Ms. Weiying Xie Profile

Weiying Xie

at Xidian Univ

SPIE Involvement:

Author

Publications (5)

SPIE Journal Paper | 23 June 2017

Hyperspectral image filtering with adaptive manifold for classification

Weiying Xie, Yunsong Li, Weiping Zhou

JEI, Vol. 26, Issue 03, 033025, (June 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.3.033025

KEYWORDS: Hyperspectral imaging, Adaptive optics, Image filtering, Image classification, Spatial filters, 3D image processing

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SPIE Journal Paper | 9 May 2016

Reconstruction of hyperspectral image using matting model for classification

Weiying Xie, Yunsong Li, Chiru Ge

OE, Vol. 55, Issue 05, 053104, (May 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.5.053104

KEYWORDS: Image classification, Hyperspectral imaging, Lithium, Image quality, Germanium, Optical engineering, Infrared imaging, Composites, Data modeling, Image processing

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Proceedings Article | 21 May 2015 Paper

A new detection algorithm for microcalcification clusters in mammographic screening

Weiying Xie, Yide Ma, Yunsong Li

Proceedings Volume 9501, 95010S (2015) https://doi.org/10.1117/12.2180765

KEYWORDS: Image segmentation, Image processing, Hough transforms, Detection and tracking algorithms, Image analysis, Mammography, Image enhancement, Blob detection, Databases, Edge detection

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Proceedings Article | 21 May 2015 Paper

Breast mass segmentation in digital mammography based on pulse coupled neural network and level set method

Weiying Xie, Yide Ma, Yunsong Li

Proceedings Volume 9501, 95010J (2015) https://doi.org/10.1117/12.2180760

KEYWORDS: Image segmentation, Breast, Image processing algorithms and systems, Mammography, Neural networks, Evolutionary algorithms, Image processing, Databases, Tumors, Image analysis

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A novel approach to mammographic image segmentation, termed as PCNN-based level set algorithm, is presented in this paper. Just as its name implies, a method based on pulse coupled neural network (PCNN) in conjunction with the variational level set method for medical image segmentation. To date, little work has been done on detecting the initial zero level set contours based on PCNN algorithm for latterly level set evolution. When all the pixels of the input image are fired by PCNN, the small pixel value will be a much more refined segmentation. In mammographic image, the breast tumor presents big pixel value. Additionally, the mammographic image with predominantly dark region, so that we firstly obtain the negative of mammographic image with predominantly dark region except the breast tumor before all the pixels of an input image are fired by PCNN. Therefore, in here, PCNN algorithm is employed to achieve mammary-specific, initial mass contour detection. After that, the initial contours are all extracted. We define the extracted contours as the initial zero level set contours for automatic mass segmentation by variational level set in mammographic image analysis. What’s more, a new proposed algorithm improves external energy of variational level set method in terms of mammographic images in low contrast. In accordance with the gray scale of mass region in mammographic image is higher than the region surrounded, so the Laplace operator is used to modify external energy, which could make the bright spot becoming much brighter than the surrounded pixels in the image. A preliminary evaluation of the proposed method performs on a known public database namely MIAS, rather than synthetic images. The experimental results demonstrate that our proposed approach can potentially obtain better masses detection results in terms of sensitivity and specificity. Ultimately, this algorithm could lead to increase both sensitivity and specificity of the physicians’ interpretation of mammograms in clinical practice.

Proceedings Article | 21 May 2015 Paper

An automatic fractional coefficient setting method of FODPSO for hyperspectral image segmentation

Weiying Xie, Yunsong Li

Proceedings Volume 9501, 95010D (2015) https://doi.org/10.1117/12.2180751

KEYWORDS: Image segmentation, Hyperspectral imaging, Particle swarm optimization, Image processing algorithms and systems, Particles, Image processing, Image classification, Lithium, Image analysis, Image fusion

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