21.7.2.8 Mammograms, Density Issues

Chapter Contents (Back)
Mammograms. Density. Breast Density. Medical, Applications.

Saha, P.K., Udupa, J.K., Conant, E.F., Chakraborty, D.P., Sullivan, D.,
Breast tissue density quantification via digitized mammograms,
MedImg(20), No. 8, August 2001, pp. 792-803.
IEEE Top Reference. 0110
BibRef

Baeg, S.[Soon], Kehtarnavaz, N.[Nasser],
Classification of breast mass abnormalities using denseness and architectural distortion,
ELCVIA(1), No. 1, August 2002, pp. 1-20.
DOI Link 0208
BibRef

Subashini, T.S., Ramalingam, V., Palanivel, S.,
Automated assessment of breast tissue density in digital mammograms,
CVIU(114), No. 1, January 2010, pp. 33-43.
Elsevier DOI 1001
Mammograms; Breast tissue density; Segmentation; Pectoral muscles; Artifact removal; Statistical features; Support vector machines BibRef

Strange, H.[Harry], Chen, Z.L.[Zhi-Li], Denton, E.R.E.[Erika R.E.], Zwiggelaar, R.[Reyer],
Modelling mammographic microcalcification clusters using persistent mereotopology,
PRL(47), No. 1, 2014, pp. 157-163.
Elsevier DOI 1408
Discrete mereotopology BibRef

Ashiru, O., Zwiggelaar, R.,
Classification of mammographic microcalcification clusters using a combination of topological and location modelling,
IPTA16(1-6)
IEEE DOI 1703
feature extraction BibRef

Strange, H.[Harry], Denton, E.R.E.[Erika R.E.], Kibiro, M.[Minnie], Zwiggelaar, R.[Reyer],
Manifold Learning for Density Segmentation in High Risk Mammograms,
IbPRIA13(245-252).
Springer DOI 1307
BibRef

Chen, Z.L.[Zhi-Li], Oliver, A.[Arnau], Denton, E.R.E.[Erika R.E.], Zwiggelaar, R.[Reyer],
Automated Mammographic Risk Classification Based on Breast Density Estimation,
IbPRIA13(237-244).
Springer DOI 1307
BibRef

Chen, Z.L.[Zhi-Li], Denton, E.R.E.[Erika R.E.], Zwiggelaar, R.[Reyer],
Local Feature Based Breast Tissue Appearance Modelling for Mammographic Risk Assessment,
BMVA(2013), No. 1, 2013, pp. 1-19.
PDF File. 1304
BibRef
Earlier:
Topographic representation based breast density segmentation for mammographic risk assessment,
ICIP12(1993-1996).
IEEE DOI 1302
BibRef

Masmoudi, A.D.[Alima Damak], Ben Ayed, N.G.[Norhen Gargouri], Masmoudi, D.S.[Dorra Sellami], Abid, R.[Riad],
LBPV descriptors-based automatic ACR/BIRADS classification approach,
JIVP(2013), No. 1, 2013, pp. 19.
DOI Link 1305
tissue density in breast cancer evaluation. BibRef

Bandyopadhyay, S.K.[Samir K.], Maitra, I.K.[Indra Kanta], Nag, S.[Sanjay],
Mammographic Density Estimation and Classification Using Segmentation and Progressive Elimination Method,
IJIG(13), No. 03, 2013, pp. 1350013.
DOI Link 1309
BibRef

Jagannath, H.S., Virmani, J., Kumar, V.,
Morphological Enhancement of Microcalcifications in Digital Mammograms,
JIEI-B(93), No. 3, September-November 2012, pp. 163-172.
Springer DOI 1506
BibRef

Kumar, I.[Indrajeet], Virmani, J.[Jitendra], Bhadauria, H.S.,
A Review of Breast Density Classification Methods,
ICCSGD15(). BibRef 1500

Kallenberg, M., Petersen, K., Nielsen, M., Ng, A.Y., Diao, P., Igel, C., Vachon, C.M., Holland, K., Winkel, R.R., Karssemeijer, N., Lillholm, M.,
Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring,
MedImg(35), No. 5, May 2016, pp. 1322-1331.
IEEE DOI 1605
Breast cancer BibRef

Rabidas, R.[Rinku], Chakraborty, J.[Jayasree], Midya, A.[Abhishek],
Analysis of 2D singularities for mammographic mass classification,
IET-CV(11), No. 1, February 2017, pp. 22-32.
DOI Link 1703
BibRef

