首页> 外文会议>Image Processing pt.1; Progress in Biomedical Optics and Imaging; vol.7 no.30 >Segmentation of Hand Radiographs Using Fast Marching Methods
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Segmentation of Hand Radiographs Using Fast Marching Methods

机译:使用快速行进方法分割手部X光片

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Rheumatoid Arthritis is one of the most common chronic diseases. Joint space width in hand radiographs is evaluated to assess joint damage in order to monitor progression of disease and response to treatment. Manual measurement of joint space width is time-consuming and highly prone to inter- and intra-observer variation. We propose a method for automatic extraction of finger bone boundaries using fast marching methods for quantitative evaluation of joint space width. The proposed algorithm includes two stages: location of hand joints followed by extraction of bone boundaries. By setting the propagation speed of the wave front as a function of image intensity values, the fast marching algorithm extracts the skeleton of the hands, in which each branch corresponds to a finger. The finger joint locations are then determined by using the image gradients along the skeletal branches. In order to extract bone boundaries at joints, the gradient magnitudes are utilized for setting the propagation speed, and the gradient phases are used for discriminating the boundaries of adjacent bones. The bone boundaries are detected by searching for the fastest paths from one side of each joint to the other side. Finally, joint space width is computed based on the extracted upper and lower bone boundaries. The algorithm was evaluated on a test set of 8 two-hand radiographs, including images from healthy patients and from patients suffering from arthritis, gout and psoriasis. Using our method, 97% of 208 joints were accurately located and 89% of 416 bone boundaries were correctly extracted.
机译:类风湿关节炎是最常见的慢性疾病之一。评估手部X射线照片中的关节间隙宽度以评估关节损伤,以监测疾病的进展和对治疗的反应。手动测量关节间隙宽度非常耗时,并且极易发生观察者之间和观察者内部的变化。我们提出了一种使用快速行进方法自动提取指骨边界的方法,用于定量评估关节间隙宽度。所提出的算法包括两个阶段:手关节的位置,然后提取骨边界。通过将波前的传播速度设置为图像强度值的函数,快速行进算法提取出手的骨骼,其中每个分支对应于一根手指。然后通过使用沿着骨骼分支的图像梯度来确定手指关节的位置。为了提取关节处的骨边界,将梯度量值用于设置传播速度,并使用梯度相位来区分相邻骨骼的边界。通过搜索从每个关节的一侧到另一侧的最快路径来检测骨骼边界。最后,根据提取的上下骨边界计算关节间隙宽度。该算法是在8张双手X射线照片的测试集上进行评估的,其中包括来自健康患者以及患有关节炎,痛风和牛皮癣的患者的图像。使用我们的方法,可以正确定位208个关节中的97%,正确提取416个骨边界中的89%。

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