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Distal locking; Fluoroscopy; Pose estimation; Parameter decomposition;
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Bone age assessment
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Bones
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Kranzfelder, M.[Michael],
Schneider, A.[Armin],
Fua, P.[Pascal],
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1610
biological tissues
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Onchis, D.M.[Darian Moaca],
Zappalá, S.[Simone],
Gotia, S.L.[Smaranda Laura],
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Generalized Hough transform
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Peng, S.L.,
Wu, J.,
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Chuang, K.S.,
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Attenuation, Bone tissue, Bones, Computed tomography, Osteoporosis,
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bone equivalent uniform phantom, bone mineral density,
bone volume fraction, computed tomography, lumbar vertebra,
partial, volume, effect
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Behrooz, A.,
Kask, P.,
Meganck, J.,
Kempner, J.,
Automated Quantitative Bone Analysis in In Vivo X-ray Micro-Computed
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IEEE DOI
1709
computerised tomography, 3D microcomputed tomography volume,
bone morphometry,
joint contrast, metaphyseal growth plate,
slice-by-slice stereological measurement,
watershed segmentation, Bone morphometry,
hybrid thresholding, medial axis, object segmentation,
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Skeletal Shape Correspondence Through Entropy,
MedImg(37), No. 1, January 2018, pp. 1-11.
IEEE DOI
1801
biomedical MRI, bone, brain, entropy, medical image processing,
statistical distributions, boundary points,
skeletal model
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Sabeti, M.[Malihe],
Boostani, R.[Reza],
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IP(28), No. 1, January 2019, pp. 127-141.
IEEE DOI
1810
bone, image classification, image enhancement, image fusion,
medical image processing, X-ray imaging, component attenuation,
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Ai, Z.[Zexiu],
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Elsevier DOI
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Image quality, Wushu, Joint damage, Quantitative CT
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Matsubara, R.[Ryota],
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Fukuda, T.[Takaichi],
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1904
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1909
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1911
bone age assessment, convolutional neural network,
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Buonamici, F.[Francesco],
Furferi, R.[Rocco],
Governi, L.[Lapo],
Lazzeri, S.[Simone],
McGreevy, K.S.[Kathleen S.],
Servi, M.[Michaela],
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2002
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Deep-learning for Tidemark Segmentation in Human Osteochondral Tissues
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Springer DOI
2003
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Farzi, M.,
Pozo, J.M.,
McCloskey, E.,
Eastell, R.,
Harvey, N.,
Wilkinson, J.M.,
Frangi, A.F.,
A Spatio-Temporal Ageing Atlas of the Proximal Femur,
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IEEE DOI
2005
Bones, Aging, Shape, Calibration, Biomedical measurement,
Signal to noise ratio, Osteoporosis, Spatio-temporal atlas, DXA,
osteoporosis
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Zhang, Y.,
Li, F.,
Qiu, L.,
Xu, L.,
Niu, X.,
Sui, Y.,
Zhang, S.,
Zhang, Q.,
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Toward Precise Osteotomies: A Coarse-to-Fine 3D Cut Plane Planning
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IEEE DOI
2005
Tumors, Planning, Bones, Surgery, Pelvis,
Cut plane planning, 3D cut plane refinement
BibRef
Kayal, E.B.[Esha Baidya],
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Sharma, R.[Raju],
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2005
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Eslami, M.,
Tabarestani, S.,
Albarqouni, S.,
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Navab, N.,
Adjouadi, M.,
Image-to-Images Translation for Multi-Task Organ Segmentation and
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IEEE DOI
2007
Bone suppression, chest X-Ray, CXR imaging,
image-to-image translation, image-to-images translation, pix2pix
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Li, H.,
Han, H.,
Li, Z.,
Wang, L.,
Wu, Z.,
Lu, J.,
Zhou, S.K.,
High-Resolution Chest X-Ray Bone Suppression Using Unpaired CT
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MedImg(39), No. 10, October 2020, pp. 3053-3063.
