Edge sharpness assessment by parametric modeling: Application to magnetic resonance imaging
Edge sharpness assessment by parametric modeling: Application to magnetic resonance imaging
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DOI：
10.1002/cmr.a.21339
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发表时间：
2015-05-01
影响因子：
0.6
通讯作者：
Simonetti, Orlando P.
中科院分区：
文献类型：
--
作者：
Ahmad, Rizwan;Ding, Yu;Simonetti, Orlando P.
在生物医学成像中，边缘清晰度是一个重要但经常被忽视的图像质量指标。在这项工作中，提出了一种应用于磁共振成像（MRI）的半自动量化边缘清晰度的方法。该方法基于图像边缘的参数化建模。首先，自动生成边缘图，并使用图形用户界面手动选择一个或多个感兴趣边缘 (EOI)。然后执行多个排除标准以消除可能不适合锐度评估的边缘像素。其次，在 EOI 的每个像素处，沿着局部垂直于 EOI 的小线段读取图像强度分布。第三，与所有 EOI 像素对应的轮廓分别与由四个参数表征的 sigmoid 函数拟合，其中一个参数代表边缘清晰度。最后，利用锐度参数的分布来量化边缘锐度。为了进行验证，该方法应用于模拟数据以及来自幻影成像和电影成像实验的 MRI 数据。即使在信噪比较差的图像中，该方法也可以快速、定量地评估边缘清晰度。尽管这种方法的实用性已在 MRI 中得到证实，但它也可以适用于其他医学成像应用。 (c) 2015 Wiley periodicals, Inc. Concepts Magn Reson Part A 44A: 138-149，2015。
In biomedical imaging, edge sharpness is an important yet often overlooked image quality metric. In this work, a semi-automatic method to quantify edge sharpness is presented with application to magnetic resonance imaging (MRI). The method is based on parametric modeling of image edges. First, an edge map is automatically generated and one or more edges-of-interest (EOI) are manually selected using graphical user interface. Multiple exclusion criteria are then enforced to eliminate edge pixels that are potentially not suitable for sharpness assessment. Second, at each pixel of the EOI, an image intensity profile is read along a small line segment that runs locally normal to the EOI. Third, the profiles corresponding to all EOI pixels are individually fitted with a sigmoid function characterized by four parameters, including one that represents edge sharpness. Last, the distribution of the sharpness parameter is used to quantify edge sharpness. For validation, the method is applied to simulated data as well as MRI data from both phantom imaging and cine imaging experiments. This method allows for fast, quantitative evaluation of edge sharpness even in images with poor signal-to-noise ratio. Although the utility of this method is demonstrated for MRI, it can be adapted for other medical imaging applications. (c) 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part A 44A: 138-149, 2015.