The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words
Abstract
In this study we present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology is based on local patch representation of the image content, using a "bag of visual words" approach. We explore the effects of various parameters on system performance, and show best results using dense sampling of simple features with spatial content, and a nonlinear kernel-based support vector machine (SVM) classifier. In a recent international competition the system was ranked first in discriminating orientation and body regions in X-ray images. 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. The system discriminates between healthy and pathological cases, and is also shown to successfully identify specific pathologies in a set of chest radiographs taken from a routine hospital examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnostics.
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Cites
Object recognition from local scale-invariant features
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Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Sampling Strategies for Bag-of-Features Image Classification
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Cited by
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879
“…more robust and discriminative in terms of medical image classification and retrieval [9 – 12], such as classifying X-ray images on the organ and pathology levels [9], breast tissue density classification in mammograms [10], and content-based retrieval of liver lesions [11] and brain tumors [12].”
BackgroundAn Efficient Deep Learning Approach to Pneumonia Classification in Healthcare
Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/4180949 · Cited by 616
Identifying pneumonia in chest X-rays: A deep learning approach
Jaiswal, A. K., Tiwari, P., Kumar, S., Gupta, D., Khanna, A., & Rodrigues, J. J. P. C. (2019) · Measurement · doi:10.1016/j.measurement.2019.05.076 · Cited by 507
Chest pathology detection using deep learning with non-medical training
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163871 · Cited by 392
Self-citationLearning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Shin, H.-C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., & Summers, R. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.274 · Cited by 373
Metadata
- Authors
- Avni, U., Greenspan, H., Konen, E., Sharon, M., & Goldberger, J.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2011-03
- Volume
- 30
- Issue
- 3
- Pages
- 733-746
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2133533561
- Cited by
- 195
- References
- 5
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Avni, U., Greenspan, H., Konen, E., Sharon, M., & Goldberger, J. (2011). X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2010.2095026
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