21.7.2.7 Mammograms, Breast Cancer, Ultrasound

Chapter Contents (Back)
Mammograms. Ultrasound. Medical, Applications.

Goldberg, V.[Victor],
Method and apparatus for diagnosis of breast tumors,
US_Patent5,260,871, Nov 9, 1993
WWW Link. BibRef 9311

Fatemi, M., Wold, L.E., Alizad, A., Greenleaf, J.F.,
Vibro-acoustic tissue mammography,
MedImg(21), No. 1, January 2002, pp. 1-8.
IEEE Top Reference. 0202
BibRef

Urban, M.W., Silva, G.T., Fatemi, M., Greenleaf, J.F.,
Multifrequency Vibro-Acoustography,
MedImg(25), No. 10, October 2006, pp. 1284-1295.
IEEE DOI 0609
BibRef

Joo, S., Yang, Y.S., Moon, W.K., Kim, H.C.,
Computer-Aided Diagnosis of Solid Breast Nodules: Use of an Artificial Neural Network Based on Multiple Sonographic Features,
MedImg(23), No. 10, October 2004, pp. 1292-1300.
IEEE Abstract. 0410
BibRef

Alizad, A., Fatemi, M., Wold, L.E., Greenleaf, J.F.,
Performance of Vibro-Acoustography in Detecting Microcalcifications in Excised Human Breast Tissue: A Study of 74 Tissue Samples,
MedImg(23), No. 3, March 2004, pp. 307-312.
IEEE Abstract. 0403
BibRef

Aguilo, M.A., Aquino, W., Brigham, J.C., Fatemi, M.,
An Inverse Problem Approach for Elasticity Imaging through Vibroacoustics,
MedImg(29), No. 4, April 2010, pp. 1012-1021.
IEEE DOI 1003
BibRef
And: Corrections: MedImg(29), No. 6, June 2010, pp. 1331-1331.
IEEE DOI 1007
BibRef

Madabhushi, A., Metaxas, D.N.,
Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions,
MedImg(22), No. 2, February 2003, pp. 155-169.
IEEE Top Reference. 0304
BibRef

Gefen, S., Tretiak, O.J., Piccoli, C.W., Donohue, K.D., Petropulu, A.P., Shankar, P.M., Dumane, V.A., Huang, L., Kutay, M.A., Genis, V., Forsberg, F., Reid, J.M., Goldberg, B.B.,
ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis,
MedImg(22), No. 2, February 2003, pp. 170-177.
IEEE Top Reference. 0304
BibRef

Oelze, M.L., O'Brien, W.D., Blue, J.P., Zachary, J.F.,
Differentiation and Characterization of Rat Mammary Fibroadenomas and 4T1 Mouse Carcinomas Using Quantitative Ultrasound Imaging,
MedImg(23), No. 6, June 2004, pp. 764-771.
IEEE Abstract. 0406
BibRef

Abbey, C.K., Zemp, R.J., Liu, J., Lindfors, K.K., Insana, M.F.,
Observer Efficiency in Discrimination Tasks Simulating Malignant and Benign Breast Lesions Imaged With Ultrasound,
MedImg(25), No. 2, February 2006, pp. 198-209.
IEEE DOI 0602
BibRef

Alemán-Flores, M.[Miguel], Álvarez-León, L.[Luis], Caselles, V.[Vicent],
Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation,
JMIV(28), No. 1, May 2007, pp. 81-97.
Springer DOI 0710
BibRef

Alemán-Flores, M.[Miguel], Álvarez-León, L.[Luis],
Video Segmentation Through Multiscale Texture Analysis,
ICIAR04(II: 339-346).
Springer DOI 0409
BibRef
Earlier:
Texture Classification through Multiscale Orientation Histogram Analysis,
ScaleSpace03(479-493).
Springer DOI 0310
BibRef

Tang, A.M., Kacher, D.F., Lam, E.Y., Wong, K.K., Jolesz, F.A., Yang, E.S.,
Simultaneous Ultrasound and MRI System for Breast Biopsy: Compatibility Assessment and Demonstration in a Dual Modality Phantom,
MedImg(27), No. 2, February 2008, pp. 247-254.
IEEE DOI 0802
BibRef

Irwin, M.R., Downey, D.B., Gardi, L., Fenster, A.,
Registered 3-D Ultrasound and Digital Stereotactic Mammography for Breast Biopsy Guidance,
MedImg(27), No. 3, March 2008, pp. 391-401.
IEEE DOI 0803
BibRef

Drukker, K., Sennett, C.A., Giger, M.L.,
Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound,
MedImg(28), No. 1, January 2009, pp. 122-128.
IEEE DOI 0901
BibRef