IEEE DOI
2010
Bones, Computed tomography, Computational modeling,
Image resolution, Manuals, Computational efficiency,
unsupervised leaning
BibRef
Liu, C.,
Xie, H.,
Zhang, S.,
Mao, Z.,
Sun, J.,
Zhang, Y.,
Misshapen Pelvis Landmark Detection With Local-Global Feature
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MedImg(39), No. 12, December 2020, pp. 3944-3954.
IEEE DOI
2012
Pelvis, Feature extraction, Deep learning, Hip, X-ray imaging,
Medical diagnostic imaging, Developmental dysplasia of the hip,
medical imaging
BibRef
Nikan, S.,
van Osch, K.,
Bartling, M.,
Allen, D.G.,
Rohani, S.A.,
Connors, B.,
Agrawal, S.K.,
Ladak, H.M.,
PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of
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IP(30), 2021, pp. 739-753.
IEEE DOI
2012
Image segmentation, Bones, Computed tomography, Shape,
Biomedical imaging, Training, PWD-3DNet
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Zehani, S.[Soraya],
Ouahabi, A.[Abdeldjalil],
Oussalah, M.[Mourad],
Mimi, M.[Malika],
Taleb-Ahmed, A.[Abdelmalik],
Bone microarchitecture characterization based on fractal analysis in
spatial frequency domain imaging,
IJIST(31), No. 1, 2021, pp. 141-159.
DOI Link
2102
discrete cosine transform (DCT), fractal analysis,
medical imaging, osteoporosis, trabecular bone texture
BibRef
Rafati, M.[Mehravar],
Farnia, F.[Fateme],
Romoozi, E.[Elham],
Nickfarjam, A.M.[Ali Mohammad],
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Measuring panoramic radiomorphometric indices for mandible bone using
active shape model and Bayesian information criterion-support vector
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IJIST(31), No. 3, 2021, pp. 1711-1730.
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2108
cortex thickness, mandible, panoramic system, ramus
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Mao, K.[Keji],
Chen, L.J.[Li-Jian],
Wang, M.[Minhao],
Xu, R.[Ruiji],
Zhao, X.M.[Xiao-Min],
Classification of hand-wrist maturity level based on similarity
matching,
IET-IPR(15), No. 12, 2021, pp. 2866-2879.
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2109
BibRef
Liu, C.B.[Chuan-Bin],
Xie, H.T.[Hong-Tao],
Zhang, Y.D.[Yong-Dong],
Self-Supervised Attention Mechanism for Pediatric Bone Age Assessment
With Efficient Weak Annotation,
MedImg(40), No. 10, October 2021, pp. 2685-2697.
IEEE DOI
2110
Annotations, Bones, Deep learning, Feature extraction, Task analysis,
Standards, Radiography, Bone age assessment, region of interest,
weak annotation
BibRef
Liu, R.[Ruhan],
Liu, M.Y.[Meng-Yao],
Sheng, B.[Bin],
Li, H.[Huating],
Li, P.[Ping],
Song, H.T.[Hai-Tao],
Zhang, P.[Ping],
Jiang, L.X.[Li-Xin],
Shen, D.G.[Ding-Gang],
NHBS-Net: A Feature Fusion Attention Network for Ultrasound Neonatal
Hip Bone Segmentation,
MedImg(40), No. 12, December 2021, pp. 3446-3458.
IEEE DOI
2112
Image segmentation, Hip, Ultrasonic imaging, Bones,
Feature extraction, Standards, Pediatrics,
medical image segmentation
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Wang, S.Q.[Shu-Qiang],
Wang, X.Y.[Xiang-Yu],
Shen, Y.Y.[Yan-Yan],
He, B.[Bing],
Zhao, X.Y.[Xin-Yan],
Cheung, P.W.H.[Prudence Wing-Hang],
Cheung, J.P.Y.[Jason Pui Yin],
Luk, K.D.K.[Keith Dip-Kei],
Hu, Y.[Yong],
An Ensemble-Based Densely-Connected Deep Learning System for
Assessment of Skeletal Maturity,
SMCS(52), No. 1, January 2022, pp. 426-437.
