TY - GEN
T1 - Multinomial level-set framework for multi-region image segmentation
AU - Raviv, Tammy Riklin
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - We present a simple and elegant level-set framework for multi-region image segmentation. The key idea is based on replacing the traditional regularized Heaviside function with the multinomial logistic regression function, commonly known as Softmax. Segmentation is addressed by solving an optimization problem which considers the image intensities likelihood, a regularizer, based on boundary smoothness, and a pairwise region interactive term, which is naturally derived from the proposed formulation. We demonstrate our method on challenging multimodal segmentation of MRI scans (4D) of brain tumor patients. Promising results are obtained for image partition into the different healthy brain tissues and the malignant regions.
AB - We present a simple and elegant level-set framework for multi-region image segmentation. The key idea is based on replacing the traditional regularized Heaviside function with the multinomial logistic regression function, commonly known as Softmax. Segmentation is addressed by solving an optimization problem which considers the image intensities likelihood, a regularizer, based on boundary smoothness, and a pairwise region interactive term, which is naturally derived from the proposed formulation. We demonstrate our method on challenging multimodal segmentation of MRI scans (4D) of brain tumor patients. Promising results are obtained for image partition into the different healthy brain tissues and the malignant regions.
UR - http://www.scopus.com/inward/record.url?scp=85019761509&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58771-4_31
DO - 10.1007/978-3-319-58771-4_31
M3 - Conference contribution
AN - SCOPUS:85019761509
SN - 9783319587707
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 386
EP - 395
BT - Scale Space and Variational Methods in Computer Vision - 6th International Conference, SSVM 2017, Proceedings
A2 - Lauze, Francois
A2 - Dong, Yiqiu
A2 - Dahl, Anders Bjorholm
PB - Springer Verlag
T2 - 6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017
Y2 - 4 June 2017 through 8 June 2017
ER -