LITIS EA 4108, Université de Rouen, 76801 Saint-Etienne-du-Rouvray, France.
IEEE Trans Biomed Eng. 2012 Sep;59(9):2677-83. doi: 10.1109/TBME.2012.2204747.
Recently, the in vivo imaging of pulmonary alveoli was made possible thanks to confocal microscopy. For these images, we wish to aid the clinician by developing a computer-aided diagnosis system, able to discriminate between healthy and pathological subjects. The lack of expertise currently available on these images has first led us to choose a generic approach, based on pixel-value description of randomly extracted subwindows and decision tree ensemble for classification (extra-trees). In order to deal with the great complexity of our images, we adapt this method by introducing a texture-based description of the subwindows, based on local binary patterns. We show through our experimental protocol that this adaptation is a promising way to classify fibered confocal fluorescence microscopy images. In addition, we introduce a rejection mechanism on the classifier output to prevent nondetection errors.
最近，共聚焦显微镜使得对肺气泡的活体成像成为可能。对于这些图像，我们希望通过开发一个计算机辅助诊断系统来帮助临床医生，该系统能够区分健康和病理受试者。由于目前缺乏这些图像方面的专业知识，我们首先选择了一种基于随机提取子窗口的像素值描述和决策树集成分类（extra-trees）的通用方法。为了处理我们图像的巨大复杂性，我们通过基于局部二值模式的子窗口的纹理描述来适应这种方法。我们通过实验方案表明，这种适应方法是对纤维共焦荧光显微镜图像进行分类的一种很有前途的方法。此外，我们在分类器输出上引入了一种拒绝机制，以防止漏检错误。
