Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
Al-Ahli Hospital, Doha, Qatar.
PLoS One. 2018 Jun 6;13(6):e0197431. doi: 10.1371/journal.pone.0197431. eCollection 2018.
The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.
光谱成像技术已被证明比 RGB 图像提供更多的鉴别信息，并已被提出用于一系列问题。有许多研究表明，它在分析组织病理学图像以进行异常检测方面具有潜力，但之前的研究也存在差异。许多基于多光谱的方法已被提出用于组织病理学图像，但使用整个多光谱立方体与使用子带或单个带的重要性仍存在争议。我们使用单个带和不同的带子集进行了全面分析，以确定光谱信息在确定结直肠图像异常方面的有效性。我们的多光谱结直肠数据集由四个类别组成，每个类别除了视觉光谱带外，还代表红外光谱带。我们通过使用空间和光谱信息分层异常来对光谱成像进行分析。对于我们的实验，我们使用了纹理描述符与集成分类方法的组合，该方法在我们的数据集上表现最佳。我们将我们的方法应用于另一个数据集，并获得了与使用最先进的方法和基于卷积神经网络的方法获得的结果相当的结果。此外，我们还探讨了带的数量与问题复杂性之间的关系，发现对于复杂任务，需要更多的带才能提高性能。我们的结果通过在纳入红外表示时提高分类准确性（提高 6%），证明了红外和可见光谱之间的协同作用。我们还强调了如何为评估基于组织病理学图像的方法将数据集划分为训练集和测试集的重要性，这在以前关于多光谱组织病理学图像的研究中没有被考虑到。
