Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65583
Title: Multi-modal and multi-layout discriminative learning for placental maturity staging
Authors: Lei, B
Li, W
Yao, Y
Jiang, X
Tan, EL
Qin, J 
Chen, S
Ni, D
Wang, T
Keywords: Color Doppler energy imaging
Fusion
Multi-layout Fisher vector
Multi-modal
Placental maturity staging
Issue Date: 2017
Publisher: Elsevier
Source: Pattern recognition, 2017, v. 63, p. 719-730 How to cite?
Journal: Pattern recognition 
Abstract: Placental maturity staging is a challenging task due to complex imaging procedure, fetal and gestational age variations. To address this issue, we extract features not only from B-mode gray-scale ultrasound (US) images, but also from color Doppler energy (CDE) images. Based on these features, we propose a method to automatically determine the placental maturity by harnessing multi-view and multi-layout discriminative learning fusion. Specifically, we devise a multi-view technique to generate features of complementary information. Scale invariant features are extracted from image locally, and a Gaussian mixture model (GMM) is then applied to summarize the high-level information features. The clustering representatives from GMM are encoded via a multi-layout Fisher vector (MFV) instead of traditional Fisher vector (FV) to aggregate features based on their spatial information. We apply a multi-layout feature encoding method to improve the staging performance using discriminative learning technique. Extensive experimental results demonstrate that our method achieves promising performance in placental maturity staging and outperforms existing methods.
URI: http://hdl.handle.net/10397/65583
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2016.09.037
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