El Khatib Alaa, Werghi Naoufel, Al-Ahmad Hussain
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:2669-72. doi: 10.1109/EMBC.2015.7318941.
In this work we present a performance comparison between a set of different state-of-the-art image descriptors for the automatic detection of polyps in colonoscopy videos. This set includes: Local binary patterns, 2-dimensional Gabor filters, wavelet-based texture, and histogram of oriented gradients. We use these descriptors in conjunction with support vector machine or nearest neighbor classifiers to classify candidate regions, which in turn are selected using the maximally stable extremal regions algorithm. We present performance scores on the ASU-Mayo Clinic polyp database.
在这项工作中，我们对一组不同的先进图像描述符进行了性能比较，用于在结肠镜检查视频中自动检测息肉。这组描述符包括：局部二值模式、二维伽柏滤波器、基于小波的纹理以及方向梯度直方图。我们将这些描述符与支持向量机或最近邻分类器结合使用，对候选区域进行分类，而候选区域又是使用最大稳定极值区域算法来选择的。我们在亚利桑那州立大学 - 梅奥诊所息肉数据库上展示了性能得分。
