Iterative peak combination: a robust technique for identifying relevant features in medical image histograms

Iterative peak combination: a robust technique for identifying relevant features in medical image histograms
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迭代峰值组合:一种用于识别医学图像直方图中相关特征的强大技术

DOI:
10.1088/2057-1976/aa929d
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发表时间:
2017
影响因子:
1.4
通讯作者:
Joshi K
Joshi K
中科院分区:
--
文献类型:
--
作者:
Joshi K

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基于直方图的方法可用于分析和变换医学图像。直方图规定化是一种这样的方法,其已被广泛用于将锥形束CT(CBCT)图像的直方图变换成与对应的CT图像的直方图匹配。然而,当将导出的变换应用于CBCT图像像素时,可能产生显著的伪影。本文提出了一种新的、鲁棒的医学图像直方图相关特征自动识别方法--迭代峰合并算法。该过程在概念上是简单的,可以同样很好地应用于CT和CBCT图像直方图。我们还演示了如何使用迭代峰组合来变换CBCT图像,以改善CBCT图像像素值的Hounsfield单位(HU)校准,而不会引入额外的伪影。我们分析了36个骨盆CBCT图像,并显示使用迭代峰值组合算法处理的CT图像和CBCT图像之间的脂肪组织像素值的平均差异为23.7 HU。相比之下,未处理CBCT图像为136.7 HU,使用直方图规范处理CBCT图像为50.9 HU。
Histogram-based methods can be used to analyse and transform medical images. Histogram specification is one such method which has been widely used to transform the histograms of cone beam CT (CBCT) images to match those of corresponding CT images. However, when the derived transformation is applied to the CBCT image pixels, significant artefacts can be produced. We propose the iterative peak combination algorithm, a novel and robust method for automatically identifying relevant features in medical image histograms. The procedure is conceptually simple and can be applied equally well to both CT and CBCT image histograms. We also demonstrate how iterative peak combination can be used to transform CBCT images in such as way as to improve the Hounsfield Unit (HU) calibration of CBCT image pixel values, without introducing additional artefacts. We analyse 36 pelvis CBCT images and show that the average difference in fat tissue pixel values between CT images and CBCT images processed using the iterative peak combination algorithm is 23.7 HU. Compared to 136.7 HU in unprocessed CBCT images and 50.9 in CBCT images processed using histogram specification.
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