmannian manifold. First, the framework learns within-class and between-class similarity distributions from a training set of images to discover the optimal manifold discrimination between normal and pathological tissues. Second, the conditional optimization scheme computes non-local pairwise as well as pattern-based higher-order potentials from the manifold subspace to recognize regions with similar labelings and incorporate global consistency in the inference process. Our HOCRF framework is applied in the context of metastatic liver tumor segmentation in CT images. Compared to state of the art methods, our method achieves better performance on a group of 30 liver tumors and can deal with highly pathological cases.
AB - The delineation of tumor boundaries in medical images is an essential task for the early detection, diagnosis and follow-up of cancer. However accurate segmentation remains challenging due to presence of noise, inhomogeneity and high appearance variability of malignant tissue. In this paper, we propose an automatic segmentation approach using fully-connected higher-order conditional random fields (HOCRF) where potentials are computed within a discriminant Grassmannian manifold. First, the framework learns within-class and between-class similarity distributions from a training set of images to discover the optimal manifold discrimination between normal and pathological tissues. Second, the conditional optimization scheme computes non-local pairwise as well as pattern-based higher-order potentials from the manifold subspace to recognize regions with similar labelings and incorporate global consistency in the inference process. Our HOCRF framework is applied in the context of metastatic liver tumor segmentation in CT images. Compared to state of the art methods, our method achieves better performance on a group of 30 liver tumors and can deal with highly pathological cases.
UR - http://www.scopus.com/inward/record.url?scp=84885800860&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-40811-3_90
DO - 10.1007/978-3-642-40811-3_90
M3 - Conference contribution
C2 - 24505731
AN - SCOPUS:84885800860
SN - 9783642408106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 719
EP - 726
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 26 September 2013
ER -