Yeh, C.K.[Chih-Kuang], Chen, Y.S.[Yung-Sheng], Fan, W.C.[Wei-Che], Liao, Y.Y.[Yin-Yin],
A disk expansion segmentation method for ultrasonic breast lesions,
PR(42), No. 5, May 2009, pp. 596-606.
Elsevier DOI 0902
Speckle noise; Lesion contour; Disk expansion method; Computer-aided diagnosis (CAD) BibRef

Liu, B.[Bo], Cheng, H.D., Huang, J.H.[Jian-Hua], Tian, J.W.[Jia-Wei], Tang, X.L.[Xiang-Long], Liu, J.[Jiafeng],
Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images,
PR(43), No. 1, January 2010, pp. 280-298.
Elsevier DOI 0909
Texture classification; Support vector machine (SVM); Computer aided diagnosis (CAD); Breast ultrasound (BUS) imaging BibRef

Xian, M.[Min], Cheng, H.D., Zhang, Y.T.[Ying-Tao],
A Fully Automatic Breast Ultrasound Image Segmentation Approach Based on Neutro-Connectedness,
ICPR14(2495-2500)
IEEE DOI 1412
Pattern recognition BibRef

Liu, B.[Bo], Cheng, H.D., Huang, J.H.[Jian-Hua], Tian, J.W.[Jia-Wei], Tang, X.L.[Xiang-Long], Liu, J.F.[Jia-Feng],
Probability density difference-based active contour for ultrasound image segmentation,
PR(43), No. 6, June 2010, pp. 2028-2042.
Elsevier DOI 1003
Image segmentation; Active contour; Probability difference; Level set; Breast ultrasound (bus) imaging BibRef

Cheng, H.D., Shan, J.[Juan], Ju, W.[Wen], Guo, Y.H.[Yan-Hui], Zhang, L.[Ling],
Automated breast cancer detection and classification using ultrasound images: A survey,
PR(43), No. 1, January 2010, pp. 299-317.
Elsevier DOI 0909
computer-aided diagnosis; Automated breast cancer detection and classification; Ultrasound imaging; Feature extraction and selection; Classifiers BibRef

Takemura, A., Shimizu, A., Hamamoto, K.,
Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection,
MedImg(29), No. 3, March 2010, pp. 598-609.
IEEE DOI 1003
BibRef

Rodtook, A.[Annupan], Makhanov, S.S.[Stanislav S.],
Continuous force field analysis for generalized gradient vector flow field,
PR(43), No. 10, October 2010, pp. 3522-3538.
Elsevier DOI 1007
Gradient vector flow; Snakes; Segmentation; Ultrasound image; Breast tumor BibRef

Rodtook, A.[Annupan], Makhanov, S.S.[Stanislav S.],
Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer,
JVCIR(24), No. 8, 2013, pp. 1414-1430.
Elsevier DOI 1312
Active contours BibRef

Chucherd, S.[Sirikan], Rodtook, A.[Annupan], Makhanov, S.S.[Stanislav S.],
Phase Portrait Analysis for Multiresolution Generalized Gradient Vector Flow,
IEICE(E93-D), No. 10, October 2010, pp. 2822-2835.
WWW Link. 1011
BibRef

Tan, T., Platel, B., Huisman, H., Sanchez, C.I., Mus, R., Karssemeijer, N.,
Computer-Aided Lesion Diagnosis in Automated 3-D Breast Ultrasound Using Coronal Spiculation,
MedImg(31), No. 5, May 2012, pp. 1034-1042.
IEEE DOI 1202
BibRef

Tan, T., Platel, B., Mus, R., Tabar, L., Mann, R.M., Karssemeijer, N.,
Computer-Aided Detection of Cancer in Automated 3-D Breast Ultrasound,
MedImg(32), No. 9, 2013, pp. 1698-1706.
IEEE DOI 1309
Automated 3-D breast ultrasound BibRef

Gomez, W., Pereira, W.C.A., Infantosi, A.F.C.,
Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound,
MedImg(31), No. 10, October 2012, pp. 1889-1899.
IEEE DOI 1210
BibRef

Mehdizadeh, S., Austeng, A., Johansen, T.F., Holm, S.,
Eigenspace Based Minimum Variance Beamforming Applied to Ultrasound Imaging of Acoustically Hard Tissues,
MedImg(31), No. 10, October 2012, pp. 1912-1921.
IEEE DOI 1210
BibRef

Moon, W.K.[Woo Kyung], Shen, Y.W.[Yi-Wei], Bae, M.S.[Min Sun], Huang, C.S.[Chiun-Sheng], Chen, J.H.[Jeon-Hor], Chang, R.F.[Ruey-Feng],
Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images,
MedImg(32), No. 7, 2013, pp. 1191-1200.
IEEE DOI 1307
Hessian matrices BibRef

