Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study

• Published in: Virtual Reality & Intelligent Hardware Vol. 6; no. 1; pp. 71 - 81
• Main Authors: Mingjian Li, Younhyun Jung, Michael Fulham, Jinman Kim
• Format: Journal Article
• Language: English
• Published: KeAi Communications Co., Ltd 01.02.2024
• Subjects: DVR; Medical CBIR; Medical images; Retrieval; Volume visualization
• ISSN: 2096-5796
• DOI: 10.1016/j.vrih.2023.08.005

Background: A medical content-based image retrieval (CBIR) system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image. CBIR is widely used in evidence- based diagnosis, teaching, and research. Although the retrieval accuracy has largely improved, there has been limited development toward visualizing important image features that indicate the similarity of retrieved images. Despite the prevalence of3D volumetric data in medical imaging such as computed tomography (CT), current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images. Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information, including the size, shape, and spatial relations of multiple structures. This process is time-consuming and reliant on users’ experience. Methods: In this study, we proposed an importance-aware 3D volume visualization method. The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process. We then integrated the proposed visualization into a CBIR system, thereby complementing the 2D cross-sectional views for relevance feedback and further analyses. Results: Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography (PET- CT) images of a non-small cell lung cancer dataset.