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

作　　者：Mingjian LI Younhyun JUNG Michael FULHAM Jinman KIM 

机构地区：[1]School of Computer Science,The University of Sydney,Sydney,NSW 2006,Australia [2]School of Computing,Gachon University,Seongnam 13120,South Korea [3]Department of Molecular Imaging,Royal Prince Alfred Hospital,Sydney,NSW 2050,Australia

出　　处：《虚拟现实与智能硬件（中英文）》2024年第1期71-81,共11页Virtual Reality & Intelligent Hardware

摘　　要：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 of 3D 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(PETCT)images of a non-small cell lung cancer dataset.

关 键 词：Volume visualization DVR Medical CBIR RETRIEVAL Medical images 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R318[自动化与计算机技术—计算机科学与技术]