21.7.3.3 Breast Mass Detection, Analysis

Chapter Contents (Back)
Mammograms. Mass Detection. Medical, Applications.

te Brake, G.M., Karssemeijer, N.,
Single and multiscale detection of masses in digital mammograms,
MedImg(18), No. 7, July 1999, pp. 628-639.
IEEE Top Reference. 0110
BibRef

Karssemeijer, N., te Brake, G.M.,
Detection of stellate distortions in mammograms,
MedImg(15), No. 5, October 1996, pp. 611-619.
IEEE Top Reference. 0203
BibRef

Hupse, R., Karssemeijer, N.,
Use of Normal Tissue Context in Computer-Aided Detection of Masses in Mammograms,
MedImg(28), No. 12, December 2009, pp. 2033-2041.
IEEE DOI 0912
BibRef

Timp, S., Varela, C., Karssemeijer, N.,
Temporal Change Analysis for Characterization of Mass Lesions in Mammography,
MedImg(26), No. 7, July 2007, pp. 945-953.
IEEE DOI 0707
BibRef

Polakowski, W.E., Cournoyer, D.A., Rogers, S.K., Desimio, M.P., Ruck, D.W., Hoffmeister, J.W., Raines, R.A.,
Computer-Aided Breast-Cancer Detection and Diagnosis of Masses Using Difference of Gaussians and Derivative-Based Feature Saliency,
MedImg(16), No. 6, December 1997, pp. 811-819.
IEEE Top Reference. 9803
BibRef

Constantinidis, A.S., Fairhurst, M.C., Rahman, A.F.R.,
A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms,
PR(34), No. 8, August 2001, pp. 1527-1537.
Elsevier DOI 0105
BibRef

Sahiner, B., Petrick, N., Chan, H.P.[Heang-Ping], Hadjiiski, L.M., Paramagul, C., Helvie, M.A., Gurcan, M.N.,
Computer-aided characterization of mammographic masses: Accuracy of mass segmentation and its effects on characterization,
MedImg(20), No. 12, December 2001, pp. 1275-1284.
IEEE Top Reference. 0201
BibRef

Hadjiiski, L.M., Sahiner, B., Chan, H.P.[Heang-Ping], Petrick, N., Helvie, M.A.,
Classification of malignant and benign masses based on hybrid ART2LDA approach,
MedImg(18), No. 12, December 1999, pp. 1178-1187.
IEEE Top Reference. 0110
BibRef

Hatanaka, Y., Hara, T., Fujita, H., Kasai, S., Endo, T.[Tokiko], Iwase, T.,
Development of an automated method for detecting mammographic masses with a partial loss of region,
MedImg(20), No. 12, December 2001, pp. 1209-1214.
IEEE Top Reference. 0201
BibRef

Lo, S.C.B.[Shih-Chung B.], Li, H.[Huai], Wang, Y.[Yue], Kinnard, L., Freedman, M.T.,
A multiple circular path convolution neural network system for detection of mammographic masses,
MedImg(21), No. 2, February 2002, pp. 150-158.
IEEE Top Reference. 0204
BibRef

Li, H., Liu, K., Lo, S.C., and Wang, Y.,
Stochastic Model and Probabilistic Decision-Based Classifier for Mass Detection in Digital Mammography,
ICIP97(III: 539-542).
IEEE DOI BibRef 9700

Eltonsy, N.H., Tourassi, G.D., Elmaghraby, A.S.,
A Concentric Morphology Model for the Detection of Masses in Mammography,
MedImg(26), No. 6, June 2007, pp. 880-889.
IEEE DOI 0706
BibRef

Castella, C.[Cyril], Eckstein, M.P.[Miguel P.], Abbey, C.K.[Craig K.], Kinkel, K.[Karen], Verdun, F.R.[Francis R.], Saunders, R.S., Samei, E., Bochud, F.O.[François O.],
Mass detection on mammograms: Influence of signal shape uncertainty on human and model observers,
JOSA-A(26), No. 2, February 2009, pp. 425-436.
WWW Link. 0902
BibRef

