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A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval

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Journal of Computer Science & Technology 2024年第2期39卷 320-335页

作者： younhyun jung Jim Kong 盛斌 Jinman Kim School of Computing Gachon UniversitySeongnam 13120Korea School of Computer Science The University of SydneySydney 2006Australia Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its *** DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of *** generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and *** at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain *** these approaches,a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the *** these approaches potentially reduce the workload to generate the TFs,they,however,require manual TF navigation of the knowledge database,as well as the likely fine tuning of the selected TF to suit the *** this work,we propose a TF design approach,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automatically navigate the knowledge *** of pre-designed TFs,our knowledge database contains volumes with SOI *** an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the *** approach largely reduces manual TF navigation and fine *** our CBR-TF approach,we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the *** the regional spatial semantics,we adopt a convolutional neural network to obtain high-level image feature *** the intensity profile,we extend the dynamic time warping technique to address subtle alignment

关键词： data retrieval direct volume rendering ray analysis transfer function

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

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虚拟现实与智能硬件（中英文） 2024年第1期6卷 71-81页

作者： Mingjian LI younhyun jung Michael FULHAM Jinman KIM School of Computer Science The University of SydneySydneyNSW 2006Australia School of Computing Gachon UniversitySeongnam 13120South Korea Department of Molecular Imaging Royal Prince Alfred HospitalSydneyNSW 2050Australia

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 *** is widely used in evidence-based diagnosis,teaching,and *** the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved *** 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 *** 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 *** process is time-consuming and reliant on users'*** In this study,we proposed an importance-aware 3D volume visualization *** rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval *** then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further *** 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

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