Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study
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 im...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-02-01
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| Series: | Virtual Reality & Intelligent Hardware |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096579623000566 |
| _version_ | 1797292953394216960 |
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| author | Mingjian Li Younhyun Jung Michael Fulham Jinman Kim |
| author_facet | Mingjian Li Younhyun Jung Michael Fulham Jinman Kim |
| author_sort | Mingjian Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T20:04:54Z |
| format | Article |
| id | doaj.art-4327908134ca4a648c45b38bdfcb4d2d |
| institution | Directory Open Access Journal |
| issn | 2096-5796 |
| language | English |
| last_indexed | 2024-03-07T20:04:54Z |
| publishDate | 2024-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Virtual Reality & Intelligent Hardware |
| spelling | doaj.art-4327908134ca4a648c45b38bdfcb4d2d2024-02-28T05:13:14ZengKeAi Communications Co., Ltd.Virtual Reality & Intelligent Hardware2096-57962024-02-01617181Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary studyMingjian Li0Younhyun Jung1Michael Fulham2Jinman Kim3School of Computer Science, The University of Sydney, Sydney, NSW 2006, AustraliaSchool of Computing, Gachon University, Seongnam 13120, South KoreaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia; Department ofMolecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, AustraliaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2096579623000566Volume visualizationDVRMedical CBIRRetrievalMedical images |
| spellingShingle | Mingjian Li Younhyun Jung Michael Fulham Jinman Kim Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study Virtual Reality & Intelligent Hardware Volume visualization DVR Medical CBIR Retrieval Medical images |
| title | Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study |
| title_full | Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study |
| title_fullStr | Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study |
| title_full_unstemmed | Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study |
| title_short | Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study |
| title_sort | importance aware 3d volume visualization for medical content based image retrieval a preliminary study |
| topic | Volume visualization DVR Medical CBIR Retrieval Medical images |
| url | http://www.sciencedirect.com/science/article/pii/S2096579623000566 |
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