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Investigating the Effectiveness of Wavelet Approximations in Resizing Images for Ultrasound Image Classification

机译:研究小波近似在调整图像大小以进行超声图像分类中的有效性

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摘要

Images are difficult to classify and annotate but the availability of digital image databases creates a constant demand for tools that automatically analyze image content and describe it with either a category or a set of variables. Ultrasound Imaging is very popular and is widely used to see the internal organ(s) condition of the patient. The main target of this research is to develop a robust image processing techniques for a better and more accurate medical image retrieval and categorization. This paper looks at an alternative to feature extraction for image classification such as image resizing technique. A new mean for image resizing using wavelet transform is proposed. Results, using real medical images, have shown the effectiveness of the proposed technique for classification task comparing to bi-cubic interpolation and feature extraction.
机译:图像很难分类和注释,但是数字图像数据库的可用性对自动分析图像内容并使用类别或一组变量进行描述的工具产生了持续的需求。超声成像非常流行,并且广泛用于查看患者的内部器官状况。这项研究的主要目标是开发一种健壮的图像处理技术,以实现更好,更准确的医学图像检索和分类。本文着眼于图像分类的特征提取替代方法,例如图像大小调整技术。提出了一种利用小波变换调整图像大小的新方法。使用真实医学图像的结果表明,与双三次插值和特征提取相比,该技术对于分类任务的有效性。

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