IEEE J Biomed Health Inform. 2018 Jul;22(4):1278-1287. doi: 10.1109/JBHI.2017.2723014. Epub 2017 Jul 4.
Content-based image retrieval (CBIR) has been widely researched for histopathological images. It is challenging to retrieve contently similar regions from histopathological whole slide images (WSIs) for regions of interest (ROIs) in different size. In this paper, we propose a novel CBIR framework for database that consists of WSIs and size-scalable query ROIs. Each WSI in the database is encoded into a matrix of binary codes. When retrieving, a group of region proposals that have similar size with the query ROI are firstly located in the database through an efficient table-lookup approach. Then, these regions are ranked by a designed multi-binary-code-based similarity measurement. Finally, the top relevant regions and their locations in the WSIs as well as the corresponding diagnostic information are returned to assist pathologists. The effectiveness of the proposed framework is evaluated on a fine-annotated WSI database of epithelial breast tumors. The experimental results have proved that the proposed framework is effective for retrieval from database that consists of WSIs. Specifically, for query ROIs of 4096 4096 pixels, the retrieval precision of the top 20 return has reached 96% and the retrieval time is less than 1.5 s.
基于内容的图像检索（CBIR）已经在病理图像中得到了广泛的研究。对于不同大小的感兴趣区域（ROI），从病理全切片图像（WSI）中检索内容相似的区域是具有挑战性的。在本文中，我们提出了一种新的 CBIR 框架，该框架用于包含 WSI 和可缩放大小查询 ROI 的数据库。数据库中的每个 WSI 都被编码为二进制码矩阵。在检索时，通过一种有效的表格查找方法，首先在数据库中定位与查询 ROI 具有相似大小的一组区域建议。然后，通过设计的基于多二进制码的相似性度量对这些区域进行排序。最后，返回顶部相关区域及其在 WSI 中的位置以及相应的诊断信息，以协助病理学家。在细标注的上皮性乳腺癌 WSI 数据库上评估了所提出框架的有效性。实验结果证明了所提出的框架对于由 WSI 组成的数据库的检索是有效的。具体来说，对于 4096 4096 像素的查询 ROI，前 20 个返回项的检索精度达到 96%，检索时间小于 1.5 秒。
