Generalized gray level dependence method for prostate cancer classification

R. Khelifi, M. Adel, S. Bourennane, A. Moussaoui
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引用次数: 5

Abstract

In this paper, we present a new approach for multi-spectral texture classification. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a Spatial and Spectral Gray Level Dependence Method (SSGLDM) in order to extend the concept of spatial gray level dependence method by assuming texture joint information between spectral bands. In addition, the new texture features measurement related to (SSGLDM) which define the image properties have been also proposed. Extensive experiments have been carried out on many multispectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the Gray Level Co-occurrence Matrix (GLCM). The results indicate a significant improvement in classification accuracy.
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前列腺癌分类的广义灰度依赖法
本文提出了一种新的多光谱纹理分类方法。因此,我们的目标是将光谱信息添加到仅处理灰度空间变化的经典纹理分析方法中。为了实现这一目标,我们提出了一种空间和光谱灰度依赖方法(SSGLDM),通过假设光谱波段之间的纹理联合信息,扩展了空间灰度依赖方法的概念。此外,还提出了与(SSGLDM)相关的新的纹理特征测量方法来定义图像的属性。在多光谱图像上进行了大量用于前列腺癌诊断的实验,定量结果表明,与灰度共生矩阵(GLCM)相比,该方法的效率更高。结果表明,分类精度显著提高。
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