ArticleJournal of digital imaging2012
Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single- and multiphase contrast-enhanced CT images.
Article in Journal of digital imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet.Bioengineering (Basel, Switzerland) · 2023Article
- MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2021Article
- A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases.Medical & biological engineering & computing · 2020Article
- Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images.IEEE journal of biomedical and health informatics · 2019Article
- Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.GigaScience · 2019Review
- Overview on subjective similarity of images for content-based medical image retrieval.Radiological physics and technology · 2018Review
- 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
- SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs.PloS one · 2017Article
- Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images.International journal of biomedical imaging · 2017Article
- Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions.IEEE journal of biomedical and health informatics · 2016Article
- Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging.IEEE transactions on bio-medical engineering · 2016Article
- Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.Neurocomputing · 2016Article
- Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation.PloS one · 2016Article
- Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.PloS one · 2015Article
- 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
- Experimenting liver fibrosis diagnostic by two photon excitation microscopy and Bag-of-Features image classification.Scientific reports · 2014Article
- Automatic medical X-ray image classification using annotation.Journal of digital imaging · 2014Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.
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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.