Dominguez, A.R.[Alfonso Rojas], Nandi, A.K.[Asoke K.],
Toward breast cancer diagnosis based on automated segmentation of masses in mammograms,
PR(42), No. 6, June 2009, pp. 1138-1148.
Elsevier DOI 0902
Breast cancer; Breast masses; Mammography; Image analysis BibRef

Cao, A.[Aize], Song, Q.[Qing], Yang, X.L.[Xu-Lei],
Robust information clustering incorporating spatial information for breast mass detection in digitized mammograms,
CVIU(109), No. 1, January 2008, pp. 86-96.
Elsevier DOI 0801
Robust information clustering; Minimax optimization of mutual information; Spatial information BibRef

Cao, A.[Aize], Song, Q.[Qing], Yang, X.L.[Xu-Lei], Wang, L.[Lei],
Breast mass segmentation based on information theory,
ICPR04(III: 758-761).
IEEE DOI 0409
BibRef

de Oliveira Martins, L.[Leonardo], Junior, G.B.[Geraldo Braz], Corrêa Silva, A.[Aristófanes], Cardoso de Paiva, A.[Anselmo], Gattass, M.[Marcelo],
Detection of Masses in Digital Mammograms using K-Means and Support Vector Machine,
ELCVIA(8), No. 2, July 2009, pp. xx-yy.
DOI Link 1002
BibRef

Neto, O.P.S., Carvalho, O., Sampaio, W., Corrêa Silva, A.[Aristófanes], Cardoso de Paiva, A.[Anselmo],
Automatic segmentation of masses in digital mammograms using particle swarm optimization and graph clustering,
WSSIP15(109-112)
IEEE DOI 1603
evolutionary computation BibRef

Muralidhar, G.S., Bovik, A.C., Giese, J.D., Sampat, M.P., Whitman, G.J., Haygood, T.M., Stephens, T.W., Markey, M.K.,
Snakules: A Model-Based Active Contour Algorithm for the Annotation of Spicules on Mammography,
MedImg(29), No. 10, October 2010, pp. 1768-1780.
IEEE DOI 1011
BibRef

Muralidhar, G.S.[Gautam S.], Markey, M.K.[Mia K.], Bovik, A.C.[Alan C.],
Snakules for automatic classification of candidate spiculated mass locations on mammography,
Southwest10(197-200).
IEEE DOI 1005
BibRef
Earlier: A1, A3, A2:
Snakules: Snakes that seek spicules on mammography,
ICIP10(4373-4376).
IEEE DOI 1009
BibRef

Sampat, M.P., Wang, Z.[Zhou], Markey, M.K., Whitman, G.J., Stephens, T.W., Bovik, A.C.,
Measuring Intra- and Inter-Observer Agreement in Identifying and Localizing Structures in Medical Images,
ICIP06(81-84).
IEEE DOI 0610
BibRef

Jahanbin, R.[Rana], Sampat, M.P.[Mehul P.], Muralidhar, G.S.[Gautam S.], Whitman, G.J.[Gary J.], Bovik, A.C.[Alan C.], Markey, M.K.[Mia K.],
Automated Region of Interest Detection of Spiculated Masses on Digital Mammograms,
Southwest08(129-132).
IEEE DOI 0803
BibRef

Muralidhar, G.S.[Gautam S.], Bovik, A.C.[Alan C.], Markey, M.K.[Mia K.],
A Steerable, Multiscale Singularity Index,
SPLetters(20), No. 1, January 2013, pp. 7-10.
IEEE DOI 1212
BibRef
And:
A new singularity index,
ICIP12(1873-1876).
IEEE DOI 1302
BibRef

Sampat, M.P., Markey, M.K., Bovik, A.C.,
Measurement and Detection of Spiculated Lesions,
Southwest06(105-109).
IEEE DOI 0603
BibRef

Wang, Y.[Ying], Tao, D.C.[Da-Cheng], Gao, X.B.[Xin-Bo], Li, X.L.[Xue-Long], Wang, B.[Bin],
Mammographic mass segmentation: Embedding multiple features in vector-valued level set in ambiguous regions,
PR(44), No. 9, September 2011, pp. 1903-1915.
Elsevier DOI 1106
Mass segmentation; Computer-aided diagnose; Vector-valued level set; Relaxed shape constraint; Mammograms
See also Relay Level Set Method for Automatic Image Segmentation, A. BibRef