IEEE DOI
2112
Bones, Training, Deep learning, Task analysis, Radiography,
Neural networks, Image segmentation, skeletal maturity
BibRef
Chen, Y.R.[Yi-Ren],
He, K.J.[Kun-Jin],
Hao, B.[Bo],
Weng, Y.P.[Yi-Ping],
Chen, Z.M.[Zheng-Ming],
FractureNet: A 3D Convolutional Neural Network Based on the
Architecture of m-Ary Tree for Fracture Type Identification,
MedImg(41), No. 5, May 2022, pp. 1196-1207.
IEEE DOI
2205
Bones, Feature extraction,
Image segmentation, Convolutional neural networks, X-ray imaging,
constrained r-combination strategy
BibRef
Li, X.G.[Xiao-Guang],
Zhou, Y.C.[Yi-Chao],
Yin, H.X.[Hong-Xia],
Wang, Z.C.[Zhen-Chang],
Zhuo, L.[Li],
Zhang, H.[Hui],
Detecting Absence of Bone Wall in Jugular Bulb by Image
Transformation Surrogate Tasks,
MedImg(41), No. 6, June 2022, pp. 1358-1370.
IEEE DOI
2206
Task analysis, Feature extraction, Anomaly detection, Bones,
Biomedical imaging, Computed tomography,
surrogate tasks
BibRef
Lee, C.H.[Cheng-Hsien],
Hu, Y.H.[Yu Hen],
Bao, S.[Stephen],
Radwin, R.G.[Robert G.],
Video-Based Automatic Wrist Flexion and Extension Classification,
HMS(52), No. 5, October 2022, pp. 824-832.
IEEE DOI
2209
Wrist, Estimation error, Task analysis, Video recording, Sensitivity,
Tracking, Prediction algorithms, Classification, ergonomics,
wrist posture
BibRef
Akdogan, V.[Volkan],
Özkaner, V.[Vedat],
Alkurt, F.Ö.[Fatih Özkan],
Karaaslan, M.[Muharrem],
Theoretical and experimental sensing of bone healing by microwave
approach,
IJIST(32), No. 6, 2022, pp. 2255-2261.
DOI Link
2212
antennas, bone healing, measurement, microwave, simulation
BibRef
Wang, F.[Fakai],
Zheng, K.[Kang],
Lu, L.[Le],
Xiao, J.[Jing],
Wu, M.[Min],
Kuo, C.F.[Chang-Fu],
Miao, S.[Shun],
Lumbar Bone Mineral Density Estimation From Chest X-Ray Images:
Anatomy-Aware Attentive Multi-ROI Modeling,
MedImg(42), No. 1, January 2023, pp. 257-267.
IEEE DOI
2301
Bones, Osteoporosis, Feature extraction, X-ray imaging, Transformers,
Biomedical imaging, Estimation, Bone mineral density,
multi-ROI modeling
BibRef
Ding, W.L.[Wei-Long],
Zong, Z.Y.[Ze-Yong],
Ding, X.[Xiao],
Mao, K.J.[Ke-Ji],
Hamate classification method based on feature-enhanced residual
network and probabilistic joint judgment,
IET-IPR(17), No. 3, 2023, pp. 819-831.
DOI Link
2303
Age from wrist bone analysis.
Chinese method, classification of hamate, feature enhancement,
probability association, residual network
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Sahin, M.E.[Muhammet Emin],
Image processing and machine learning-based bone fracture detection
and classification using X-ray images,
IJIST(33), No. 3, 2023, pp. 853-865.
DOI Link
2305
bone fracture, classification, feature extraction,
image processing, machine learning, X-ray images
BibRef
Liu, H.C.[Han-Chao],
Liu, Y.[Yuhe],
Mu, T.J.[Tai-Jiang],
Huang, X.L.[Xiao-Lei],
Hu, S.M.[Shi-Min],
Skeleton-CutMix: Mixing Up Skeleton With Probabilistic Bone Exchange
for Supervised Domain Adaptation,
IP(32), 2023, pp. 4046-4058.