Yang, M.C., Moon, W.K., Wang, Y.C.F., Bae, M.S., Huang, C.S., Chen, J.H., Chang, R.F.,
Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis,
MedImg(32), No. 12, 2013, pp. 2262-2273.
IEEE DOI 1312
Databases BibRef

Lo, C.M., Chen, R.T., Chang, Y.C., Yang, Y.W., Hung, M.J., Huang, C.S., Chang, R.F.,
Multi-Dimensional Tumor Detection in Automated Whole Breast Ultrasound Using Topographic Watershed,
MedImg(33), No. 7, July 2014, pp. 1503-1511.
IEEE DOI 1407
Breast BibRef

Xian, M.[Min], Zhang, Y.T.[Ying-Tao], Cheng, H.D.,
Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains,
PR(48), No. 2, 2015, pp. 485-497.
Elsevier DOI 1411
Breast ultrasound (BUS) images BibRef

Xian, M.[Min], Huang, J.H.[Jian-Hua], Zhang, Y.T.[Ying-Tao], Tang, X.L.[Xiang-Long],
Multiple-domain knowledge based MRF model for tumor segmentation in breast ultrasound images,
ICIP12(2021-2024).
IEEE DOI 1302
BibRef

Flores, W.G.[Wilfrido Gómez], de Albuquerque Pereira, W.C.[Wagner Coelho], Infantosi, A.F.C.[Antonio Fernando Catelli],
Improving classification performance of breast lesions on ultrasonography,
PR(48), No. 4, 2015, pp. 1125-1136.
Elsevier DOI 1502
Ultrasonography BibRef

Uniyal, N., Eskandari, H., Abolmaesumi, P., Sojoudi, S., Gordon, P., Warren, L., Rohling, R.N., Salcudean, S.E., Moradi, M.,
Ultrasound RF Time Series for Classification of Breast Lesions,
MedImg(34), No. 2, February 2015, pp. 652-661.
IEEE DOI 1502
Biopsy BibRef

Samei, G., Goksel, O., Lobo, J., Mohareri, O., Black, P., Rohling, R.N., Salcudean, S.E.,
Real-Time FEM-Based Registration of 3-D to 2.5-D Transrectal Ultrasound Images,
MedImg(37), No. 8, August 2018, pp. 1877-1886.
IEEE DOI 1808
Strain, Surgery, Magnetic resonance imaging, image registration BibRef

Gangeh, M.J., Tadayyon, H., Sannachi, L., Sadeghi-Naini, A., Tran, W.T., Czarnota, G.J.,
Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer,
MedImg(35), No. 3, March 2016, pp. 778-790.
IEEE DOI 1603
Breast cancer BibRef

Xian, M.[Min], Zhang, Y.T.[Ying-Tao], Cheng, H.D., Xu, F.[Fei], Zhang, B.[Boyu], Ding, J.[Jianrui],
Automatic breast ultrasound image segmentation: A survey,
PR(79), 2018, pp. 340-355.
Elsevier DOI 1804
Survey, Ultrasound. Breast ultrasound (BUS) images, Breast cancer, Segmentation, Benchmark, Early detection, Computer-aided diagnosis (CAD) BibRef

Shao, H.Y.[Hao-Yang], Zhang, Y.T.[Ying-Tao], Xian, M.[Min], Cheng, H.D., Xu, F.[Fei], Ding, J.[Jianrui],
A saliency model for automated tumor detection in breast ultrasound images,
ICIP15(1424-1428)
IEEE DOI 1512
Breast ultrasound (BUS) images BibRef

Kozegar, E., Soryani, M., Behnam, H., Salamati, M., Tan, T.,
Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model,
MedImg(37), No. 4, April 2018, pp. 918-928.
IEEE DOI 1804
Breast cancer, Deformable models, Image segmentation, Shape, Ultrasonic imaging, Ultrasound, breast, mass, segmentation BibRef

Rodtook, A.[Annupan], Kirimasthong, K.[Khwunta], Lohitvisate, W.[Wanrudee], Makhanov, S.S.[Stanislav S.],
Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities,
PR(79), 2018, pp. 172-182.
Elsevier DOI 1804
Ultrasound image segmentation BibRef

Lal, M.[Madan], Kaur, L.[Lakhwinder], Gupta, S.[Savita],
Modified spatial neutrosophic clustering technique for boundary extraction of tumours in B-mode BUS images,
IET-IPR(12), No. 8, August 2018, pp. 1338-1344.
DOI Link 1808
BibRef

