Department of Electronic, Informatics and Systems, Università di Bologna, Via Venezia 52, 47023 Cesena, Italy.
Artif Intell Med. 2010 Jun;49(2):117-25. doi: 10.1016/j.artmed.2010.02.006. Epub 2010 Mar 24.
This paper focuses on the use of image-based machine learning techniques in medical image analysis. In particular, we present some variants of local binary patterns (LBP), which are widely considered the state of the art among texture descriptors. After we provide a detailed review of the literature about existing LBP variants and discuss the most salient approaches, along with their pros and cons, we report new experiments using several LBP-based descriptors and propose a set of novel texture descriptors for the representation of biomedical images. The standard LBP operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Our variants are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. These sets of features are then used for training a machine-learning classifier (a stand-alone support vector machine).
Extensive experiments are conducted using the following three datasets:
Our results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets. EQP is based on an elliptic neighborhood and a 5 levels scale for encoding the local gray-scale difference. Particularly interesting are the results on the widely studied 2D-HeLa dataset, where, to the best of our knowledge, the proposed descriptor obtains the highest performance among all the several texture descriptors tested in the literature.
本文专注于在医学图像分析中使用基于图像的机器学习技术。特别地，我们提出了局部二值模式（LBP）的一些变体，这些变体被广泛认为是纹理描述符的最新技术。在对现有 LBP 变体的文献进行详细回顾并讨论最突出的方法及其优缺点之后，我们报告了使用几种基于 LBP 的描述符进行的新实验，并提出了一组用于表示生物医学图像的新纹理描述符。标准 LBP 算子定义为灰度不变纹理度量，源自局部邻域中的纹理的一般定义。我们的变体通过考虑邻域计算的不同形状和局部灰度差异评估的不同编码来获得。然后，将这些特征集用于训练机器学习分类器（独立支持向量机）。
使用以下三个数据集进行了广泛的实验：
我们的结果表明，在本文提出的从所有测试数据集的纹理中提取信息的方法中，名为伸长五进制模式（EQP）的新变体是一种性能非常出色的方法。EQP 基于椭圆邻域和用于编码局部灰度差异的 5 级尺度。在广泛研究的 2D-HeLa 数据集上的结果特别有趣，据我们所知，在所测试的文献中的所有几种纹理描述符中，所提出的描述符获得了最高的性能。
