University of Salzburg, Department of Computer Sciences, Jakob Haringerstrasse 2, 5020 Salzburg, Austria.
Hiroshima University, Department of Information Engineering, Graduate School of Engineering, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan.
Med Image Anal. 2016 Jul;31:16-36. doi: 10.1016/j.media.2016.02.001. Epub 2016 Feb 16.
In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution. So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.
在这项工作中，应用了各种基于小波的方法，如离散小波变换、双树复小波变换、Gabor 小波变换、Curvelets、Contourlets 和 Shearlets，用于结肠息肉的自动分类。这些方法在 8 个高清内窥镜图像数据库上进行了测试，其中每个数据库都是使用不同的成像模式（Pentax 的 i-Scan 技术结合或不结合黏膜染色）、2 个 NBI 高倍放大数据库和 1 个具有 chromoscopy 高倍放大图像的数据库获得的。为了评估基于小波的方法在结肠息肉分类方面的适用性，使用通用框架比较了 3 种小波变换和最近的 Curvelets、Contourlets 和 Shearlets 的分类性能。小波变换已经被广泛应用于结肠息肉的分类，而 Curvelets、Contourlets 和 Shearlets 迄今为止尚未用于此目的。我们应用不同的特征提取技术来提取基于小波的方法的子带信息。基于小波的方法的 25 种方法中的大多数已经在不同的纹理分类环境中发布。因此，目的也是使用通用框架评估和比较它们的分类性能。25 种方法中有 3 种是新颖的。这三种方法从 Curvelets、Contourlets 和 Shearlets 的子带中提取 Weibull 特征。此外，还应用了 5 种最先进的非小波方法来处理我们的数据库，以便我们可以将它们的结果与基于小波的方法进行比较。结果表明，从子带系数中提取 Weibull 分布参数通常会导致较高的分类结果，尤其是对于双树复小波变换、Gabor 小波变换和 Shearlet 变换。这三种基于小波的变换与 Weibull 特征相结合，甚至在大多数数据库上都优于最先进的方法。我们还将表明，Weibull 分布比其他常用的概率分布（如高斯分布和广义高斯分布）更适合于模型子带系数分布。因此，这项工作对结肠息肉分类的基于小波的方法进行了合理的总结，并且用于我们实验的大量内窥镜息肉数据库确保了所获得结果的高度重要性。
