21.7.2.9 Mammograms, MRI, Magnetic Resonance Imaging

Levman, J., Leung, T., Causer, P., Plewes, D., Martel, A.L.,
Classification of Dynamic Contrast-Enhanced Magnetic Resonance Breast Lesions by Support Vector Machines,
MedImg(27), No. 5, May 2008, pp. 688-696.
IEEE DOI 0711
BibRef

Gal, Y.[Yaniv], Mehnert, A.J.H.[Andrew J.H.], Bradley, A.P.[Andrew P.], McMahon, K., Kennedy, D.[Dominic], Crozier, S.[Stuart],
Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means,
MedImg(29), No. 2, February 2010, pp. 302-310.
IEEE DOI 1002
BibRef
Earlier: A1, A2, A3, A5, A6, Only:
Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the Breast,
DICTA09(132-139).
IEEE DOI 0912
BibRef

Nagarajan, M.B.[Mahesh B.], Huber, M.B.[Markus B.], Schlossbauer, T.[Thomas], Leinsinger, G.[Gerda], Krol, A.[Andrzej], Wismüller, A.[Axel],
Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement,
MVA(24), No. 7, October 2013, pp. 1371-1381.
Springer DOI 1309
BibRef
Earlier:
Classifying Small Lesions on Breast MRI through Dynamic Enhancement Pattern Characterization,
MLMI11(352-359).
Springer DOI 1109
BibRef

Soares, F., Janela, F., Pereira, M., Seabra, J., Freire, M.M.,
3D Lacunarity in Multifractal Analysis of Breast Tumor Lesions in Dynamic Contrast-Enhanced Magnetic Resonance Imaging,
IP(22), No. 11, 2013, pp. 4422-4435.
IEEE DOI 1310
biological organs BibRef

Platel, B., Mus, R., Welte, T., Karssemeijer, N., Mann, R.,
Automated Characterization of Breast Lesions Imaged With an Ultrafast DCE-MR Protocol,
MedImg(33), No. 2, February 2014, pp. 225-232.
IEEE DOI 1403
biomedical MRI BibRef

Ribes, S., Didierlaurent, D., Decoster, N., Gonneau, E., Risser, L., Feillel, V., Caselles, O.,
Automatic Segmentation of Breast MR Images Through a Markov Random Field Statistical Model,
MedImg(33), No. 10, October 2014, pp. 1986-1996.
IEEE DOI 1411
Markov processes BibRef

Khalvati, F., Gallego-Ortiz, C., Balasingham, S., Martel, A.L.,
Automated Segmentation of Breast in 3-D MR Images Using a Robust Atlas,
MedImg(34), No. 1, January 2015, pp. 116-125.
IEEE DOI 1502
biological organs BibRef

Rasti, R.[Reza], Teshnehlab, M.[Mohammad], Phung, S.L.[Son Lam],
Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks,
PR(72), No. 1, 2017, pp. 381-390.
Elsevier DOI 1708
Breast, cancer BibRef

Garcia, E., Diez, Y., Diaz, O., Llado, X., Gubern-Merida, A., Marti, R., Marti, J., Oliver, A.,
Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific Biomechanical Model,
MedImg(37), No. 3, March 2018, pp. 712-723.
IEEE DOI 1804
biological organs, biological tissues, biomechanics, biomedical MRI, elasticity, image registration, subject-specific biomechanical models BibRef

Kallel, I.K.[I. Khanfir], Almouahed, S., Solaiman, B., Bosse, E.,
An iterative possibilistic knowledge diffusion approach for blind medical image segmentation,
PR(78), 2018, pp. 182-197.
Elsevier DOI 1804
Possibilistic knowledge representation, Knowledge diffusion modeling, Iterative segmentation, Mammographic medical images BibRef

Zhang, L., Jiang, S., Zhao, Y., Feng, J., Pogue, B.W., Paulsen, K.D.,
Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,
MedImg(37), No. 5, May 2018, pp. 1247-1252.
IEEE DOI 1805
Breast, Cancer, Image reconstruction, Optical fibers, Tumors, Optical imaging, breast, image reconstruction, magnetic resonance imaging BibRef

Piantadosi, G.[Gabriele], Marrone, S.[Stefano], Fusco, R.[Roberta], Sansone, M.[Mario], Sansone, C.[Carlo],
Comprehensive computer-aided diagnosis for breast T1-weighted DCE-MRI through quantitative dynamical features and spatio-temporal local binary patterns,
IET-CV(12), No. 7, October 2018, pp. 1007-1017.
DOI Link 1809
BibRef

Gravina, M.[Michela], Marrone, S.[Stefano], Piantadosi, G.[Gabriele], Sansone, M.[Mario], Sansone, C.[Carlo],
3TP-CNN: Radiomics and Deep Learning for Lesions Classification in DCE-MRI,
CIAP19(II:661-671).
Springer DOI 1909
BibRef