Palma, G.[Giovanni], Bloch, I.[Isabelle], Muller, S.[Serge],
Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches,
PR(47), No. 7, 2014, pp. 2467-2480.
Elsevier DOI 1404
Digital breast tomosynthesis BibRef

Tsochatzidis, L.[Lazaros], Zagoris, K.[Konstantinos], Arikidis, N.[Nikolaos], Karahaliou, A.[Anna], Costaridou, L.[Lena], Pratikakis, I.E.[Ioannis E.],
Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach,
PR(71), No. 1, 2017, pp. 106-117.
Elsevier DOI 1707
Mammography BibRef

Liu, J., Zhang, S.T.[Shao-Ting], Liu, W., Deng, C., Zheng, Y., Metaxas, D.N.,
Scalable Mammogram Retrieval Using Composite Anchor Graph Hashing With Iterative Quantization,
CirSysVideo(27), No. 11, November 2017, pp. 2450-2460.
IEEE DOI 1712
Binary codes, Breast cancer, Databases, Mammography, Quantization (signal), Visualization, Composite hashing, scalable BibRef

Jiang, M.[Menglin], Zhang, S.T.[Shao-Ting], Metaxas, D.N.[Dimitris N.],
Detection of Mammographic Masses by Content-Based Image Retrieval,
MLMI14(33-41).
Springer DOI 1410
BibRef

Sajeev, S.[Shelda], Bajger, M.[Mariusz], Lee, G.[Gobert],
Superpixel texture analysis for classification of breast masses in dense background,
IET-CV(12), No. 6, September 2018, pp. 779-786.
DOI Link 1808
BibRef

Gu, S.H.[Sheng-Hua], Chen, Y.[Yi], Sheng, F.Q.[Fang-Qing], Zhan, T.M.[Tian-Ming], Chen, Y.J.[Yun-Jie],
A novel method for breast mass segmentation: from superpixel to subpixel segmentation,
MVA(30), No. 7-8, October 2019, pp. 1111-1122.
Springer DOI 1911
BibRef

Gnanasekaran, V.S.[Vaira Suganthi], Joypaul, S.[Sutha], Sundaram, P.M.[Parvathy Meenakshi], Chairman, D.D.[Durga Devi],
Deep learning algorithm for breast masses classification in mammograms,
IET-IPR(14), No. 12, October 2020, pp. 2860-2868.
DOI Link 2010
BibRef

Xu, S.Z.[Sheng-Zhou], Adeli, E.[Ehsan], Cheng, J.Z.[Jie-Zhi], Xiang, L.[Lei], Li, Y.[Yang], Lee, S.W.[Seong-Whan], Shen, D.G.[Ding-Gang],
Mammographic mass segmentation using multichannel and multiscale fully convolutional networks,
IJIST(30), No. 4, 2020, pp. 1095-1107.
DOI Link 2011
fully convolutional network, mammogram, mass segmentation, multichannel, multiscale BibRef

Shen, T., Gou, C., Wang, J., Wang, F.,
Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model,
SPLetters(27), 2020, pp. 196-200.
IEEE DOI 2002
Mixed-supervision, deep learning, segmentation and classification, mammogram BibRef

Andreadis, T.[Theofilos], Emmanouilidis, C.[Christodoulos], Goumas, S.[Stefanos], Koulouriotis, D.[Dimitrios],
Development of an intelligent CAD system for mass detection in mammographic images,
IET-IPR(14), No. 10, August 2020, pp. 1960-1966.
DOI Link 2008
BibRef

Cao, X., Chen, H., Li, Y., Peng, Y., Wang, S., Cheng, L.,
Uncertainty Aware Temporal-Ensembling Model for Semi-Supervised ABUS Mass Segmentation,
MedImg(40), No. 1, January 2021, pp. 431-443.
IEEE DOI 2012
Uncertainty, Image segmentation, Breast, Reliability, Training, Biomedical imaging, breast mass BibRef

