Instituto de Telecomunicações, Department of Computer Science, Faculdade de Ciłncias da Universidade do Porto, Porto, Portugal.
IEEE Trans Biomed Eng. 2012 Oct;59(10):2893-904. doi: 10.1109/TBME.2012.2212440. Epub 2012 Aug 8.
Automatic classification of lesions for gastroenterology imaging scenarios poses novel challenges to computer-assisted decision systems, which are mostly attributed to the dynamics of the image acquisition conditions. Such challenges demand that automatic systems are able to give robust characterizations of tissues irrespective of camera rotation, zoom, and illumination gradients when viewing the inner surface of the gastrointestinal tract. In this paper, we study the invariance properties of Gabor filters and propose a novel descriptor, the autocorrelation Gabor features (AGF). We show that our proposed AGF is invariant to scale, rotation, and illumination changes in the images. We integrate these new features in a texton framework (Texton-AGF) to classify images from two complementary gastroenterology imaging scenarios (chromoendoscopy and narrow-band imaging) broadly into three different groups: normal, precancerous, and cancerous. Results show that they compare favorably to using state-of-the-art texture descriptors for both imaging modalities.
用于胃肠成像场景的病变自动分类对计算机辅助决策系统提出了新的挑战，这主要归因于图像采集条件的动态性。这些挑战要求自动系统能够在观察胃肠道内表面时，无论相机旋转、缩放和照明梯度如何，都能够对组织进行稳健的描述。在本文中，我们研究了 Gabor 滤波器的不变性，并提出了一种新的描述符，自相关 Gabor 特征（AGF）。我们证明了我们提出的 AGF 对图像中的尺度、旋转和光照变化具有不变性。我们将这些新特征集成到一个纹理元框架（Texton-AGF）中，以便将来自两种互补的胃肠成像场景（染色内镜和窄带成像）的图像广泛地分为正常、癌前和癌症三组。结果表明，对于这两种成像模式，它们的性能都优于使用最先进的纹理描述符。