Zhang, J., Saha, A., Zhu, Z., Mazurowski, M.A.[Maciej A.],
Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics,
MedImg(38), No. 2, February 2019, pp. 435-447.
IEEE DOI 1902
Image segmentation, Breast tumors, Lesions, Feature extraction, Breast cancer, molecular subtype classification BibRef

Zhu, Z.[Zhe], Mittendorf, A.[Amber], Shropshire, E.[Erin], Allen, B.[Brian], Miller, C.[Chad], Bashir, M.R.[Mustafa R.], Mazurowski, M.A.[Maciej A.],
3D Pyramid Pooling Network for Abdominal MRI Series Classification,
PAMI(44), No. 4, April 2022, pp. 1688-1698.
IEEE DOI 2203
Magnetic resonance imaging, Biomedical imaging, Liver, Task analysis, Convolutional neural networks, Annotations, 3D pyramid pooling network BibRef

Biswas, B.[Biswajit], Ghosh, S.K.[Swarup Kr], Ghosh, A.[Anupam],
A novel automated magnetic resonance image segmentation approach based on elliptical gamma mixture model for breast lumps detection,
IJIST(29), No. 4, 2019, pp. 599-616.
DOI Link 1911
breast hamartoma, computation unified device architecture (CUDA), semisupervised classifier BibRef

Whitney, H.M.[Heather M.], Li, H.[Hui], Ji, Y.[Yu], Liu, P.F.[Pei-Fang], Giger, M.L.[Maryellen L.],
Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods,
PIEEE(108), No. 1, January 2020, pp. 163-177.
IEEE DOI 2001
Feature extraction, Lesions, Biomedical imaging, Breast cancer, Cancer, Magnetic resonance imaging, Breast cancer, transfer learning BibRef

Wu, C., Hormuth, D.A., Oliver, T.A., Pineda, F., Lorenzo, G., Karczmar, G.S., Moser, R.D., Yankeelov, T.E.,
Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics,
MedImg(39), No. 9, September 2020, pp. 2760-2771.
IEEE DOI 2009
Magnetic resonance imaging, Tumors, Computational fluid dynamics, Computational modeling, Cancer, Hemodynamics, Tumor, 1D-3D coupled, diffusion MRI BibRef

Holste, G.[Gregory], Partridge, S.C.[Savannah C.], Rahbar, H.[Habib], Biswas, D.[Debosmita], Lee, C.I.[Christoph I.], Alessio, A.M.[Adam M.],
End-to-End Learning of Fused Image and Non-Image Features for Improved Breast Cancer Classification from MRI,
CVAMD21(3287-3296)
IEEE DOI 2112
Deep learning, Sensitivity, Magnetic resonance imaging, Receivers, Medical services, Predictive models BibRef

Li, A.J.[Ai-Jing], Pan, Y.N.[Yu-Ning], Chen, B.[Bin], Huang, R.[Rong], Xia, J.[Jianbi], Jin, Y.H.[Yin-Hua], Zheng, J.J.[Jian-Jun],
Evaluation of quantitative dynamic contrast-enhanced (DCE)-MRI parameters using a reference region model in invasive ductal carcinoma (IDC) patients,
IJIST(31), No. 1, 2021, pp. 215-222.
DOI Link 2102
breast invasive ductal carcinoma, DCE-MRI, prognostic factors BibRef

Shrivastava, N.[Neeraj], Bharti, J.[Jyoti],
Breast Tumor Detection in MR Images Based on Density,
IJIG(22), No. 1 2022, pp. 2250001.
DOI Link 2202
BibRef

Feng, B.[Bao], Zhou, H.Y.[Hao-Yang], Feng, J.[Jin], Chen, Y.H.[Ye-Hang], Liu, Y.[Yu], Yu, T.Y.[Tian-You], Liu, Z.S.[Zhuang-Sheng], Long, W.S.[Wan-Sheng],
Active contour model of breast cancer DCE-MRI segmentation with an extreme learning machine and a fuzzy C-means cluster,
IET-IPR(16), No. 11, 2022, pp. 2947-2958.
DOI Link 2208
BibRef

Stelter, J.K.[Jonathan K.], Boehm, C.[Christof], Ruschke, S.[Stefan], Weiss, K.[Kilian], Diefenbach, M.N.[Maximilian N.], Wu, M.M.[Ming-Ming], Borde, T.[Tabea], Schmidt, G.P.[Georg P.], Makowski, M.R.[Marcus R.], Fallenberg, E.M.[Eva M.], Karampinos, D.C.[Dimitrios C.],
Hierarchical Multi-Resolution Graph-Cuts for Water-Fat-Silicone Separation in Breast MRI,
MedImg(41), No. 11, November 2022, pp. 3253-3265.
IEEE DOI 2211
Fats, Chemicals, Estimation, Breast, In vivo, Spatial resolution, Magnetic resonance imaging, silicone implants BibRef

