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基于三维尺度不变特征变换的容积超声全景图

Volumetric ultrasound panorama based on 3D SIFT.

作者信息

Ni Dong, Qul Yingge, Yang Xuan, Chui Yim Pan, Wong Tien-Tsin, Ho Simon S M, Heng Pheng Ann

机构信息

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China.

出版信息

Med Image Comput Comput Assist Interv. 2008;11(Pt 2):52-60. doi: 10.1007/978-3-540-85990-1_7.

Abstract

The reconstruction of three-dimensional (3D) ultrasound panorama from multiple ultrasound volumes can provide a wide field of view for better clinical diagnosis. Registration of ultrasound volumes has been a key issue for the success of this panoramic process. In this paper, we propose a method to register and stitch ultrasound volumes, which are scanned by dedicated ultrasound probe, based on an improved 3D Scale Invariant Feature Transform (SIFT) algorithm. We propose methods to exclude artifacts from ultrasound images in order to improve the overall performance in 3D feature point extraction and matching. Our method has been validated on both phantom and clinical data sets of human liver. Experimental results show the effectiveness and stability of our approach, and the precision of our method is comparable to that of the position tracker based registration.

摘要

从多个超声容积重建三维(3D)超声全景图可为更好的临床诊断提供广阔视野。超声容积配准一直是这一全景过程成功的关键问题。在本文中,我们提出一种基于改进的三维尺度不变特征变换(SIFT)算法,对由专用超声探头扫描的超声容积进行配准和拼接的方法。我们提出了从超声图像中排除伪像的方法,以提高三维特征点提取和匹配的整体性能。我们的方法已在人体肝脏的模型和临床数据集上得到验证。实验结果表明了我们方法的有效性和稳定性,且我们方法的精度与基于位置跟踪器的配准方法相当。

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