The DOI matches its Crossref record; 178 of the 186 references it cites were found in a registry or an index.
Automatic breast ultrasound image segmentation: A survey
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In the citing paper
“However, few focus on medical US analysis, aside from one or two papers that examine specific tasks such as breast US image segmentation [55].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [55]
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · cited 1×
“Let w be the normal vector to the hyperplane, and { = (0, 1 − ( ∙ + ))} be slack variables for the soft margins; then the problem can be formulated as a constrained quadratic optimization problem [97] 1 + ‖ ‖ (30)”
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708 · cited 1×
“[115] Global thresholding ROI generation Otsu’s algorithm [130] Depended on image preprocessing.”
MethodFully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · cited 1×
“Deep learning: in the last several years, deep learning has demonstrated impressive performance for many tasks such as object recognition [158], image classification [159], semantic segmentation [160], facial expression recognition [163], speech recognition [164], medical applications [161, 170], etc.”
Background(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066 · cited 1×
“Graph-based approaches Graph-based approaches gain increasing popularity in BUS image segmentation because they offer several advantages: (1) they provide a simple way to organize task-related priors and image information in a unified framework; (2) they are flexible and suitable for expressing soft constraints betwee…”
BackgroundA decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949 · cited 1×
“Let = ( , ℰ ) be the subgraph corresponding to non-tumor regions, and = ( , ℰ ) be the subgraph corresponding to the tumor regions, where , ⊆ and ℰ , ℰ ⊆ ℰ, ∪ = , ∩ = ∅, and ℰ ∩ ℰ = ∅.”
Research works printed but not linked: 3
- J. Shan, A Fully Automatic Segmentation Method for Breast Ultrasound Images, Dissertation, USU, 2011.
- Kupinski, 1998, Automated seeded lesion segmentation on digital mammograms, IEEE Trans. Med. Imaging, 17, 510, 10.1109/42.730396 · DOI
- P.O. Glauner, Deep convolutional neural networks for smile recognition, arXiv preprint arXiv:1508.06535, 2015. · arXiv
Cited by
Sorted by citations
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
“However, few focus on medical US analysis, aside from one or two papers that examine specific tasks such as breast US image segmentation [55].”
BackgroundAAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images
Chen, G., Li, L., Dai, Y., Zhang, J., & Yap, M. H. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3226268 · Cited by 402
“The segmentation of breast ultrasound (BUS) images can help us characterize and localize breast tumors, which is one of the key steps in computer-aided diagnosis (CAD) [4].”
BackgroundBreast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
Byra, M., Jarosik, P., Szubert, A., Galperin, M. Y., Ojeda‐Fournier, H., Olson, L. K., O'Boyle, M. K., Comstock, C., & André, M. P. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102027 · Cited by 328
“However, automatic segmentation in US imaging is considered a challenging task due to relatively low US image contrast, speckle noise and large variations in breast mass sizes and shapes [3,4].”
BackgroundDeep learning in ultrasound imaging
van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316
“Similar applications of deep learning include automated identification and segmentation of tumors in breast ultrasound [106], [107], [108], localization of clinically relevant B-line artifacts in lung ultrasonography [109], and”
BackgroundSegmentation of breast ultrasound image with semantic classification of superpixels
Huang, Q., Huang, Y., Luo, Y., Yuan, F., & Li, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101657 · Cited by 222
Metadata
- Authors
- Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J.
- Affiliations
- University of Idaho (US)
- Harbin Institute of Technology (CN)
- Utah State University (US)
- Corresponding author
- Cheng, H.D.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 79
- Pages
- 340-355
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2963104294
- Cited by
- 284
- References
- 186
- Citations by year
- 2026: 20 · 2025: 47 · 2024: 41 · 2023: 37 · 2022: 31 · 2021: 42 · 2020: 36 · 2019: 20 · 2018: 10
- FWCI
- 13.51
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- public-domain
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- breast ultrasound, image segmentation, breast cancer, automatic segmentation, medical image analysis
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