Extending UML for Conceptual Modeling of Annotation of Medical Images

TL;DR

This paper proposes an extension to UML for better conceptual modeling of medical image annotation, addressing the specific needs of the medical domain to improve image analysis and semantic understanding.

Contribution

It introduces a new UML profile tailored for medical image annotation, enhancing the modeling capabilities for this specialized domain.

Findings

A new UML profile for medical image annotation is defined.

The extended UML improves the modeling of medical image annotation processes.

The approach facilitates better integration of medical domain specifics into UML models.

Abstract

Imaging has occupied a huge role in the management of patients, whether hospitalized or not. Depending on the patients clinical problem, a variety of imaging modalities were available for use. This gave birth of the annotation of medical image process. The annotation is intended to image analysis and solve the problem of semantic gap. The reason for image annotation is due to increase in acquisition of images. Physicians and radiologists feel better while using annotation techniques for faster remedy in surgery and medicine due to the following reasons: giving details to the patients, searching the present and past records from the larger databases, and giving solutions to them in a faster and more accurate way. However, classical conceptual modeling does not incorporate the specificity of medical domain specially the annotation of medical image. The design phase is the most important activity in the successful building of annotation process. For this reason, we focus in this paper on presenting the conceptual modeling of the annotation of medical image by defining a new profile using the StarUML extensibility mechanism.