IEEE DOI
2307
Skeleton, Bones, Task analysis, Adaptation models,
Target recognition, Joints, Data models,
supervised domain adaptation
BibRef
He, K.[Keke],
Gou, F.F.[Fang-Fang],
Wu, J.[Jia],
Image segmentation technology based on transformer in medical
decision-making system,
IET-IPR(17), No. 10, 2023, pp. 3040-3054.
DOI Link
2308
biomedical MRI, bone, CAD, image segmentation,
learning (artificial intelligence)
BibRef
Zeng, B.[Bolun],
Wang, H.X.[Hui-Xiang],
Xu, J.C.[Jiang-Chang],
Tu, P.[Puxun],
Joskowicz, L.[Leo],
Chen, X.J.[Xiao-Jun],
Two-Stage Structure-Focused Contrastive Learning for Automatic
Identification and Localization of Complex Pelvic Fractures,
MedImg(42), No. 9, September 2023, pp. 2751-2762.
IEEE DOI
2310
BibRef
Amasya, H.[Hakan],
Jaju, P.P.[Prashant Prakash],
Ezhov, M.[Matvey],
Gusarev, M.[Maxim],
Atakan, C.[Cemal],
Sanders, A.[Alex],
Manulius, D.[David],
Golitskya, M.[Maria],
Shrivastava, K.[Kriti],
Singh, A.[Ajita],
Gupta, A.[Anuja],
Önder, M.[Merve],
Orhan, K.[Kaan],
Development and validation of an artificial intelligence software for
periodontal bone loss in panoramic imaging,
IJIST(34), No. 1, 2024, pp. e22973.
DOI Link
2401
alveolar bone, dentistry, periodontal diagnostics,
periodontal disease, radiography
BibRef
Chang, C.[Cheng],
Sun, H.[Hao],
Guo, X.[Xin],
Sun, Z.H.[Zhen-Hui],
Wu, M.K.[Meng-Kun],
Yin, M.H.[Ming-Huan],
An, B.C.[Bai-Chuan],
Zhuang, C.[Chao],
An automatic measurement method for ankle key angles based on point
cloud segmentation network,
IJIST(34), No. 1, 2024, pp. e22961.
DOI Link
2401
automatic measurement, computer-aided measurement,
CT tomography, deep learning, point cloud segmentation network
BibRef
Vijayaraj, J.,
Abirami, B.,
Mohanty, S.N.[Sachi Nandan],
Kavitha, V.P.,
An efficient convolutional histogram-oriented gradients and deep
convolutional learning approach for accurate classification of bone
cancer,
IJIST(34), No. 2, 2024, pp. e23000.
DOI Link
2402
bone cancer, deep learning, feature extraction, ROI extraction
BibRef
Wu, J.D.[Jin-Dong],
Mi, Q.Z.[Qun-Zheng],
Zhang, Y.[Yi],
Wu, T.N.[Tong-Ning],
SVTNet: Automatic bone age assessment network based on TW3 method and
vision transformer,
IJIST(34), No. 2, 2024, pp. e22990.
