ArticleIEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society2011
A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback.
Article in IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.Scientific reports · 2026Article
- RMIS-Net: a fast medical image segmentation network based on multilayer perceptron.PeerJ. Computer science · 2025Article
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- Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features.Knowledge-based systems · 2023Article
- Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response.Frontiers in oncology · 2021Article
- Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey.Medical & biological engineering & computing · 2020Review
- A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases.Medical & biological engineering & computing · 2020Article
- Medical Image Analysis using Convolutional Neural Networks: A Review.Journal of medical systems · 2018Review
- Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.International journal of computer assisted radiology and surgery · 2016Article
- Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies.BMC bioinformatics · 2015Article
- Optimal query-based relevance feedback in medical image retrieval using score fusion-based classification.Journal of digital imaging · 2015Review
- Content-based histopathology image retrieval using CometCloud.BMC bioinformatics · 2014Article
- Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.IEEE transactions on bio-medical engineering · 2014Article
- Parallel content-based sub-image retrieval using hierarchical searching.Bioinformatics (Oxford, England) · 2014Article
- The application of similar image retrieval in electronic commerce.TheScientificWorldJournal · 2014Article
- Article
- Window classification of brain CT images in biomedical articles.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Pathology imaging informatics for quantitative analysis of whole-slide images.Journal of the American Medical Informatics Association : JAMIAReview
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3 authors.
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Abstract
This paper presents a classification-driven biomedical image retrieval framework based on image filtering and similarity fusion by employing supervised learning techniques. In this framework, the probabilistic outputs of a multiclass support vector machine (SVM) classifier as category prediction of query and database images are exploited at first to filter out irrelevant images, thereby reducing the search space for similarity matching. Images are classified at a global level according to their modalities based on different low-level, concept, and keypoint-based features. It is difficult to find a unique feature to compare images effectively for all types of queries. Hence, a query-specific adaptive linear combination of similarity matching approach is proposed by relying on the image classification and feedback information from users. Based on the prediction of a query image category, individual precomputed weights of different features are adjusted online. The prediction of the classifier may be inaccurate in some cases and a user might have a different semantic interpretation about retrieved images. Hence, the weights are finally determined by considering both precision and rank order information of each individual feature representation by considering top retrieved relevant images as judged by the users. As a result, the system can adapt itself to individual searches to produce query-specific results. Experiment is performed in a diverse collection of 5 000 biomedical images of different modalities, body parts, and orientations. It demonstrates the efficiency (about half computation time compared to search on entire collection) and effectiveness (about 10%-15% improvement in precision at each recall level) of the retrieval approach.
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