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Weighted Robust PCA for Statistical Shape Modeling

Ma, Jingting and Lin, Feng and Honsdorf, Jonas and Lentzen, Katharina and Wesarg, Stefan and Erdt, Marius (2016):
Weighted Robust PCA for Statistical Shape Modeling.
In: Medical Imaging and Augmented Reality, Springer, In: 7th International Conference, MIAR 2016, Bern, Switzerland, August 24-26, In: Lecture Notes in Computer Science (LNCS); 9805, DOI: 10.1007/978-3-319-43775-0,
[Conference or Workshop Item]

Abstract

Statistical shape models (SSMs) play an important role in medical image analysis. A sufficiently large number of high quality datasets is needed in order to create a SSM containing all possible shape variations. However, the available datasets may contain corrupted or missing data due to the fact that clinical images are often captured incompletely or contain artifacts. In this work, we propose a weighted Robust Principal Component Analysis (WRPCA) method to create SSMs from incomplete or corrupted datasets. In particular, we introduce a weighting scheme into the conventional Robust Principal Component Analysis (RPCA) algorithm in order to discriminate unusable data from meaningful ones in the decomposition of the training data matrix more accurately. For evaluation, the proposed WRPCA is compared with conventional RPCA on both corrupted (63 CT datasets of the liver) and incomplete datasets (15 MRI datasets of the human foot). The results show a significant improvement in terms of reconstruction accuracy on both datasets.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Ma, Jingting and Lin, Feng and Honsdorf, Jonas and Lentzen, Katharina and Wesarg, Stefan and Erdt, Marius
Title: Weighted Robust PCA for Statistical Shape Modeling
Language: English
Abstract:

Statistical shape models (SSMs) play an important role in medical image analysis. A sufficiently large number of high quality datasets is needed in order to create a SSM containing all possible shape variations. However, the available datasets may contain corrupted or missing data due to the fact that clinical images are often captured incompletely or contain artifacts. In this work, we propose a weighted Robust Principal Component Analysis (WRPCA) method to create SSMs from incomplete or corrupted datasets. In particular, we introduce a weighting scheme into the conventional Robust Principal Component Analysis (RPCA) algorithm in order to discriminate unusable data from meaningful ones in the decomposition of the training data matrix more accurately. For evaluation, the proposed WRPCA is compared with conventional RPCA on both corrupted (63 CT datasets of the liver) and incomplete datasets (15 MRI datasets of the human foot). The results show a significant improvement in terms of reconstruction accuracy on both datasets.

Title of Book: Medical Imaging and Augmented Reality
Series Name: Lecture Notes in Computer Science (LNCS); 9805
Publisher: Springer
Uncontrolled Keywords: Guiding Theme: Individual Health, Research Area: Computer graphics (CG), Research Area: Modeling (MOD), Statistical shape models (SSM), Shape reconstruction
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 7th International Conference, MIAR 2016
Event Location: Bern, Switzerland
Event Dates: August 24-26
Date Deposited: 07 May 2019 09:26
DOI: 10.1007/978-3-319-43775-0
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