DOI Link
2402
attention mechanism, bone age assessment (BAA),
clinical interpretability, Tanner-Whitehouse 3 (TW3), vision transformer
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Zhang, Q.X.[Qi-Xiang],
Wang, S.P.[Shun-Ping],
Chan, Y.W.[Yu-Wei],
Chang, C.H.[Chih-Hung],
Image Detection of Rare Orthopedic Diseases based on Explainable AI,
IoTDesign24(654-659)
IEEE DOI
2404
Training, Heating systems, Adaptation models, Visualization,
Medical services, Robustness, Real-time systems
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Feng, Q.Y.[Qian-Yu],
Cao, Y.C.[Yong-Chun],
Lin, Q.[Qiang],
Man, Z.X.[Zheng-Xing],
He, Y.[Yang],
Liu, C.[ChengYang],
SPECT bone scan image classification by fusing multi-attention
mechanism with deep residual networks,
CVIDL23(47-51)
IEEE DOI
2403
Measurement, Nuclear medicine, Computational modeling, Bones,
Feature extraction, Transformers, Metastasis, SPECT, CBAM, CoT, ResNet
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Wu, T.[Tao],
Luo, R.[Renze],
Lin, H.Y.[Hong-Yu],
Yu, H.[Hong],
Wang, Q.S.[Qing-Song],
Liu, H.[Heng],
Research on focal segmentation of bone scan based on Swin Transformer,
CVIDL23(426-430)
IEEE DOI
2403
Deep learning, Image segmentation, Imaging, Medical services, Bones,
Transformers, Feature extraction, bone scan, leision segmention, feature fusion
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Phillips, R.[Robert],
Dittmer, K.[Keren],
Smith, R.[Rachel],
Baer, K.[Kenzie],
Butler, A.[Anthony],
Aligning Bone Tumour Radiology and Histology Data - How Could We
Close the Loopƒ,
IVCNZ23(1-6)
IEEE DOI
2403
Histopathology, Surgery, Morphology, Radiology, Bones, Oncology,
Planning, Radiopathology, Patient-Specific Treatments,
Methodology
BibRef
Guarnera, F.[Francesco],
Rondinella, A.[Alessia],
Giudice, O.[Oliver],
Ortis, A.[Alessandro],
Battiato, S.[Sebastiano],
Rundo, F.[Francesco],
Fallica, G.[Giorgio],
Traina, F.[Francesco],
Conoci, S.[Sabrina],
Early Detection of Hip Periprosthetic Joint Infections Through CNN on
Computed Tomography Images,
CIAP23(II:134-143).
Springer DOI
2312
BibRef
Lee, K.[Kyungsu],
Lew, H.M.[Hah Min],
Lee, M.H.[Moon Hwan],
Kim, J.Y.[Jun-Young],
Hwang, J.Y.[Jae Youn],
CSS-Net: Classification and Substitution for Segmentation of Rotator
Cuff Tear,
ACCV22(VI:101-114).
Springer DOI
2307
BibRef
Shi, K.J.[Kang-Jian],
Wu, F.[Fuli],
Gong, J.[Jing],
Hao, P.Y.[Peng-Yi],
Deep Guided Context-aware Network for Anomaly Detection in
Musculoskeletal Radiographs,
ICPR22(4493-4499)
IEEE DOI
2212
Radiography, Location awareness, Knowledge engineering,
Musculoskeletal system, Visualization, X-rays, Feature extraction,
prior knowledge
BibRef
Do, T.C.[Thanh-Cong],
Yang, H.J.[Hyung-Jeong],
Kang, S.R.[Sae-Ryung],
Kim, S.H.[Soo-Hyung],
Lee, G.S.[Guee-Sang],
Min, J.J.[Jung-Joon],
Positional Multi-Cross-Attention for Bone Age Estimation Using Deep
Multiple Instance Learning,
ICPR22(4285-4291)
IEEE DOI
2212
Learning systems, Deep learning, Hospitals, Estimation,
Network architecture, Aging, Bones, Bone age estamation, Attention mechanism
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Xie, Z.Z.[Zhao-Zhi],
Zhao, K.[Keyang],
Yan, X.[Xu],
Wu, S.[Shenghui],
Mei, J.[Jiong],
Lu, H.T.[Hong-Tao],
Merged U-Net for Bone Tumors X-Ray Images Segmentation,
ICIP22(1276-1280)
IEEE DOI
2211
Location awareness, Image segmentation, Medical services,
Logic gates, Bones, Lesions, X-ray imaging, Bone tumors, Merged u-net,
Hierarchical feature aggregation
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Jabbar, A.J.[Alaa Jamal],
Abdulmunem, A.A.[Ashwan A.],
Bone age assessment using deep learning architecture: A Survey,
ISCV22(1-6)
IEEE DOI
2208
Wrist, Pediatrics, Magnetic resonance imaging, Forensics, Surgery,
Ultrasonography, Bones, Bone age assessment, Conventional network,
Deep Pre-Trained Models.