Panigrahi, L.[Lipismita], Verma, K.[Kesari], Singh, B.K.[Bikesh Kumar],
Hybrid segmentation method based on multi-scale Gaussian kernel fuzzy clustering with spatial bias correction and region-scalable fitting for breast US images,
IET-CV(12), No. 8, December 2018, pp. 1067-1077.
DOI Link 1812
BibRef

Chiang, T., Huang, Y., Chen, R., Huang, C., Chang, R.,
Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation,
MedImg(38), No. 1, January 2019, pp. 240-249.
IEEE DOI 1901
Lesions, Feature extraction, Ultrasonic imaging, Image edge detection, Breast cancer, convolutional neural networks BibRef

Shin, S.Y., Lee, S., Yun, I.D., Kim, S.M., Lee, K.M.,
Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images,
MedImg(38), No. 3, March 2019, pp. 762-774.
IEEE DOI 1903
Training, Cancer, Ultrasonic imaging, Machine learning, Breast, Image segmentation, Lesions, Breast ultrasound, weakly supervised learning BibRef

Uzun, B.[Banu], Yücel, H.[Hazel],
An Inverse Source Problem Connected with Thermoacoustic Imaging in Multi-layer Planar Medium,
JMIV(61), No. 6, July 2019, pp. 874-884.
Springer DOI 1907
BibRef

Wang, Y., Wang, N., Xu, M., Yu, J., Qin, C., Luo, X., Yang, X., Wang, T., Li, A., Ni, D.,
Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound,
MedImg(39), No. 4, April 2020, pp. 866-876.
IEEE DOI 2004
Cancer, Sensitivity, Breast, Ultrasonic imaging, Lesions, Biomedical imaging, threshold loss BibRef

Nikolaev, A.V., de Jong, L., Weijers, G., Groenhuis, V., Mann, R.M., Siepel, F.J., Maris, B.M., Stramigioli, S., Hansen, H.H.G., de Korte, C.L.,
Quantitative Evaluation of an Automated Cone-Based Breast Ultrasound Scanner for MRI-3D US Image Fusion,
MedImg(40), No. 4, April 2021, pp. 1229-1239.
IEEE DOI 2104
Breast, Ultrasonic imaging, Lesions, Magnetic resonance imaging, Imaging, Biomedical imaging, Breast, 3D US BibRef

Chen, C.[Chen], Wang, Y.[Yong], Niu, J.W.[Jian-Wei], Liu, X.F.[Xue-Feng], Li, Q.F.[Qing-Feng], Gong, X.[Xuantong],
Domain Knowledge Powered Deep Learning for Breast Cancer Diagnosis Based on Contrast-Enhanced Ultrasound Videos,
MedImg(40), No. 9, September 2021, pp. 2439-2451.
IEEE DOI 2109
Videos, Solid modeling, Tumors, Brightness, Deep learning, Feature extraction, Breast cancer, 3D convolution, domain knowledge BibRef

Ning, Z.Y.[Zhen-Yuan], Zhong, S.Z.[Sheng-Zhou], Feng, Q.J.[Qian-Jin], Chen, W.F.[Wu-Fan], Zhang, Y.[Yu],
SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image,
MedImg(41), No. 2, February 2022, pp. 476-490.
IEEE DOI 2202
Lesions, Image segmentation, Streaming media, Breast, Ultrasonic imaging, Image edge detection, Shape, deep learning BibRef

Luo, Y.Z.[Yao-Zhong], Huang, Q.H.[Qing-Hua], Li, X.L.[Xue-Long],
Segmentation information with attention integration for classification of breast tumor in ultrasound image,
PR(124), 2022, pp. 108427.
Elsevier DOI 2203
Computer-aided diagnosis, Breast ultrasound, Deep convolution neural network, Feature combination BibRef

Kang, J.[Jinbum], Han, K.[Kanghee], Song, I.[Ilseob], Kim, K.S.[Kang-Sik], Jang, W.S.[Won Seuk], Kim, M.J.[Min Jung], Yoo, Y.[Yangmo],
Real-Time Ultrasound Detection of Breast Microcalcifications Using Multifocus Twinkling Artifact Imaging,
MedImg(41), No. 5, May 2022, pp. 1300-1308.
IEEE DOI 2205
Wires, Imaging, Acoustics, Surface roughness, Rough surfaces, Real-time systems, Doppler effect, Breast microcalcification, biopsy guidance BibRef

Zhang, H.J.[Hui-Juan], Bo, W.[Wei], Wang, D.[Depeng], DiSpirito, A.[Anthony], Huang, C.[Chuqin], Nyayapathi, N.[Nikhila], Zheng, E.[Emily], Vu, T.[Tri], Gong, Y.Y.[Yi-Yang], Yao, J.J.[Jun-Jie], Xu, W.Y.[Wen-Yao], Xia, J.[Jun],
Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography,
MedImg(41), No. 5, May 2022, pp. 1279-1288.
IEEE DOI 2205
Image resolution, Transducers, Imaging, Convolution, Training, Image reconstruction, Breast imaging, resolution enhancement BibRef