Chandraraju, T.S.[Thirumarai Selvi], Jeyaprakash, A.[Amudha],
Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities,
IJIST(32), No. 5, 2022, pp. 1561-1576.
DOI Link 2209
altered phase preserving dynamic range compression (APPDRC), breast cancer, chaotic krill herd optimization (CKHO), deep belief network (DBN) BibRef

Sun, L.[Lilei], Wen, J.[Jie], Wang, J.Q.[Jun-Qian], Zhang, Z.[Zheng], Zhao, Y.[Yong], Zhang, G.Y.[Gui-Ying], Xu, Y.[Yong],
Breast mass classification based on supervised contrastive learning and multi-view consistency penalty on mammography,
IET-Bio(11), No. 6, 2022, pp. 588-600.
DOI Link 2212
BibRef

Pan, A.[Ansi], Xu, S.Z.[Sheng-Zhou],
Mammographic mass recognition using feature reuse and channel attention mechanism,
IJIST(32), No. 6, 2022, pp. 2154-2162.
DOI Link 2212
breast cancer, convolutional neural network, mass recognition, mammogram BibRef

Chakravarthy, S.R.S.[S. R. Sannasi], Rajaguru, H.[Harikumar],
SKMAT-U-Net architecture for breast mass segmentation,
IJIST(32), No. 6, 2022, pp. 1880-1888.
DOI Link 2212
breast cancer, convolution neural network, loss function, segmentation, ultrasound BibRef

Liu, Y.H.[Yu-Hang], Zhang, F.D.[Fan-Dong], Chen, C.Q.[Chao-Qi], Wang, S.W.[Si-Wen], Wang, Y.Z.[Yi-Zhou], Yu, Y.Z.[Yi-Zhou],
Act Like a Radiologist: Towards Reliable Multi-View Correspondence Reasoning for Mammogram Mass Detection,
PAMI(44), No. 10, October 2022, pp. 5947-5961.
IEEE DOI 2209
Cognition, Mammography, Visualization, Solid modeling, Bipartite graph, Semantics, Proposals, Detection, mammogram BibRef

Zhang, J.D.[Jia-Dong], Cui, Z.M.[Zhi-Ming], Zhou, L.P.[Lu-Ping], Sun, Y.Q.[Yi-Qun], Li, Z.H.[Zhen-Hui], Liu, Z.Y.[Zai-Yi], Shen, D.G.[Ding-Gang],
Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning,
MedImg(42), No. 12, December 2023, pp. 3944-3955.
IEEE DOI Code:
WWW Link. 2312
BibRef

Li, G.B.[Guo-Bin], Zwiggelaar, R.[Reyer],
Feature learning based on connectivity estimation for unbiased mammography mass classification,
CVIU(238), 2024, pp. 103884.
Elsevier DOI 2312
Breast cancer, Texture features, Deep learned features, Interpretability BibRef

Bania, R.K.[Rubul Kumar], Halder, A.[Anindya],
Automatic Breast Mass Lesion Detection in Mammogram Image,
IJIG(25), No. 1, Januaury 2025, pp. 2450056.
DOI Link 2502
BibRef

Yang, J.[Julia], Barnett, A.J.[Alina Jade], Donnelly, J.[Jon], Kishore, S.[Satvik], Fang, J.[Jerry], Schwartz, F.R.[Fides Regina], Chen, C.F.[Chao-Fan], Lo, J.Y.[Joseph Y.], Rudin, C.[Cynthia],
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography,
DEF-AI-MIA24(5003-5009)
IEEE DOI 2410
Deep learning, Accuracy, Computational modeling, Prototypes, Computer architecture, Radiology, mammography, cancer, breast cancer BibRef

Zhao, Z.W.[Zi-Wei], Wang, D.[Dong], Chen, Y.H.[Yi-Hong], Wang, Z.T.[Zi-Teng], Wang, L.W.[Li-Wei],
Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection,
ECCV22(XXI:384-400).
Springer DOI 2211
BibRef