BibRef
Bütüner, S.,
Sehirli, E.,
Comparison of Segmentation Methods Used for Bone Fracture Images,
SmartCityApp21(137-141).
DOI Link
2201
BibRef
Asiri, Z.M.[Zayed M.],
Martin, B.L.[Brianna L.],
Bottema, M.J.[Murk J.],
Branch Profiles for Shape Analysis,
DICTA20(1-7)
IEEE DOI
2201
Shape, Digital images, Rats, Biology, Cancellous bone, Strain
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Pratiwi, A.[Annisa],
Phu, S.N.[Sinh Nguyen],
Essomba, T.[Terence],
Nurahmi, L.[Latifah],
Comparisons of Hybrid Mechanisms Based on Their Singularities for Bone
Reduction Surgery: 3-PRP-3-RPS and 3-RPS-3-PRP,
DHM21(I:152-162).
Springer DOI
2108
BibRef
Jia, Y.[Yang],
Du, H.R.[Han-Rong],
Wang, H.J.[Hai-Juan],
Chen, W.G.[Wei-Guang],
Jin, X.H.[Xiao-Hui],
Qi, W.[Wei],
Yang, B.[Bin],
Zhang, Q.J.[Qiu-Juan],
A Survey of Deep Learning Based Fully Automatic Bone Age Assessment
Algorithms,
DLPR20(688-702).
Springer DOI
2103
BibRef
Li, K.R.,
Hsung, T.C.,
Yeung, A.W.K.,
Bornstein, M.M.,
On Segmentation of Maxillary Sinus Membrane using Automatic Vertex
Screening,
VCIP20(108-111)
IEEE DOI
2102
biomembranes, bone, computerised tomography, image reconstruction,
image segmentation, medical image processing, sinus bone cavity,
cone beam computed tomography
BibRef
Wu, S.,
Yan, L.,
Liu, X.,
Yu, Y.,
Zhang, S.,
An End-To-End Network For Detecting Multi-Domain Fractures On X-Ray
Images,
ICIP20(448-452)
IEEE DOI
2011
X-ray imaging, Feature extraction, Training, Bones, Wrist,
Task analysis, Radiography, X-ray Images, Fracture Detection,
Feature Enhancement
BibRef
Chen, H.M.[Hao-Min],
Wang, Y.R.[Yi-Rui],
Zheng, K.[Kang],
Li, W.J.[Wei-Jian],
Chang, C.T.[Chi-Tung],
Harrison, A.P.[Adam P.],
Xiao, J.[Jing],
Hager, G.D.[Gregory D.],
Lu, L.[Le],
Liao, C.H.[Chien-Hung],
Miao, S.[Shun],
Anatomy-aware Siamese Network: Exploiting Semantic Asymmetry for
Accurate Pelvic Fracture Detection in X-ray Images,
ECCV20(XXIII:239-255).
Springer DOI
2011
BibRef
Pelka, O.,
Nensa, F.,
Friedrich, C.M.,
Branding: Fusion of Meta Data and Musculoskeletal Radiographs for
Multi-Modal Diagnostic Recognition,
VRMI19(467-475)
IEEE DOI
2004
Radiology, Training, Diagnostic radiography,
Medical diagnostic imaging, Machine learning, Data Fusion,
Radiographs
BibRef
Gong, Y.,
Yin, H.,
Liu, J.,
Liu, B.,
Qiu, G.,
Soft Tissue Removal in X-Ray Images by Half Window Dark Channel Prior,
ICIP19(3576-3580)
IEEE DOI
1910
X-ray, bone, soft tissue, half window
BibRef
Fiorentino, M.C.[Maria Chiara],
Moccia, S.[Sara],
Cipolletta, E.[Edoardo],
Filippucci, E.[Emilio],
Frontoni, E.[Emanuele],
A Learning Approach for Informative-Frame Selection in US Rheumatology
Images,
NTIAP19(228-236).
