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Your search keyword '"Li, Pingxiang"' showing total 148 results
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148 results on '"Li, Pingxiang"'

2. Selective Information Passing for MR/CT Image Segmentation

Author

Zhu, Qikui, Li, Liang, Hao, Jiangnan, Zha, Yunfei, Zhang, Yan, Cheng, Yanxiang, Liao, Fei, and Li, Pingxiang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of clear boundary and significant shape and texture variation between images. Many researchers proposed an encoder-decoder architecture with skip connections to combine low-level feature maps from the encoder path with high-level feature maps from the decoder path for automatically segmenting medical images. The skip connections have been shown to be effective in recovering fine-grained details of the target objects and may facilitate the gradient back-propagation. However, not all the feature maps transmitted by those connections contribute positively to the network performance. In this paper, to adaptively select useful information to pass through those skip connections, we propose a novel 3D network with self-supervised function, named selective information passing network (SIP-Net). We evaluate our proposed model on the MICCAI Prostate MR Image Segmentation 2012 Grant Challenge dataset, TCIA Pancreas CT-82 and MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge dataset. The experimental results across these data sets show that our model achieved improved segmentation results and outperformed other state-of-the-art methods. The source code of this work is available at https://github.com/ahukui/SIPNet.

Published
2020

39. PolSAR additive noise estimation based on shadow regions.

Author

Yang, Le, Shi, Lei, Yang, Jie, Li, Pingxiang, Zhao, Lingli, and Zhao, Jinqi

Subjects
POLARIMETRY, SYNTHETIC aperture radar, SYNTHETIC apertures, NOISE, THERMAL noise, RADAR antennas
Abstract

Additive noise results from the thermal noise of the radar receiver or antenna, and often has an adverse effect on the subsequent applications. To weaken the influence of additive noise, it is crucial to know the actual additive noise level. However, the system noise level is not always provided in some single- or full-polarimetric synthetic aperture radar (PolSAR) products. As one of the typical low backscattering objects (LBOs) in PolSAR images, shadow occurs as soon as the depression angle of the synthetic aperture radar (SAR) system reaches a more value than the slope away from the radar illumination, and thus provides a feasible way to measure additive noise. This paper proposes a framework to automatically extract shadow regions by a trained model using the bag of words (BoW) model with data from different scenes. The additive noise can then be estimated across a large area based on the extracted shadow. The experiments based on single- and full-polarimetric Radarsat-2 data suggest that the shadow-based framework is an effective way to estimate additive noise. [ABSTRACT FROM AUTHOR]

Published
2021
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