Mishra, A.K.[Arnab Kumar], Roy, P.[Pinki], Bandyopadhyay, S.[Sivaji], Das, S.K.[Sujit Kumar],
CR-SSL: A closely related self-supervised learning based approach for improving breast ultrasound tumor segmentation,
IJIST(32), No. 4, 2022, pp. 1209-1220.
DOI Link 2207
breast cancer, breast ultrasound, deep learning, self-supervised learning BibRef

Atrey, K.[Kushangi], Singh, B.K.[Bikesh Kumar], Roy, A.[Abhijit], Bodhey, N.K.[Narendra Kuber],
Real-time automated segmentation of breast lesions using CNN-based deep learning paradigm: Investigation on mammogram and ultrasound,
IJIST(32), No. 4, 2022, pp. 1084-1100.
DOI Link 2207
computer-aided segmentation, convolutional neural network, deep learning, mammogram, ultrasound BibRef

Fei, X.Y.[Xiao-Yan], Zhou, S.C.[Shi-Chong], Han, X.M.[Xiang-Min], Wang, J.[Jun], Ying, S.H.[Shi-Hui], Chang, C.[Cai], Zhou, W.J.[Wei-Jun], Shi, J.[Jun],
Doubly supervised parameter transfer classifier for diagnosis of breast cancer with imbalanced ultrasound imaging modalities,
PR(120), 2021, pp. 108139.
Elsevier DOI 2109
Doubly supervised parameter transfer classifier, Support vector machine plus, Breast cancer BibRef

Han, X.M.[Xiang-Min], Fei, X.Y.[Xiao-Yan], Wang, J.[Jun], Zhou, T.[Tao], Ying, S.H.[Shi-Hui], Shi, J.[Jun], Shen, D.G.[Ding-Gang],
Doubly Supervised Transfer Classifier for Computer-Aided Diagnosis With Imbalanced Modalities,
MedImg(41), No. 8, August 2022, pp. 2009-2020.
IEEE DOI 2208
Solid modeling, Imaging, Classification algorithms, Diseases, Magnetic resonance imaging, Knowledge transfer, block-diagonal low-rank BibRef

Karunanayake, N.[Nalan], Lohitvisate, W.[Wanrudee], Makhanov, S.S.[Stanislav S.],
Artificial life for segmentation of fusion ultrasound images of breast abnormalities,
PR(131), 2022, pp. 108838.
Elsevier DOI 2208
Artificial life, Fusion image, Medical image segmentation, Genetic algorithm, Ultrasound images, Breast cancer BibRef

Li, X.F.[Xiao-Feng], Sang, Y.P.[Yu-Peng], Ma, X.[Xianmin], Cai, Y.J.[Ying-Jie],
Quantitative feature classification for breast ultrasound images using improved naive bayes,
IET-IPR(17), No. 5, 2023, pp. 1417-1426.
DOI Link 2304
breast ultrasound images, feature classification, image reconstruction, improved naive bayes, texture feature BibRef

Li, Y.F.[Yan-Feng], Zhang, Z.[Zilu], Cheng, Z.Y.[Zhan-Yi], Cheng, L.[Lin], Chen, X.[Xin],
Semi-Supervised Learning for ABUS Tumor Detection Using Deep Learning Methodxo,
IET-IPR(17), No. 7, 2023, pp. 2113-2126.
DOI Link 2305
automated breast ultrasound, copy-paste strategy, semi-supervised learning, tumor detection BibRef

Chen, G.P.[Gong-Ping], Li, L.[Lei], Dai, Y.[Yu], Zhang, J.[Jianxun], Yap, M.H.[Moi Hoon],
AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images,
MedImg(42), No. 5, May 2023, pp. 1289-1300.
IEEE DOI 2305
Image segmentation, Convolution, Breast, Lesions, Ultrasonic imaging, Kernel, Breast tumors, Ultrasound images, deep learning BibRef

Chen, Y.X.[Yi-Xiong], Zhang, C.H.[Chun-Hui], Ding, C.H.Q.[Chris H. Q.], Liu, L.[Li],
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning,
MedImg(42), No. 5, May 2023, pp. 1388-1400.
IEEE DOI 2305
Videos, Medical diagnostic imaging, Semantics, Training, Visualization, Representation learning, Ultrasonic imaging, breast tumor BibRef

