Color treatment in endoscopic image classification using multi-scale local color vector patterns.
Color treatment in endoscopic image classification using multi-scale local color vector patterns.
复制标题
DOI：
10.1016/j.media.2011.05.006
复制
发表时间：
2012-01
影响因子：
10.9
通讯作者：
Wrba, F.
中科院分区：
文献类型：
--
作者：
Haefner, M.;Liedlgruber, M.;Uhl, A.;Vecsei, A.;Wrba, F.
在这项工作中，我们提出了一种新的多尺度算子，它是基于图像内的全色信息。为了评估该方法，我们使用该算子从内窥镜图像中提取特征，并根据相应的息肉类对图像进行分类。与其他基于LBP的算子相比，LCVP使用了所有可用的颜色信息，但为图像提供了更紧凑的描述符。与其他评估的基于LBP的方法相比，EMLCVP的速度快了7.5倍。在息肉分类方面，LCVP的准确性仅与以前开发的方法无显著差异。在这项工作中，我们提出了一种新的方法来描述局部纹理特性的彩色图像的内窥镜图像的自动分类的目的。与可比较的局部二进制模式算子方法相比，其中相应的纹理算子几乎总是单独应用于每个颜色通道，我们从图像中构建颜色向量场。基于这个领域的建议运营商计算相邻像素之间的相似性。由此产生的图像描述符是一个紧凑的一维直方图，我们使用的k-最近邻分类器进行分类。为了显示该操作符的可用性，我们使用它来分类放大内窥镜图像根据坑模式分类方案。除此之外，我们还表明，与以前提出的运营商相比，我们不仅能够在我们的应用场景中获得有竞争力的分类结果，而且所提出的运营商也能够在速度，特征紧凑性或两者方面优于其他方法。
In this work we propose a novel multi-scale operator which is based on the full color information within an image. In order to evaluate the method, we extract features from endoscopic images using this operator and classify the images according to the respective class of polyps. ► Compared to other LBP-based operators LCVP uses all color information available, yet yielding a more compact descriptor for an image. ► LCVP is up to 7.5 times faster compared to other LBP-based methods evaluated. ► In terms of a classification of polyps the accuracy of LCVP differs insignificantly only from previously developed methods. In this work we propose a novel method to describe local texture properties within color images with the aim of automated classification of endoscopic images. In contrast to comparable Local Binary Patterns operator approaches, where the respective texture operator is almost always applied to each color channel separately, we construct a color vector field from an image. Based on this field the proposed operator computes the similarity between neighboring pixels. The resulting image descriptor is a compact 1D-histogram which we use for a classification using the k-nearest neighbors classifier. To show the usability of this operator we use it to classify magnification-endoscopic images according to the pit pattern classification scheme. Apart from that, we also show that compared to previously proposed operators we are not only able to get competitive classification results in our application scenario, but that the proposed operator is also able to outperform the other methods either in terms of speed, feature compactness, or both.
登录
查看更多内容
影响因子：
7.7
作者：
Iakovidis, Dimitris K.;Maroulis, Dimitris E.;Karkanis, Stavros A.
通讯作者：
Karkanis, Stavros A.
影响因子：
10.6
作者：
Mackiewicz, Michal;Berens, Jeff;Fisher, Mark
通讯作者：
Fisher, Mark
影响因子：
3.4
作者：
KUDO, S;HIROTA, S;YAGYUU, A
通讯作者：
YAGYUU, A
影响因子：
4.3
作者：
Kato, Shigeharu;Fu, Kuang I;Fujimori, Takahiro
通讯作者：
Fujimori, Takahiro
影响因子：
2.8
作者：
Andrade Zanoni, Esdras Camargo;Cutait, Raul;Camara Lopes, Luiz H.
通讯作者：
Camara Lopes, Luiz H.