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X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words
2011
IEEE Transactions on Medical Imaging
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The methodology is based on local patch representation of the image content, using a "bag of visual words" approach. ...
In addition to organ-level discrimination, we show an application to pathology-level categorization of chest x-ray data, the most popular examination in radiology. ...
X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words Uri Avni, Hayit Greenspan, Eli Konen , Michal Sharon, Jacob Goldberger Abstract-In this study we present ...
doi:10.1109/tmi.2010.2095026
pmid:21118769
fatcat:gseqpdx2zfc4rdkmyysal7dqfm
X-ray image categorization and retrieval using patch-based visualwords representation
2009
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
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In addition to organ-level discrimination, we show initial results of pathology-level categorization of chest x-ray data. ...
In a recent international competition the system was ranked as one of the top schemes in discriminating orientation and body regions in x-ray images, and in medical visual retrieval. ...
Categorization on the pathology level Image similarity-based categorization and retrieval becomes of clinical value once the task involves a diagnostic-level categorization, such as healthy vs pathology ...
doi:10.1109/isbi.2009.5193056
dblp:conf/isbi/AvniGSKG09
fatcat:uolonl2mhbcudb5xgzeqsyz67m
X-ray Categorization and Spatial Localization of Chest Pathologies
[chapter]
2011
Lecture Notes in Computer Science
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We show an application to pathology-level categorization of chest x-ray data, the most popular examination in radiology. ...
In this study we present an efficient image categorization system for medical image databases utilizing a local patch representation based on both content and location. ...
The authors wish to thank Dr. Eli Konen and Dr. ...
doi:10.1007/978-3-642-23626-6_25
fatcat:cnmmljhsevdshlbm4czezkyela
Biomedical image representation and classification using an entropy weighted probabilistic concept feature space
2014
Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations
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low level and visual word-based approaches. ...
Furthermore, importance of concepts is measured as Shannon entropy based on pixel values of image patches and used to refine the feature vector to overcome the limitation of the "TF-IDF"based weighting ...
We would like to thank the CLEF 3 organizers for making the database available for the experiments. ...
doi:10.1117/12.2043911
fatcat:mz4iznlqp5drjivrevs7u4am44
Table of Contents
2011
IEEE Transactions on Medical Imaging
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Tosun and C. Gunduz-Demir 721 X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words ................. ...
X. Wu, X. Dou, A. Wahle, and M. ...
doi:10.1109/tmi.2011.2119271
fatcat:klhzctp3onbhbogbthbp542pei
Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval
2015
Journal of Medical Imaging
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Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. ...
Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. ...
We thank the Image-CLEFmed 23 organizers for making the data set available for the experiments. ...
doi:10.1117/1.jmi.2.4.046502
pmid:26730398
pmcid:PMC4695659
fatcat:6yx6dxqzszgirhzyzfkhixaxle
Medical-Image Retrieval Based on Knowledge-Assisted Text and Image Indexing
2007
IEEE transactions on circuits and systems for video technology (Print)
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Two fusion approaches are developed to improve textual retrieval using the UMLS-based image indexing. ...
First, a simple fusion of the textual and visual retrieval approaches is proposed, improving significantly the retrieval results of both text and image retrieval. ...
Deselaers for the organization of image retrieval from medical collections. Finally, they would also like to thank T. Joachims for making his software available. ...
doi:10.1109/tcsvt.2007.897114
fatcat:s3phy77d3nhazfgubsyqshzaqu
Quantitative Morphometry for Osteochondral Tissues Using Second Harmonic Generation Microscopy and Image Texture Information
2018
Scientific Reports
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In this study, we addressed this issue by employing an approach based on texture analysis. Image texture analysis using the gray level co-occurrence matrix was explored to extract image features. ...
X-rays and CT provide useful diagnostic information by detecting morphological changes in bone and calcified tissues, but these techniques are limited to detecting the late stages of OA progression. ...
Acknowledgements The authors sincerely thank Drs. Yusuke Oshima, Sota Takanezawa and Ryosuke Kawakami of Ehime University for their helpful comments on this work. ...
doi:10.1038/s41598-018-21005-9
pmid:29434299
pmcid:PMC5809560
fatcat:3ygjba5ubzem5gtm2ierjh7pvi
Towards the improvement of textual anatomy image classification using image local features
2011
Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval - MMAR '11
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Last, a hybrid scheme on the results from the textual and visual methods is applied to achieved further improvement of the classification results. ...
First, a mutual information (MI) based filter is applied to select a set of terms with top MI scores for each anatomical class and help reduce the noise existing in the raw text. ...
Patch-based BoW models are used to classify endomicroscopic images [2] and breast tissue density in mammograms [4] . They are also effective in X-ray image classification [6, 3] . ...
doi:10.1145/2072545.2072551
fatcat:nhlmxwffijcqrp2jkjnn53shmm
The State of the Art of Medical Imaging Technology: from Creation to Archive and Back
2011
Open Medical Informatics Journal
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Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. ...
The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical ...
ACKNOWLEDGEMENT The authors would like to acknowledge JISC (www.jisc.ac.uk) at the UK for their continuously financial support for the two projects of MIRAGE and MIRAGE 2011, and thank European Commission ...
doi:10.2174/1874431101105010073
pmid:21915232
pmcid:PMC3170936
fatcat:jdbktuw4obg6jf35zrvoq47poq
A query expansion framework in image retrieval domain based on local and global analysis
2011
Information Processing & Management
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In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. ...
We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. ...
The authors would like to thank the CLEF organizers 3 for making the databases available for the experiments. ...
doi:10.1016/j.ipm.2010.12.001
pmid:21822350
pmcid:PMC3150552
fatcat:ow4wnbo7bfcvbd43svf7lh36c4
SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs
2017
PLoS ONE
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The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. ...
Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares ...
The authors also thank the editor and anonymous reviewers for their prolific and highly constructive comments and suggestions which improved our manuscript significantly. ...
doi:10.1371/journal.pone.0181707
pmid:28771497
pmcid:PMC5542646
fatcat:vseqnumxhncafpz2gmpneb6opi
Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
[article]
2021
arXiv
pre-print
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The similarity between the query case and retrieved similar cases is determined by visual features extracted from pathologically abnormal regions. ...
Extensive experiments on two medical image datasets demonstrate that Y-Net can alleviate the ambiguity of pathologically abnormal regions and its retrieval performance outperforms the state-of-the-art ...
Recently, many existing works on instance-level retrieval typically extracted visual features by using convolutional neural networks (CNN) to prevent the visual features unique to an instance from drowning ...
arXiv:2105.08872v1
fatcat:5ga4eudoorchja5xwswm453jje
On the Automatic Generation of Medical Imaging Reports
[article]
2017
arXiv
pre-print
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Other Versions
Medical imaging is widely used in clinical practice for diagnosis and treatment. ...
We demonstrate the effectiveness of the proposed methods on two publicly available datasets. ...
[16] adopt a CNN-RNN based framework to predict tags of chest x-ray images. ...
arXiv:1711.08195v2
fatcat:plcxiky6vndsli2mown7oqh3ee
Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and the squamous carcinoma), using thousands ...
., cells) accurately, using hierarchical voting and repulsive active contour. ...
It uses Gaussian mixture models (GMM) and informationtheoretic image matching via the Kullback-Leibler (K-L) measure to match and categorize X-ray images by body regions. Song et al. ...
doi:10.1109/cvpr.2015.7299174
dblp:conf/cvpr/ZhangSYZ15
fatcat:r3t6fuu4yvgphk4omnmvyxnzni
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