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首页> 外文会议>Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009 >Texture Analysis of Ultrasonic Image Based on Wavelet Packet Denoising and Feature Extraction
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Texture Analysis of Ultrasonic Image Based on Wavelet Packet Denoising and Feature Extraction

机译：基于小波包去噪和特征提取的超声图像纹理分析

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摘要

The paper introduces a kind of approach for ultrasonic image categorization based on wavelet packet denoising and texture analysis. Firstly, the texture image denoising method based on wavelet packet transform modulus maximum is adopted aiming at texture images of complicated texture and abundant details. The method can maintain image details at the same time of denoising. Then by using gray level co-occurrence matrix (GLCM) method, parameters in four directions which can represent images texture feature efficiently are extracted: energy, contrast, entropy and inverse difference moment. Finally neural network is used to identify two kinds of images according to extracted characteristic parameters and achieves good effects.
机译：介绍了一种基于小波包去噪和纹理分析的超声图像分类方法。首先，针对纹理复杂，细节丰富的纹理图像，采用基于小波包变换模极大值的纹理图像去噪方法。该方法可以在去噪的同时保持图像细节。然后，通过使用灰度共生矩阵（GLCM）方法，从四个方向上分别提取能有效表示图像纹理特征的参数：能量，对比度，熵和反差矩。最后利用神经网络根据提取的特征参数对两种图像进行识别，取得了良好的效果。

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