Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Standard
Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos. / Ganz, Melanie; Nielsen, Mads; Brandt, Sami.
Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. red. / Fei Wang; Pingkun Yan; Kenji Suzuki; Dinggang Shen. Springer, 2010. s. 34-41 (Lecture notes in computer science, Bind 6357).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos
AU - Ganz, Melanie
AU - Nielsen, Mads
AU - Brandt, Sami
N1 - Conference code: 1
PY - 2010
Y1 - 2010
N2 - We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation of calcifications, where the area overlap with the ground truth shapes improved significantly compared to the case where the prior was not used.
AB - We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation of calcifications, where the area overlap with the ground truth shapes improved significantly compared to the case where the prior was not used.
U2 - 10.1007/978-3-642-15948-0_5
DO - 10.1007/978-3-642-15948-0_5
M3 - Article in proceedings
SN - 978-3-642-15947-3
T3 - Lecture notes in computer science
SP - 34
EP - 41
BT - Machine Learning in Medical Imaging
A2 - Wang, Fei
A2 - Yan, Pingkun
A2 - Suzuki, Kenji
A2 - Shen, Dinggang
PB - Springer
T2 - 1st International Workshop on Machine Learning in Medical Imaging
Y2 - 20 September 2010 through 20 September 2010
ER -
ID: 170194357