Wu, H.[Huisi], Huang, X.T.[Xiao-Ting], Guo, X.R.[Xin-Rong], Wen, Z.[Zhenkun], Qin, J.[Jing],
Cross-Image Dependency Modeling for Breast Ultrasound Segmentation,
MedImg(42), No. 6, June 2023, pp. 1619-1631.
IEEE DOI 2306
Lesions, Image segmentation, Semantics, Context modeling, Task analysis, Feature extraction, Ultrasonic imaging, deep learning BibRef

Mo, Y.H.[Yu-Hao], Han, C.[Chu], Liu, Y.[Yu], Liu, M.[Min], Shi, Z.W.[Zhen-Wei], Lin, J.[Jiatai], Zhao, B.C.[Bing-Chao], Huang, C.W.[Chun-Wang], Qiu, B.J.[Bing-Jiang], Cui, Y.[Yanfen], Wu, L.[Lei], Pan, X.P.[Xi-Peng], Xu, Z.[Zeyan], Huang, X.M.[Xiao-Mei], Li, Z.[Zhenhui], Liu, Z.[Zaiyi], Wang, Y.[Ying], Liang, C.H.[Chang-Hong],
HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images,
MedImg(42), No. 6, June 2023, pp. 1696-1706.
IEEE DOI 2306
Breast cancer, Breast, Solid modeling, Transformers, Lesions, Hospitals, Computational modeling, Breast cancer diagnosis, anatomical structure BibRef

Luo, Y.Z.[Yao-Zhong], Huang, Q.H.[Qing-Hua], Liu, L.Z.[Long-Zhong],
Classification of tumor in one single ultrasound image via a novel multi-view learning strategy,
PR(143), 2023, pp. 109776.
Elsevier DOI 2310
Image classification, Deep learning, Multi-view learning, Breast cancer recognition BibRef

Qi, W.B.[Wen-Bo], Wu, H.C., Chan, S.C.,
MDF-Net: A Multi-Scale Dynamic Fusion Network for Breast Tumor Segmentation of Ultrasound Images,
IP(32), 2023, pp. 4842-4855.
IEEE DOI 2310
BibRef

Özcan, H.[Hakan],
BUS-CAD: A computer-aided diagnosis system for breast tumor classification in ultrasound images using grid-search-optimized machine learning algorithms with extended and Boruta-selected features,
IJIST(33), No. 5, 2023, pp. 1480-1493.
DOI Link 2310
all-feature selection, breast cancer, classification, iterative correlation analysis, ultrasound BibRef

Rautela, K.[Kamakshi], Kumar, D.[Dinesh], Kumar, V.[Vijay],
Active contour and texture features hybrid model for breast cancer detection from ultrasonic images,
IJIST(33), No. 6, 2023, pp. 2061-2072.
DOI Link 2311
active contour, breast cancer, feature extraction, texture feature, ultrasound BibRef

Rengarajan, R.[Rajeshwari], Devasena, M.S.G.[M. S. Geetha], Gopu, G.,
Enhanced grasshopper optimization-based selection of ultrasound and elastography features for breast lesion classification,
IJIST(33), No. 6, 2023, pp. 2142-2156.
DOI Link 2311
breast cancer, computer-aided diagnosis, elastography, enhanced grasshopper optimization algorithm, image segmentation BibRef

Li, H.Y.[Hai-Yan], Wang, X.[Xu], Tang, Y.[Yiyin], Ye, S.H.[Shu-Hua],
BCUIS-Net: A breast cancer ultrasound image segmentation network via boundary-aware and shape feature fusion,
IJIST(34), No. 1, 2024, pp. e23011.
DOI Link 2401
boundary aware module, breast lesion segmentation, shape feature fusion module, shape fusion loss BibRef

You, G.[Guizeng], Yang, X.[Xinwu], Lee, X.[Xuanbo], Zhu, K.Q.[Kong-Qiang],
EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images,
IET-IPR(18), No. 2, 2024, pp. 523-534.
DOI Link 2402
cancer, convolutional neural networks, image segmentation, ultrasonic imaging BibRef


Adityan, M.K.L.[M. K. Laksath], Sharma, H.[Himanchal], Paul, A.[Angshuman],
Segmentation and Classification-Based Diagnosis of Tumors From Breast Ultrasound Images Using Multibranch Unet,
ICIP23(2505-2509)
IEEE DOI 2312
BibRef

Zhang, H.Y.[Heng-Yu], Xu, J.X.[Jing-Xuan], Wang, M.Y.[Meng-Yu], Li, Y.F.[Yan-Feng],
DenseATT-Net: Densely-Connected Neural Network with Intensive Attention Modules for 3D ABUS Mass Segmentation,
ICIVC22(348-353)
IEEE DOI 2301
3D automated breast ultrasound. Training, Image segmentation, Solid modeling, Ultrasonic imaging, Shape, Computational modeling, Deep Learning, ABUS Images, Attention Module BibRef