Springer DOI
1909
BibRef
Min, Z.[Zhe],
Meng, M.Q.H.[Max Q.H.],
Joint Alignment of Multiple Generalized Point Sets with Anisotropic
Positional Uncertainty Based on Expectation Maximization,
3DV18(170-179)
IEEE DOI
1812
bone, computerised tomography,
expectation-maximisation algorithm, Gaussian distribution,
Expectation Maximization
BibRef
Su, R.,
Chen, W.,
Wei, L.,
Li, X.,
Jin, Q.,
Tao, W.,
Encoded Texture Features to Characterize Bone Radiograph Images,
ICPR18(3856-3861)
IEEE DOI
1812
Bones, Feature extraction, Osteoporosis, Radio frequency,
Kernel, Task analysis, Encoded features, Gabor
BibRef
Kociolek, M.[Marcin],
Piórkowski, A.[Adam],
Obuchowicz, R.[Rafal],
Kaminski, P.[Pawel],
Strzelecki, M.[Michal],
Lytic Region Recognition in Hip Radiograms by Means of Statistical
Dominance Transform,
ICCVG18(349-360).
Springer DOI
1810
BibRef
Calderon, S.,
Fallas, F.,
Zumbado, M.,
Tyrrell, P.N.,
Stark, H.,
Emersic, Z.,
Meden, B.,
Solis, M.,
Assessing the Impact of the Deceived Non Local Means Filter as a
Preprocessing Stage in a Convolutional Neural Network Based Approach
for Age Estimation Using Digital Hand X-Ray Images,
ICIP18(1752-1756)
IEEE DOI
1809
X-ray imaging, Feature extraction, Noise reduction, Training,
Estimation, Bones, Biomedical imaging, x-rays, neural networks,
convolution
BibRef
Yunpeng, L.,
Wenli, C.,
Guobin, H.,
Renfang, W.,
Ran, J.,
Automatic Segmentation of Shoulder Joint in MRI Using Patch-Based and
Fully Convolutional Networks,
ICIP18(3508-3512)
IEEE DOI
1809
Bones, Image segmentation, Magnetic resonance imaging, Head,
Shoulder, Joints, Biomedical imaging, deep learning,
magnetic resonance imaging
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Hosseinian, S.,
Arefi, H.,
Photogrammetry in 3D Modelling of Human Bone Structures From
Radiographs,
PTVSBB17(115-121).
DOI Link
1805
BibRef
Zhou, J.L.[Jian-Long],
Li, Z.L.[Ze-Lin],
Zhi, W.M.[Wei-Ming],
Liang, B.[Bin],
Moses, D.[Daniel],
Dawes, L.[Laughlin],
Using Convolutional Neural Networks and Transfer Learning for Bone
Age Classification,
DICTA17(1-6)
IEEE DOI
1804
bone, convolution, diagnostic radiography, image classification,
learning (artificial intelligence), medical image processing,
X-ray imaging
BibRef
Malladi, S.P.K.[Sai Phani Kumar],
Veduruparthi, B.K.[Bijju Kranthi],
Mukherjee, J.[Jayanta],
Das, P.P.[Partha Pratim],
Chakrabarti, S.[Saswat],
Mallick, I.[Indranil],
Automated Measurement of Translational Margins and Rotational Shifts in
Pelvic Structures Using CBCT Images of Rectal Cancer Patients,
PReMI17(103-109).
Springer DOI
1711
BibRef
Kaipala, J.[Jukka],
López, M.B.[Miguel Bordallo],
Saarakkala, S.[Simo],
Thevenot, J.[Jérôme],
Automatic Segmentation of Bone Tissue from Computed Tomography Using a
Volumetric Local Binary Patterns Based Method,
SCIA17(II: 221-232).