Chokri, F.[Ferkous], Farida, M.H.[Merouani Hayet],
Mammographic mass classification according to Bi-RADS lexicon,
IET-CV(11), No. 3, April 2017, pp. 189-198.
DOI Link 1704
BibRef

Jiao, Z.C.[Zhi-Cheng], Gao, X.B.[Xin-Bo], Wang, Y.[Ying], Li, J.[Jie],
A parasitic metric learning net for breast mass classification based on mammography,
PR(75), No. 1, 2018, pp. 292-301.
Elsevier DOI 1712
Deep learning BibRef

Suhail, Z.[Zobia], Hamidinekoo, A.[Azam], Zwiggelaar, R.[Reyer],
Mammographic mass classification using filter response patches,
IET-CV(12), No. 8, December 2018, pp. 1060-1066.
DOI Link 1812
BibRef

Kim, H., Lee, J., Soh, J., Min, J., Choi, Y.W.[Y. Wook], Cho, S.,
Backprojection Filtration Image Reconstruction Approach for Reducing High-Density Object Artifacts in Digital Breast Tomosynthesis,
MedImg(38), No. 5, May 2019, pp. 1161-1171.
IEEE DOI 1905
Image reconstruction, Image segmentation, Trajectory, Reconstruction algorithms, Band-pass filters, Detectors, image reconstruction BibRef

Rajalakshmi, N.R.[N. Ravitha], Vidhyapriya, R., Elango, N., Ramesh, N.[Nikhil],
Deeply supervised U-Net for mass segmentation in digital mammograms,
IJIST(31), No. 1, 2021, pp. 59-71.
DOI Link 2102
conditional random fields, deep supervision, mammograms, mass segmentation BibRef

Li, H.[Hua], Niu, J.[Jing], Li, D.G.[Den-Gao], Zhang, C.[Chen],
Classification of breast mass in two-view mammograms via deep learning,
IET-IPR(15), No. 2, 2021, pp. 454-467.
DOI Link 2106
BibRef

Verma, P.[Parag], Dumka, A.[Ankur], Bhardwaj, A.[Anuj], Kestwal, M.C.[Mukesh Chandra],
Classifying Breast Density in Mammographic Images Using Wavelet-Based and Fine-Tuned Sensory Neural Networks,
IJIG(21), No. 5 2021, pp. 2140004.
DOI Link 2201
BibRef

Li, H.[Heyi], Chen, D.D.[Dong-Dong], Nailon, W.H.[William H.], Davies, M.E.[Mike E.], Laurenson, D.I.[David I.],
Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography,
MedImg(41), No. 1, January 2022, pp. 3-13.
IEEE DOI 2201
Cancer, Feature extraction, Breast, Image segmentation, Mammography, Task analysis, Shape, Mammography diagnosis, dual-path network, deep learning BibRef

Lee, J.[Juhun], Nishikawa, R.M.[Robert M.],
Identifying Women With Mammographically-Occult Breast Cancer Leveraging GAN-Simulated Mammograms,
MedImg(41), No. 1, January 2022, pp. 225-236.
IEEE DOI 2201
Mammography, Cancer, Breast, Generative adversarial networks, Convolutional neural networks, Generators, Lesions, radon cumulative distribution transform BibRef

Hans, R.[Rahul], Kaur, H.[Harjot],
Hybrid Biogeography-Based Optimization and Genetic Algorithm for Feature Selection in Mammographic Breast Density Classification,
IJIG(22), No. 3 2022, pp. 2140007.
DOI Link 2206
BibRef


Cao, H.C.[Hai-Chao], Pu, S.L.[Shi-Liang], Tan, W.M.[Wen-Ming],
A Novel Method for Segmentation of Breast Masses Based on Mammography Images,
ICIP21(3782-3786)
IEEE DOI 2201
Training, Image segmentation, Histograms, Shape, Semantics, Training data, Prediction methods, deep learning, breast mass segmentation BibRef

Tang, Y.X.[Yu-Xing], Cao, Z.J.[Zhen-Jie], Zhang, Y.B.[Yan-Bo], Yang, Z.C.[Zhi-Cheng], Ji, Z.C.[Zong-Cheng], Wang, Y.W.[Yi-Wei], Han, M.[Mei], Ma, J.[Jie], Xiao, J.[Jing], Chang, P.[Peng],
Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms,
CVPR21(3854-3863)
IEEE DOI 2111
Training, Uncertainty, Computational modeling, Semisupervised learning, Radiology, Probabilistic logic, Data models BibRef

Li, H., Mukundan, R., Boyd, S.,
Breast Density Classification Using Multifractal Spectrum with Histogram Analysis,
IVCNZ19(1-6)
IEEE DOI 2004
biological organs, cancer, diagnostic radiography, feature extraction, image classification, image texture, image enhancement BibRef

