Gaussian Function Model for Task-Specific Evaluation in Medical Imaging: A Theoretical Investigation.
In medical image diagnosis, understanding image characteristics is crucial for selecting and optimizing imaging systems and advancing their development. Objective image quality assessments, based on specific diagnostic tasks, have become a standard in medical image analysis, bridging the gap between...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 39; no. 1; pp. 794 - 805 |
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| Format: | equations & formulas pictorial research tables/charts Journal Article |
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Springer Nature
Feb2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191694184&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191694184 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2026 vid: 39 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191694184 191694184 191694184 10.1007/s10278-025-01511-9 191694184 ppf: 794 ppct: 11 formats: tig: atl: Gaussian Function Model for Task-Specific Evaluation in Medical Imaging: A Theoretical Investigation. aug: au: Maruyama, Sho affil: https://ror.org/048xnxc75 Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan sug: subj: Diagnostic Imaging Methods Image Interpretation, Computer Assisted Models, Statistical Conceptual Framework Task Performance and Analysis Human Descriptive Statistics Image Processing, Computer Assisted Signal Processing, Computer Assisted Computer Simulation Tomography, X-Ray Computed Enhancement of Contrast Effect Observational Methods Quality Assessment ab: In medical image diagnosis, understanding image characteristics is crucial for selecting and optimizing imaging systems and advancing their development. Objective image quality assessments, based on specific diagnostic tasks, have become a standard in medical image analysis, bridging the gap between experimental observations and clinical applications. However, conventional task-based assessments often rely on ideal observer models that assume target signals have circular shapes with well-defined edges. This simplification rarely reflects the true complexity of lesion morphology, where edges exhibit variability. This study proposes a more practical approach by employing a Gaussian distribution to represent target signal shapes. This study explicitly derives the task function for Gaussian signals and evaluates the detectability index through simulations based on head computed tomography (CT) images with low-contrast lesions. Detectability indices were calculated for both circular and Gaussian signals using non-prewhitening and Hotelling observer models. The results demonstrate that Gaussian signals consistently exhibit lower detectability indices compared to circular signals, with differences becoming more pronounced for larger signal sizes. Simulated images closely resembling actual CT images confirm the validity of these calculations. These findings quantitatively clarify the influence of signal shape on detection performance, highlighting the limitations of conventional circular models. Thus, it provides a theoretical framework for task-based assessments in medical imaging, offering improved accuracy and clinical relevance for future advancements in the field. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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