Springer DOI
1706
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Haddad, O.,
Leboucher, J.,
Troccaz, J.,
Stindel, E.,
Initialized Iterative Closest Point for bone recognition in
ultrasound volumes,
ICPR16(2801-2806)
IEEE DOI
1705
Bones, Computed tomography, Iterative closest point algorithm,
Probes, Shape, Ultrasonic, imaging
BibRef
Shigeta, H.,
Mashita, T.,
Kikuta, J.,
Seno, S.,
Takemura, H.,
Matsuda, H.,
Ishii, M.,
A bone marrow cavity segmentation method using wavelet-based texture
feature,
ICPR16(2133-2138)
IEEE DOI
1705
Biomedical imaging, Bones, Cavity resonators, Image segmentation,
Manuals, Microscopy, Support, vector, machines
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Masson-Sibut, A.,
Nakib, A.,
Real-time assessment of bone structure positions via ultrasound imaging,
RealTimeIP(13), No. 1, March 2017, pp. 135-145.
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Cros, O.,
Eklund, A.,
Gaihede, M.,
Knutsson, H.,
Enhancement of micro-channels within the human mastoid bone based on
local structure tensor analysis,
IPTA16(1-6)
IEEE DOI
1703
blood
BibRef
Gontar, A.,
Williams, S.,
Bottema, M.J.,
Characterising 3D Structure of Cancellous Bone,
DICTA16(1-7)
IEEE DOI
1701
Bones
BibRef
Bielecka, M.[Marzena],
Piórkowski, A.[Adam],
Optimization of Numerical Calculations of Geometric Features of a Curve
Describing Preprocessed X-Ray Images of Bones as a Starting Point for
Syntactic Analysis of Finger Bone Contours,
ICCVG16(365-376).
Springer DOI
1611
BibRef
Manzano, F.M.[Fernando Montoya],
Ayala-Raggi, S.E.[Salvador E.],
Sánchez-Urrieta, S.[Susana],
Barreto-Flores, A.[Aldrin],
Portillo-Robledo, J.F.[José Francisco],
Bautista-López, V.E.[Verónica Edith],
Towards a Supervised Incremental Learning System for Automatic
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MCPR16(346-355).
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1608
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Dijia Wu, L.L.,
Lay, N.,
Liu, D.,
Nogues, I.,
Summers, R.M.,
Accurate 3D bone segmentation in challenging CT images:
Bottom-up parsing and contextualized optimization,
WACV16(1-10)
IEEE DOI
1606
Bones
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Nguyen, C.T.,
Havlicek, J.P.,
Chakrabarty, J.H.,
Duong, Q.,
Vesely, S.K.,
Towards automatic 3D bone marrow segmentation,
Southwest16(9-12)
IEEE DOI
1605
Biomedical measurement
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Ouertani, F.[Fatma],
Vazquez, C.[Carlos],
Cresson, T.[Thierry],
de Guise, J.[Jacques],
Simultaneous extraction of two adjacent bony structures in x-ray
images: Application to hip joint segmentation,
ICIP15(4555-4559)
IEEE DOI
1512
3D minimal path
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Kazeminia, S.,
Karimi, N.,
Mirmahboub, B.,
Soroushmehr, S.M.R.,
Samavi, S.,
Najarian, K.,
Bone extraction in X-ray images by analysis of line fluctuations,
ICIP15(882-886)
IEEE DOI
1512
X-ray; bone segmentation; edge detection; noise cancellation
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Awang, N.[Norazimah],
Sulaiman, R.[Riza],
Shapi'i, A.[Azrulhizam],
Rashid, A.H.A.[Abdul Halim Abdul],
Amran, M.F.M.[Mohd Fahmi Mohamad],
Osman, S.[Salyani],
A Comparative Study of Computer Aided System Preoperative Planning for
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IVIC15(189-198).
Springer DOI
1511
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Shao, C.W.,
Chiu, H.L.,
Chang, S.K.,
A Study on Generic Representation of Skeletal Remains Replication of
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CIPA15(379-386).
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Lindblad, J.[Joakim],
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0906
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Earlier:
Registration of 2D Histological Images of Bone Implants with 3D SRmuCT
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Sarve, H.[Hamid],
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1009
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Sarve, H.[Hamid],
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Supervised Learning for Guiding Hierarchy Construction:
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Earlier:
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Winkelbach, S.,
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See also Fast Active Appearance Model Search Using Canonical Correlation Analysis.
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications, Trabecular Bone, Spongy Bone .