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Tissue classification for laparoscopic image understanding based on multispectral texture analysis

机译:基于多光谱纹理分析的组织分类以了解腹腔镜图像

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Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.
机译:术中组织分类是在计算机辅助微创手术中提供情境感知的可视化的先决条件之一。由于传统RGB医学图像中许多解剖结构难以区分,因此我们提出了一种基于多光谱图像斑块的分类方法。在一项全面的离体研究中,我们表明(1)与广泛使用的特征描述符结合使用时,多光谱成像数据优于RGB数据进行器官组织分类,并且(2)将组织纹理与反射光谱结合起来可提高分类性能。因此,多光谱组织分析可能会演变为计算机辅助腹腔镜检查中的一项关键技术。

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