Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform
Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform
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DOI：
10.1109/42.730399
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发表时间：
1998-08-01
影响因子：
10.6
通讯作者：
Krstic, M
中科院分区：
文献类型：
--
作者：
Mojsilovic, A;Popovic, M;Krstic, M
本文介绍了一种新的纹理特征的方法，基于不可分离小波分解，并将其应用于视觉上相似的肝脏弥漫性疾病的歧视。提出的特征提取算法采用不可分梅花形小波变换，并利用变换区域的能量来表征纹理。一组三种不同的组织类型的分类实验表明，尺度/频率的方法，特别是一个基于不可分离的小波变换，可能是一个可靠的方法的纹理特征和分析的B-扫描肝脏图像。梅花变换与传统小波分解的比较表明，梅花变换更适合于表征噪声数据，更适合于实际应用，需要以较低的旋转灵敏度进行描述。
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.