Zhuang, X.W.[Xian-Wei], Zhu, X.[Xiner], Hu, H.J.[Hao-Ji], Yao, J.[Jincao], Li, W.[Wei], Yang, C.[Chen], Wang, L.P.[Li-Ping], Feng, N.[Na], Xu, D.[Dong],
Residual Swin Transformer Unet with Consistency Regularization for Automatic Breast Ultrasound Tumor Segmentation,
ICIP22(3071-3075)
IEEE DOI 2211
Image segmentation, Ultrasonic imaging, Semantics, Self-supervised learning, Transformers, Decoding, Task analysis, Convolutional Neural Networks BibRef

Jin, S.B.[Song-Bai], Lu, W.K.[Wen-Kai], Monkam, P.[Patrice],
Deep Neural Network-Based Noisy Pixel Estimation for Breast Ultrasound Segmentation,
ICIP22(1776-1780)
IEEE DOI 2211
Deep learning, Training, Image segmentation, Ultrasonic imaging, Breast tumors, Annotations, Neural networks, Image segmentation, breast tumor segmentation BibRef

Wijata, A.M.[Agata M.], Nalepa, J.[Jakub],
Unbiased Validation of the Algorithms for Automatic Needle Localization in Ultrasound-Guided Breast Biopsies,
ICIP22(3571-3575)
IEEE DOI 2211
Location awareness, Ultrasonic imaging, Image analysis, Biopsy, Breast biopsy, Needles, Lesions, Biopsy needle localization. BibRef

Kim, D.[Daekyung], Nam, C.M.[Chang-Mo], Park, H.[Haesol], Jang, M.J.[Mi-Jung], Lee, K.J.[Kyong Joon],
Weakly supervised Branch Network with Template Mask for Classifying Masses in 3D Automated Breast Ultrasound,
WACV22(3212-3219)
IEEE DOI 2202
Training, Image segmentation, Ultrasonic imaging, Shape, Neural networks, Radiology, Object Detection/Recognition/Categorization BibRef

Huang, K.[Kuan], Zhang, Y.T.[Ying-Tao], Cheng, H.D., Xing, P.[Ping], Zhang, B.[Boyu],
Semantic Segmentation of Breast Ultrasound Image with Pyramid Fuzzy Uncertainty Reduction and Direction Connectedness Feature,
ICPR21(3357-3364)
IEEE DOI 2105
Deep learning, Weight measurement, Image segmentation, Uncertainty, Ultrasonic imaging, Semantics, Breast, fuzzy logic, direction connectedness BibRef

Teixeira, J.F.[João F.], Carreiro, A.M.[António M.], Santos, R.M.[Rute M.], Oliveira, H.P.[Hélder P.],
B-mode Ultrasound Breast Anatomy Segmentation,
ICIAR20(II:193-201).
Springer DOI 2007
BibRef

Torres, F.[Fabian], Escalante-Ramirez, B.[Boris], Olveres, J.[Jimena], Yen, P.L.[Ping-Lang],
Lesion Detection in Breast Ultrasound Images Using a Machine Learning Approach and Genetic Optimization,
IbPRIA19(I:289-301).
Springer DOI 1910
BibRef

Byra, M.[Michal], Sznajder, T.[Tomasz], Korzinek, D.[Danijel], Piotrzkowska-Wroblewska, H.[Hanna], Dobruch-Sobczak, K.[Katarzyna], Nowicki, A.[Andrzej], Marasek, K.[Krzysztof],
Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification with Deep Learning,
IbPRIA(I:41-52).
Springer DOI 1910
BibRef

Huang, K., Cheng, H.D., Zhang, Y., Zhang, B., Xing, P., Ning, C.,
Medical Knowledge Constrained Semantic Breast Ultrasound Image Segmentation,
ICPR18(1193-1198)
IEEE DOI 1812
Tumors, Image segmentation, Training, Breast cancer, Wavelet transforms, Biomedical imaging, Semantics, conditional random field (CRF) BibRef

Xu, F., Xian, M., Zhang, Y., Huang, K., Cheng, H.D., Zhang, B., Ding, J., Ning, C., Wang, Y.,
A Hybrid Framework for Tumor Saliency Estimation,
ICPR18(3935-3940)
IEEE DOI 1812
Tumors, Adaptation models, Estimation, Image segmentation, Correlation, Visualization, Computer science, Breast ultrasound, Automatic segmentation BibRef

Rodríguez-Cristerna, A.[Arturo], Gómez-Flores, W.[Wilfrido], de Albuquerque-Pereira, W.C.[Wagner Coelho],
BUSAT: A MATLAB Toolbox for Breast Ultrasound Image Analysis,
MCPR17(268-277).
Springer DOI 1706
BibRef

