Mojsilović A, Popović M, Marković S, Krstić M
Bell Laboratories, Lucent Technologies, Murray Hill, NJ 07972, USA.
IEEE Trans Med Imaging. 1998 Aug;17(4):541-9. doi: 10.1109/42.730399.
This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algorithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity.
本文描述了一种基于不可分离小波分解的纹理特征描述新方法及其在鉴别视觉上相似的肝脏弥漫性疾病中的应用。所提出的特征提取算法应用不可分离梅花形小波变换，并使用变换区域的能量来表征纹理。对一组三种不同组织类型进行的分类实验表明，尺度/频率方法，特别是基于不可分离小波变换的方法，可能是一种用于B超肝脏图像纹理特征描述和分析的可靠方法。梅花形变换与传统小波分解之间的比较表明，梅花形变换更适合于噪声数据的特征描述以及对旋转敏感性较低的实际应用描述。
