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基于加压开式 DGVF 蛇模型的腰椎 X 射线图像双边检测

Double-edge detection of radiographic lumbar vertebrae images using pressurized open DGVF snakes.

机构信息

Texas Tech University, Lubbock, TX 79415, USA.

出版信息

IEEE Trans Biomed Eng. 2010 Jun;57(6):1325-34. doi: 10.1109/TBME.2010.2040082. Epub 2010 Feb 17.

Abstract

The detection of double edges in X-ray images of lumbar vertebrae is of prime importance in the assessment of vertebral injury or collapse that may be caused by osteoporosis and other spine pathology. In addition, if the above double-edge detection process is conducted within an automatic framework, it would not only facilitate inexpensive and fast means of obtaining objective morphometric measurements on the spine, but also remove the human subjectivity involved in the morphometric analysis. This paper proposes a novel force-formulation scheme, termed as pressurized open directional gradient vector flow snakes, to discriminate and detect the superior and inferior double edges present in the radiographic images of the lumbar vertebrae. As part of the validation process, this algorithm is applied to a set of 100 lumbar images and the detection results are quantified using analyst-generated ground truth. The promising nature of the detection results bears testimony to the efficacy of the proposed approach.

摘要

在评估可能由骨质疏松症和其他脊柱病变引起的椎体损伤或塌陷时,腰椎 X 射线图像中双边缘的检测至关重要。此外,如果上述双边缘检测过程在自动框架内进行,不仅可以方便地获得脊柱客观形态计量测量的廉价和快速手段,还可以消除形态计量分析中涉及的人为主观性。本文提出了一种新的力公式方案,称为受压开放式定向梯度向量流蛇,以区分和检测腰椎放射图像中存在的上边缘和下边缘。作为验证过程的一部分,该算法应用于一组 100 张腰椎图像,并使用分析师生成的地面实况来量化检测结果。检测结果的良好性质证明了所提出方法的有效性。

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