Using Hausdorff Distance for New Medical Image Annotation

TL;DR

This paper proposes a method using Hausdorff distance to efficiently find similar medical images for annotation, reducing manual effort and ambiguity in the annotation process.

Contribution

It introduces a novel approach leveraging Hausdorff distance for image similarity measurement to improve medical image annotation efficiency.

Findings

Effective similarity computation between images using Hausdorff distance

Reduces manual annotation effort and ambiguity

Facilitates faster medical image annotation process

Abstract

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.