Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos

Melanie Ganz; Mads Nielsen; Sami Brandt

doi:10.1007/978-3-642-15948-0_5

Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos

Melanie Ganz, Mads Nielsen, Sami Brandt

Department of Computer Science

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Machine Learning in Medical Imaging : First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings
Editors	Fei Wang, Pingkun Yan, Kenji Suzuki, Dinggang Shen
Number of pages	8
Publisher	Springer
Publication date	2010
Pages	34-41
ISBN (Print)	978-3-642-15947-3
ISBN (Electronic)	978-3-642-15948-0
DOIs	https://doi.org/10.1007/978-3-642-15948-0_5
Publication status	Published - 2010
Event	1st International Workshop on Machine Learning in Medical Imaging - Beijing, China Duration: 20 Sept 2010 → 20 Sept 2010 Conference number: 1

Conference

Conference	1st International Workshop on Machine Learning in Medical Imaging
Number	1
Country/Territory	China
City	Beijing
Period	20/09/2010 → 20/09/2010

Series	Lecture notes in computer science
Volume	6357
ISSN	0302-9743

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10.1007/978-3-642-15948-0_5

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Ganz, M., Nielsen, M., & Brandt, S. (2010). Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos. In F. Wang, P. Yan, K. Suzuki, & D. Shen (Eds.), Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings (pp. 34-41). Springer. https://doi.org/10.1007/978-3-642-15948-0_5

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. ed. / Fei Wang; Pingkun Yan; Kenji Suzuki; Dinggang Shen. Springer, 2010. p. 34-41 (Lecture notes in computer science, Vol. 6357).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Ganz, M , Nielsen, M & Brandt, S 2010, Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos. in F Wang, P Yan, K Suzuki & D Shen (eds), Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. Springer, Lecture notes in computer science, vol. 6357, pp. 34-41, 1st International Workshop on Machine Learning in Medical Imaging, Beijing, China, 20/09/2010. https://doi.org/10.1007/978-3-642-15948-0_5

Ganz M , Nielsen M, Brandt S. Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos. In Wang F, Yan P, Suzuki K, Shen D, editors, Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. Springer. 2010. p. 34-41. (Lecture notes in computer science, Vol. 6357). doi: 10.1007/978-3-642-15948-0_5

Ganz, Melanie ; Nielsen, Mads ; Brandt, Sami. / Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos. Machine Learning in Medical Imaging: First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings. editor / Fei Wang ; Pingkun Yan ; Kenji Suzuki ; Dinggang Shen. Springer, 2010. pp. 34-41 (Lecture notes in computer science, Vol. 6357).

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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.

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Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos

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