Luo, Y., Han, S., Huang, Q.,
A Novel Graph-Based Segmentation Method for Breast Ultrasound Images,
DICTA16(1-6)
IEEE DOI 1701
Breast tumors BibRef

Elawady, M.[Mohamed], Sadek, I.[Ibrahim], Shabayek, A.E.[Abd El_Rahman], Pons, G.[Gerard], Ganau, S.[Sergi],
Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images,
ICIAR16(206-213).
Springer DOI 1608
BibRef

Liu, S.B.[Song-Bo], Cheng, H.D., Liu, Y.[Yan], Huang, J.H.[Jian-Hua], Zhang, Y.T.[Ying-Tao], Tang, X.L.[Xiang-Long],
An effective computer aided diagnosis system using B-Mode and color Doppler flow imaging for breast cancer,
VCIP13(1-4)
IEEE DOI 1402
biomedical ultrasonics BibRef

Pons, G.[Gerard], Martí, R.[Robert], Ganau, S.[Sergi], Sentís, M.[Melcior], Martí, J.[Joan],
Feasibility Study of Lesion Detection Using Deformable Part Models in Breast Ultrasound Images,
IbPRIA13(269-276).
Springer DOI 1307
BibRef

Harary, S.[Sivan], Walach, E.[Eugene],
Identification of Malignant Breast Tumors Based on Acoustic Attenuation Mapping of Conventional Ultrasound Images,
MCVM12(233-243).
Springer DOI 1305
BibRef

Rodrigues, R.[Rafael], Pinheiro, A.[Antonio], Braz, R.[Rui], Pereira, M.[Manuela], Moutinho, J.,
Towards breast ultrasound image segmentation using multi-resolution pixel descriptors,
ICPR12(2833-2836).
WWW Link. 1302
BibRef

Hao, Z.H.[Zhi-Hui], Wang, Q.A.[Qi-Ang], Ren, H.B.[Hai-Bing], Xu, K.H.[Kuan-Hong], Seong, Y.K.[Yeong Kyeong], Kim, J.[Jiyeun],
Multiscale superpixel classification for tumor segmentation in breast ultrasound images,
ICIP12(2817-2820).
IEEE DOI 1302
BibRef

Pons, G.[Gerard], Martí, J.[Joan], Martí, R.[Robert], Noble, J.A.[J. Alison],
Simultaneous Lesion Segmentation and Bias Correction in Breast Ultrasound Images,
IbPRIA11(692-699).
Springer DOI 1106
BibRef

Bocchi, L.[Leonardo], Rogai, F.[Francesco],
A Genetic Fuzzy Rules Learning Approach for Unseeded Segmentation in Echography,
EvoIASP12(305-314).
Springer DOI 1204
BibRef
Earlier:
Segmentation of Ultrasound Breast Images: Optimization of Algorithm Parameters,
EvoIASP11(163-172).
Springer DOI 1104
BibRef

Singh, M.S.[M. Suheshkumar], Rajan, K., Vasu, R.M., Sijeesh, K.,
A novel two sources ultrasound modulated optical tomographic system for screening breast cancer through elasticity characterization,
ICIP09(669-672).
IEEE DOI 0911
BibRef

Shan, J.[Juan], Wang, Y.X.[Yu-Xuan], Cheng, H.D.,
Completely automatic segmentation for breast ultrasound using multiple-domain features,
ICIP10(1713-1716).
IEEE DOI 1009
BibRef
Earlier: A1, A3, A2:
A novel automatic seed point selection algorithm for breast ultrasound images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Ponomaryov, V.[Volodymyr], Sanchez-Ramirez, J.L.[Jose Luis], Juarez-Landin, C.[Cristina],
Optimal Wavelet Filters Selection for Ultrasound and Mammography Compression,
CIARP08(62-69).
Springer DOI 0809
BibRef

Ye, Z.[Zhen], Suri, J.[Jasjit], Sun, Y.J.[Ya-Jie], Janer, R.,
Four Image Interpolation Techniques for Ultrasound Breast Phantom Data Acquired Using Fischer's Full Field Digital Mammography and Ultrasound System (FFDMUS): A Comparative Approach,
ICIP05(II: 1238-1241).
IEEE DOI 0512
BibRef

Huang, Y.L.[Yu-Len], Chen, D.R.[Dar-Ren], Liu, Y.K.[Ya-Kuang],
Breast cancer diagnosis using image retrieval for different ultrasonic systems,
ICIP04(V: 2957-2960).
IEEE DOI 0505
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Mammograms, Density Issues .


Last update:Mar 16, 2024 at 20:36:19