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基于相位一致性的能谱 CT 图像降维检测。

Energy Spectrum CT Image Detection Based Dimensionality Reduction with Phase Congruency.

机构信息

School of Computer, South China Normal University, Guangzhou, 510631, China.

College of Computer Science, Inner Mongolia Key Laboratory of Social Computing and Data Processing, Inner Mongolia University, Hohhot, China.

出版信息

J Med Syst. 2018 Jan 27;42(3):49. doi: 10.1007/s10916-018-0904-y.

Abstract

The image feature detection is widely used in image registration, image stitching and object recognition. The feature detection algorithm can be applied to the detection of artificial images, and can be used to detect the energy spectrum CT image. A new algorithm of phase consistency detection based on dimensionality reduction is proposed in this paper. We mainly focus on the phase congruency of the spectral CT images in the paper and try to use dimensionality reduction to integrate the information of phase congruency detected in the image. The experimental results show that the algorithm can detect the energy spectrum CT image with clear edge and contour, which is beneficial to the subsequent processing. Meanwhile, the algorithm presented is more effective in diagnosis of disease for medical professionals.

摘要

图像特征检测广泛应用于图像配准、图像拼接和目标识别。特征检测算法可应用于人工图像的检测,也可用于能谱 CT 图像的检测。本文提出了一种基于降维的相位一致性检测新算法。本文主要关注能谱 CT 图像的相位一致性,并尝试利用降维来整合图像中检测到的相位一致性信息。实验结果表明,该算法能够检测到具有清晰边缘和轮廓的能谱 CT 图像,有利于后续处理。同时,所提出的算法在医学专业人员的疾病诊断方面更有效。

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