Pereira, T.M.C.[Teresa M. C.], Pelicano, A.C.[Ana Catarina], Godinho, D.M.[Daniela M.], Gonçalves, M.C.T.[Maria C. T.], Castela, T.[Tiago], Orvalho, M.L.[Maria Lurdes], Sencadas, V.[Vitor], Sebastião, R.[Raquel], Conceição, R.C.[Raquel C.],
Breast MRI Multi-tumor Segmentation Using 3d Region Growing,
CIARP23(II:15-29).
Springer DOI 2312
BibRef

Chen, Q.Q.[Qian-Qian], Zhang, J.D.[Jia-Dong], Meng, R.Q.[Run-Qi], Zhou, L.[Lei], Li, Z.H.[Zhen-Hui], Feng, Q.J.[Qian-Jin], Shen, D.G.[Ding-Gang],
Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis,
MedImg(43), No. 5, May 2024, pp. 1958-1971.
IEEE DOI Code:
WWW Link. 2405
Image segmentation, Magnetic resonance imaging, Tumors, Breast tumors, Medical diagnostic imaging, Breast cancer, disentanglement BibRef

Khaled, R.[Roa'a], Vidal, J.[Joel], Martí, R.[Robert],
Deep Learning Based Segmentation of Breast Lesions in DCE-MRI,
AIHA20(417-430).
Springer DOI 2103
BibRef

Aprea, F.[Federica], Marrone, S.[Stefano], Sansone, C.[Carlo],
Neural Machine Registration for Motion Correction in Breast DCE-MRI,
ICPR21(4332-4339)
IEEE DOI 2105
Face recognition, Brightness, Dynamics, Focusing, Artificial neural networks, Distortion, Physiology BibRef

Galli, A.[Antonio], Gravina, M.[Michela], Marrone, S.[Stefano], Piantadosi, G.[Gabriele], Sansone, M.[Mario], Sansone, C.[Carlo],
Evaluating Impacts of Motion Correction on Deep Learning Approaches for Breast DCE-MRI Segmentation and Classification,
CAIP19(II:294-304).
Springer DOI 1909
BibRef

Soleimani, H.[Hossein], Rincon, J.[Jose], Michailovich, O.V.[Oleg V.],
Segmentation of Breast MRI Scans in the Presence of Bias Fields,
ICIAR19(I:376-387).
Springer DOI 1909
BibRef

Piantadosi, G., Sansone, M., Sansone, C.,
Breast Segmentation in MRI via U-Net Deep Convolutional Neural Networks,
ICPR18(3917-3922)
IEEE DOI 1812
Image segmentation, Breast, Task analysis, Proposals, Convolution BibRef

Fabijanska, A.[Anna], Vacavant, A.[Antoine], Lebre, M.A.[Marie-Ange], Pavan, A.L.M.[Ana L. M.], de Pina, D.R.[Diana R.], Abergel, A.[Armand], Chabrot, P.[Pascal], Magnin, B.[Benoît],
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework,
ICCVG18(319-328).
Springer DOI 1810
BibRef

Comelli, A.[Albert], Bruno, A.[Alessandro], di Vittorio, M.L.[Maria Laura], Ienzi, F.[Federica], Lagalla, R.[Roberto], Vitabile, S.[Salvatore], Ardizzone, E.[Edoardo],
Automatic Multi-seed Detection for MR Breast Image Segmentation,
CIAP17(I:706-717).
Springer DOI 1711
BibRef

Marrone, S., Piantadosi, G., Fusco, R., Petrillo, A., Sansone, M., Sansone, C.,
Breast segmentation using Fuzzy C-Means and anatomical priors in DCE-MRI,
ICPR16(1472-1477)
IEEE DOI 1705
Breast, Heart, Image edge detection, Image segmentation, Lesions, Muscles, Breast DCE-MRI, Fuzzy C-Means, Segmentation BibRef

Tzalavra, A.[Alexia], Dalakleidi, K.[Kalliopi], Zacharaki, E.I.[Evangelia I.], Tsiaparass, N.[Nikolaos], Constantinidis, F.[Fotios], Paragios, N.[Nikos], Nikita, K.S.[Konstantina S.],
Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI,
MLMI16(296-304).
Springer DOI 1611
BibRef

Razavi, M.[Mohammad], Wang, L.[Lei], Tan, T.[Tao], Karssemeijer, N.[Nico], Linsen, L.[Lars], Frese, U.[Udo], Hahn, H.K.[Horst K.], Zachmann, G.[Gabriel],
Novel Morphological Features for Non-mass-like Breast Lesion Classification on DCE-MRI,
MLMI16(305-312).
Springer DOI 1611
BibRef

