ArticleRadiology2010
Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results.
Article in Radiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 100 citations in OpenAlex.
- A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases.Medical & biological engineering & computing · 2020Article
- ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.Tomography (Ann Arbor, Mich.) · 2019Article
- Ontology-Based Approach for Liver Cancer Diagnosis and Treatment.Journal of digital imaging · 2019Article
- Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.Journal of digital imaging · 2018Review
- A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.International journal of computer assisted radiology and surgery · 2018Article
- Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images.International journal of computer assisted radiology and surgery · 2018Article
- Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions.IEEE journal of biomedical and health informatics · 2016Article
- Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.Neurocomputing · 2016Article
- Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2015Article
- A systematic approach for using DICOM structured reports in clinical processes: focus on breast cancer.Journal of digital imaging · 2015Review
- On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.Medical image analysis · 2014Article
- Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.IEEE transactions on medical imaging · 2014Article
- A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations.Journal of biomedical informatics · 2014Article
- Clinical evaluation of semi-automatic landmark-based lesion tracking software for CT-scans.Cancer imaging : the official publication of the International Cancer Imaging Society · 2014Article
- Automated tracking of quantitative assessments of tumor burden in clinical trials.Translational oncology · 2014Article
- Medical Image Retrieval: A Multimodal Approach.Cancer informatics · 2014Article
- Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data.Journal of digital imaging · 2013Review
- 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
- Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography.European radiology · 2013Article
- A similarity study of content-based image retrieval system for breast cancer using decision tree.Medical physics · 2013Article
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Authors and funding
10 authors at 4 institutions in 4 countries.
Funding
Abstract
purposeTo develop a system to facilitate the retrieval of radiologic images that contain similar-appearing lesions and to perform a preliminary evaluation of this system with a database of computed tomographic (CT) images of the liver and an external standard of image similarity. MATERIALS AND
methodsInstitutional review board approval was obtained for retrospective analysis of deidentified patient images. Thereafter, 30 portal venous phase CT images of the liver exhibiting one of three types of liver lesions (13 cysts, seven hemangiomas, 10 metastases) were selected. A radiologist used a controlled lexicon and a tool developed for complete and standardized description of lesions to identify and annotate each lesion with semantic features. In addition, this software automatically computed image features on the basis of image texture and boundary sharpness. Semantic and computer-generated features were weighted and combined into a feature vector representing each image. An independent reference standard was created for pairwise image similarity. This was used in a leave-one-out cross-validation to train weights that optimized the rankings of images in the database in terms of similarity to query images. Performance was evaluated by using precision-recall curves and normalized discounted cumulative gain (NDCG), a common measure for the usefulness of information retrieval.
resultsWhen used individually, groups of semantic, texture, and boundary features resulted in various levels of performance in retrieving relevant lesions. However, combining all features produced the best overall results. Mean precision was greater than 90% at all values of recall, and mean, best, and worst case retrieval accuracy was greater than 95%, 100%, and greater than 78%, respectively, with NDCG.
conclusionPreliminary assessment of this approach shows excellent retrieval results for three types of liver lesions visible on portal venous CT images, warranting continued development and validation in a larger and more comprehensive database.
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