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Feature Classification in Ultrasound Textures for Image Quality Assessment:a Preliminary Study on the Characterization and Selection of Haralick Parameters by Means of Correlation Matrices

机译:图像质量评估超声纹理中的特征分类:通过相关矩阵对Haralick参数的表征和选择的初步研究

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This paper describes a preliminary study on feature selection from the gray level co-occurrence matrix (GLCM) among the 14 features proposed by R.M. Haralick (1979) with the aim to apply them to ultrasound image classification and Quality Assessment. In particular4 main-classes of images with different patterns (Lines, Chess, alternates Row and Circles) have been implemented and different levels of speckle noisehave been added to simulate ultrasound images with different textures. With the aim to characterize therelationship betweenHaralickfeatures and the pattern type, size, contrastand noise, someCorrelation Matrices have been implemented. Preliminary results are explained and discussed.
机译:本文介绍了R.9提出的14个特征中灰度共发生矩阵(GLCM)特征选择的初步研究。 Haralick(1979)旨在将它们应用于超声图像分类和质量评估。特别是4已经实现了不同模式(线条,国际象棋,替代行和圆圈)的主要图像,并且添加了不同级别的散斑噪声漏洞,以模拟具有不同纹理的超声图像。旨在在哈拉克里克治疗和图案类型,尺寸,触对噪声,SomErecoration矩阵之间表征相关性。解释和讨论了初步结果。

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