Using Hausdorff Distance for New Medical Image Annotation
This work addresses the challenge of efficient and consistent annotation for medical professionals, but it is incremental as it applies an existing distance metric to a specific domain.
The authors tackled the problem of repetitive and ambiguous medical image annotation by proposing an approach that uses Hausdorff distance to compute similarity between new and stored images, allowing users to assign existing annotations to new images based on similarity.
Medical images annotation is most of the time a repetitive hard task. Collecting old similar annotations and assigning them to new medical images may not only enhance the annotation process, but also reduce ambiguity caused by repetitive annotations. The goal of this work is to propose an approach based on Hausdorff distance able to compute similarity between a new medical image and old stored images. User has to choose then one of the similar images and annotations related to the selected one are assigned to the new one.