Veenland J F, Grashuis J L, Gelsema E S
Department of Medical Informatics, Erasmus University Rotterdam, The Netherlands.
Med Phys. 1998 Jun;25(6):922-36. doi: 10.1118/1.598271.
Tissue structures, represented by textures in radiographs, can be quantified using texture analysis methods. Different texture analysis methods have been used to discriminate between different aspects of various diseases in primarily x rays of chest, bone, and breasts. However, most of these methods have not specifically been developed for use on radiographs. Certain characteristics of the radiographic process, e.g., noise and blurring, influence the visible texture. In order for a texture analysis method to be able to discriminate between different underlying textures, it should not be too sensitive for such processes as image noise and blur. In this study, we investigated the sensitivity of four different texture analysis methods for image noise and blur. First, a baseline measurement was performed of the discriminative performance of the Spatial Gray-Level Dependence method, the Fourier Power Spectrum, the Fractal Dimension, and the Morphological Gradient Method on images, which were not affected by radiographic noise and blur. Two types of images were used: fractal and Brodatz. Whereas the Brodatz images represent very different textures, the differences between the fractal images are more gradual. We assume that the behavior of the different texture analysis methods on the fractal images is representative for their performance on radiologic textures. On these types of images we simulated the effect of four different noise levels and the effect of two different modulation transfer functions, corresponding with different screenfilm combinations. The influence on the discriminative performance of the four texture analysis methods was evaluated. The influence of noise on the discriminative performance is, as expected, dependent on the image type used; the discrimination of more gradually different images, such as the fractal images, is already lowered for relatively low noise levels. In contrast, when the images are more different, only high noise levels decrease the discriminative performance. The discriminative power of the Morphological Gradient Method is least affected by image blur. We conclude that the discriminative performance of the Morphological Gradient Method is superior to that of other methods in circumstances which mimic the conditions prevailing in radiographs.
以X光片中的纹理表示的组织结构，可以使用纹理分析方法进行量化。不同的纹理分析方法已被用于区分各种疾病在胸部、骨骼和乳房的X光片中的不同方面。然而，这些方法大多并非专门为X光片开发。X光成像过程的某些特性，如噪声和模糊，会影响可见纹理。为了使纹理分析方法能够区分不同的潜在纹理，它不应对图像噪声和模糊等过程过于敏感。在本研究中，我们研究了四种不同纹理分析方法对图像噪声和模糊的敏感性。首先，对空间灰度依赖方法、傅里叶功率谱、分形维数和形态梯度方法在不受X光噪声和模糊影响的图像上的判别性能进行了基线测量。使用了两种类型的图像：分形图像和布罗达茨图像。布罗达茨图像代表非常不同的纹理，而分形图像之间的差异则更为渐进。我们假设不同纹理分析方法在分形图像上的表现代表了它们在放射纹理上的性能。在这些类型的图像上，我们模拟了四种不同噪声水平的影响以及两种不同调制传递函数的影响，这两种函数对应不同的增感屏-胶片组合。评估了对四种纹理分析方法判别性能的影响。正如预期的那样，噪声对判别性能的影响取决于所使用的图像类型；对于分形图像等差异较为渐进的图像，相对较低的噪声水平就会降低判别能力。相反，当图像差异较大时，只有高噪声水平才会降低判别性能。形态梯度方法的判别能力受图像模糊的影响最小。我们得出结论，在模拟X光片普遍存在的条件下，形态梯度方法的判别性能优于其他方法。