Urbán, S.[Szabolcs], Ruskó, L.[László], Nagy, A.[Antal],
A Self-learning Tumor Segmentation Method on DCE-MRI Images,
ICIAR16(591-598).
Springer DOI 1608
BibRef

Razavi, M.[Mohammad], Wang, L.[Lei], Gubern-Mérida, A.[Albert], Ivanovska, T.[Tatyana], Laue, H.[Hendrik], Karssemeijer, N.[Nico], Hahn, H.K.[Horst K.],
Towards Accurate Segmentation of Fibroglandular Tissue in Breast MRI Using Fuzzy C-Means and Skin-Folds Removal,
CIAP15(I:528-536).
Springer DOI 1511
BibRef

Maken, F.A., Gal, Y., McClymont, D., Bradley, A.P.,
Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging,
DICTA14(1-8)
IEEE DOI 1502
biomedical MRI BibRef

Liu, Y.P.[Yi-Ping], Liu, H.[Hui], Zhao, Z.W.[Zuo-Wei], Zhang, L.[Lina], Liu, X.[Xiang],
A new active contour model-based segmentation approach for accurate extraction of the lesion from breast DCE-MRI,
ICIP13(1140-1143)
IEEE DOI 1402
Active contours BibRef

Srikantha, A.[Abhilash],
Symmetry-Based Detection and Diagnosis of DCIS in Breast MRI,
GCPR13(255-260).
Springer DOI 1311
BibRef

Marrone, S.[Stefano], Piantadosi, G.[Gabriele],
Automatic Lesion Detection in Breast DCE-MRI,
CIAP13(II:359-368).
Springer DOI 1309
BibRef

Liang, X.[Xi], Ramamohanara, K., Frazer, H., Yang, Q.[Qing],
Lesion Segmentation in Dynamic Contrast Enhanced MRI of Breast,
DICTA12(1-8).
IEEE DOI 1303
BibRef

Marrone, S.[Stefano], Piantadosi, G.[Gabriele], Fusco, R.[Roberta], Petrillo, A.[Antonella], Sansone, M.[Mario], Sansone, C.[Carlo],
An Investigation of Deep Learning for Lesions Malignancy Classification in Breast DCE-MRI,
CIAP17(II:479-489).
Springer DOI 1711
BibRef

Piantadosi, G.[Gabriele], Fusco, R.[Roberta], Petrillo, A.[Antonella], Sansone, M.[Mario], Sansone, C.[Carlo],
LBP-TOP for Volume Lesion Classification in Breast DCE-MRI,
CIAP15(I:647-657).
Springer DOI 1511
BibRef
Earlier: A2, A4, A3, A5, Only:
A Multiple Classifier System for Classification of Breast Lesions Using Dynamic and Morphological Features in DCE-MRI,
SSSPR12(684-692).
Springer DOI 1211
BibRef

Gravina, M.[Michela], Marrone, S.[Stefano], Sansone, M.[Mario], Sansone, C.[Carlo],
DAE-CNN: Exploiting and disentangling contrast agent effects for breast lesions classification in DCE-MRI,
PRL(145), 2021, pp. 67-73.
Elsevier DOI 2104
BibRef

Fusco, R.[Roberta], Sansone, M.[Mario], Sansone, C.[Carlo], Petrillo, A.[Antonella],
Selection of Suspicious ROIs in Breast DCE-MRI,
CIAP11(I: 48-57).
Springer DOI 1109
BibRef

Tao, Y.[Yimo], Xuan, J.H.[Jian-Hua], Freedman, M.T.[Matthew T.], Chepko, G.[Gloria], Shields, P.G.[Peter G.], Wang, Y.[Yue],
Imaging biomarker analysis of rat mammary fat pads and glandular tissues in MRI images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Meyer-Baese, A., Lange, O., Schlossbauer, T., Wismuller, A.,
Computer-aided diagnosis and visualization based on clustering and independent component analysis for breast MRI,
ICIP08(3000-3003).
IEEE DOI 0810
BibRef

d'Elia, C., Marrocco, C., Molinara, M., Poggi, G., Scarpa, G., Tortorella, F.,
Detection of microcalcifications clusters in mammograms through TS-MRF segmentation and SVM-based classification,
ICPR04(III: 742-745).
IEEE DOI 0409
BibRef

Marrocco, C.[Claudio], Molinara, M.[Mario], Tortorella, F.[Francesco],
Exploring Cascade Classifiers for Detecting Clusters of Microcalcifications,
CIAP11(I: 384-392).
Springer DOI 1109
BibRef

Marrocco, C.[Claudio], Molinara, M.[Mario], Tortorella, F.[Francesco],
Algorithms for Detecting Clusters of Microcalcifications in Mammograms,
CIAP05(884-891).
Springer DOI 0509
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Mammograms, Three Dimensional Analysis, Registration .