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340 results on '"Yang, Gongping"'

117. Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior.

Author

Xi, Xiaoming, Meng, Xianjing, Yang, Lu, Nie, Xiushan, Yang, Gongping, Chen, Haoyu, Fan, Xin, Yin, Yilong, and Chen, Xinjian

Subjects
IMAGE segmentation, NEOVASCULARIZATION, OPTICAL coherence tomography, ARTIFICIAL neural networks, POTENTIAL functions
Abstract

Automated segmentation of choroidal neovascularization (CNV) in optical coherence tomography (OCT) images plays an important role for the treatment of CNV disease. This paper proposes multi-scale convolutional neural networks with structure prior to segment CNV from OCT data. The proposed framework consists of two stages. In the first stage, the structure prior learning method based on sparse representation-based classification and the local potential function is developed to capture the global spatial structure and local similarity structure prior. The obtained prior can be used to improve the distinctiveness between CNV and background patches. In the second stage, multi-scale CNN model with incorporation of the learned structure prior is constructed for CNV segmentation. In this stage, multi-scale analysis is used to capture effective contextual information, which is robust to varying sizes of CNV. The proposed method was evaluated on 15 spectral domain OCT data with CNV. The experimental results demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

Published
2019
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125. <graphic file='1687-6180-2010-729378-i1.gif'/>-Means Based Fingerprint Segmentation with Sensor Interoperability

Author

Yang Xiukun, Yang Gongping, Zhou Guang-Tong, and Yin Yilong

Subjects
lcsh:TK5101-6720, lcsh:Electronics, lcsh:TK7800-8360, lcsh:Telecommunication
Abstract

A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a -means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the -means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV). SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors.

Published
2010

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