Lee, J.[Jaehwan], Yoo, D.[Donggeun], Kim, H.E.[Hyo-Eun],
Photometric Transformer Networks and Label Adjustment for Breast Density Prediction,
VRMI19(460-466)
IEEE DOI 2004
learning (artificial intelligence), mammography, medical image processing, neural nets, breast density prediction, label refinement BibRef

Lizzi, F.[Francesca], Laruina, F.[Francesco], Oliva, P.[Piernicola], Retico, A.[Alessandra], Fantacci, M.E.[Maria Evelina],
Residual Convolutional Neural Networks to Automatically Extract Significant Breast Density Features,
CAIPWS19(28-35).
Springer DOI 1909
BibRef

Zhang, L.L.[Lin-Lin], Li, Y.F.[Yan-Feng], Chen, H.J.[Hou-Jin], Cheng, L.[Lin],
Mammographic Mass Detection by Bilateral Analysis Based on Convolution Neural Network,
ICIP19(784-788)
IEEE DOI 1910
Mass Detection, Mammogram, Deep Learning, Region Registration BibRef

Li, Y.F.[Yan-Feng], Chen, H.J.[Hou-Jin], Zhang, L.L.[Lin-Lin], Cheng, L.[Lin],
Mammographic mass detection based on convolution neural network,
ICPR18(3850-3855)
IEEE DOI 1812
Mammography, Feature extraction, Strips, Muscles, Breast cancer, Training, convolution neural network, mammogram, mass detection, deep learning BibRef

Tlusty, T., Amit, G., Ben-Ari, R.,
Unsupervised clustering of mammograms for outlier detection and breast density estimation,
ICPR18(3808-3813)
IEEE DOI 1812
Breast, Image segmentation, Mammography, Training, Feature extraction, Implants BibRef

Hernández-Hernández, S.[Saiveth], Orantes-Molina, A.[Antonio], Cruz-Barbosa, R.[Raúl],
Improving Breast Mass Classification Through Kernel Methods and the Fusion of Clinical Data and Image Descriptors,
MCPR18(258-266).
Springer DOI 1807
BibRef

Sajeev, S., Bajger, M., Lee, G.,
Structured Micro-Pattern Based LBP Features for Classification of Masses in Dense Breasts,
DICTA17(1-8)
IEEE DOI 1804
biological organs, cancer, feature extraction, image classification, image texture, mammography, Mammography BibRef

García, E.[Eloy], Oliver, A.[Arnau], Diez, Y.[Yago], Diaz, O.[Oliver], Lladó, X.[Xavier], Martí, R.[Robert], Martí, J.[Joan],
Similarity Metrics for Intensity-Based Registration Using Breast Density Maps,
IbPRIA17(217-225).
Springer DOI 1706
BibRef

Rampun, A.[Andrik], Morrow, P.[Philip], Scotney, B.[Bryan], Winder, J.[John],
Breast Density Classification Using Local Ternary Patterns in Mammograms,
ICIAR17(463-470).
Springer DOI 1706
BibRef

Fonseca, P.[Pablo], Castañeda, B.[Benjamin], Valenzuela, R.[Ricardo], Wainer, J.[Jacques],
Breast Density Classification with Convolutional Neural Networks,
CIARP16(101-108).
Springer DOI 1703
BibRef

Sajeev, S., Bajger, M.[Mariusz], Lee, G.[Gobert],
Segmentation of Breast Masses in Local Dense Background Using Adaptive Clip Limit-CLAHE,
DICTA15(1-8)
IEEE DOI 1603
entropy BibRef

Lao, Z.Q.[Zhi-Qiang], Huo, Z.M.[Zhi-Min],
Quantitative assessment of breast dense tissue on mammograms,
ICIP09(2605-2608).
IEEE DOI 0911
BibRef

Hadley, E.M.[Edward M.], Denton, E.R.E.[Erika R. E.], Pont, J.[Josep], Pérez, E.[Elsa], Zwiggelaar, R.[Reyer],
Risk Classification of Mammograms Using Anatomical Linear Structure and Density Information,
IbPRIA07(II: 186-193).
Springer DOI 0706
BibRef

Bosch, A.[Anna], Munoz, X.[Xavier], Oliver, A.[Arnau], Marti, J.[Joan],
Modeling and Classifying Breast Tissue Density in Mammograms,
CVPR06(II: 1552-1558).
IEEE DOI 0606
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Mammograms, MRI, Magnetic Resonance Imaging .


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