ArticleJournal of digital imaging2009
Ontology of gaps in content-based image retrieval.
Article in Journal of digital imaging, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory.Scientific reports · 2025Article
- Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification.Scientific reports · 2024Article
- SPIE Medical Imaging 50th anniversary: history of the Picture Archiving and Communication Systems Conference.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases.Journal of digital imaging · 2020Article
- Workflow Lexicons in Healthcare: Validation of the SWIM Lexicon.Journal of digital imaging · 2017Article
- Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy.Journal of digital imaging · 2016Article
- Mining biomedical images towards valuable information retrieval in biomedical and life sciences.Database : the journal of biological databases and curation · 2016Review
- Designing user interfaces to enhance human interpretation of medical content-based image retrieval: application to PET-CT images.International journal of computer assisted radiology and surgery · 2013Article
- Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single- and multiphase contrast-enhanced CT images.Journal of digital imaging · 2012Article
- Content-based microscopic image retrieval system for multi-image queries.IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society · 2012Article
- Web-based bone age assessment by content-based image retrieval for case-based reasoning.International journal of computer assisted radiology and surgery · 2012Article
- Towards a repository for standardized medical image and signal case data annotated with ground truth.Journal of digital imaging · 2012Article
- Prototypes for content-based image retrieval in clinical practice.The open medical informatics journal · 2011Article
- Comparative analysis of two methods for wound bed area measurement.International wound journal · 2010Article
- Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography.Journal of digital imaging · 2010Article
- Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives.Algorithms · 2009Article
- SPIRS: a Web-based image retrieval system for large biomedical databases.International journal of medical informatics · 2009Article
- Content-Based Image Retrieval in Medicine: Retrospective Assessment, State of the Art, and Future Directions.International journal of healthcare information systems and informatics : official publication of the Information Resources Management Association · 2009Article
- Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.Journal of the American Medical Informatics Association : JAMIAArticle
- Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the "semantic gap." The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of "gaps" in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.