Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
IDLab, Department of Electronics and Information Systems, Ghent University, Technologiepark-Zwijnaarde 19, 9052, Gent, Belgium.
Sci Rep. 2020 Dec 3;10(1):21061. doi: 10.1038/s41598-020-77933-y.
We propose a new method based on Topological Data Analysis (TDA) consisting of Topological Image Modification (TIM) and Topological Image Processing (TIP) for object detection. Through this newly introduced method, we artificially destruct irrelevant objects, and construct new objects with known topological properties in irrelevant regions of an image. This ensures that we are able to identify the important objects in relevant regions of the image. We do this by means of persistent homology, which allows us to simultaneously select appropriate thresholds, as well as the objects corresponding to these thresholds, and separate them from the noisy background of an image. This leads to a new image, processed in a completely unsupervised manner, from which one may more efficiently extract important objects. We demonstrate the usefulness of this proposed method for topological image processing through a case-study of unsupervised segmentation of the ISIC 2018 skin lesion images. Code for this project is available on https://bitbucket.org/ghentdatascience/topimgprocess .
我们提出了一种基于拓扑数据分析（TDA）的新方法，包括拓扑图像修改（TIM）和拓扑图像处理（TIP），用于目标检测。通过这种新引入的方法，我们人为地破坏不相关的物体，并在图像的不相关区域中用具有已知拓扑性质的新物体来构造。这确保了我们能够识别图像相关区域中的重要物体。我们通过持久同调来做到这一点，这使我们能够同时选择合适的阈值，以及与这些阈值对应的物体，并将它们与图像的噪声背景区分开来。这导致了一个新的图像，以完全无监督的方式进行处理，从中可以更有效地提取重要的物体。我们通过对 ISIC 2018 皮肤病变图像的无监督分割的案例研究，展示了这种拓扑图像处理方法的有用性。该项目的代码可在 https://bitbucket.org/ghentdatascience/topimgprocess 上获得。
