21.7.4 Medical Applications -- Skeleton, Bone

Willink, R.,
A sequential algorithm for recognition of a developing pattern with application in orthotic engineering,
PR(41), No. 2, February 2008, pp. 627-636.
Elsevier DOI 0711
Classification; Discrimination; Exponential smoothing; Grip recognition; Intention recognition; k nearest neighbours; Outliers; Sequential classification; Sequential discrimination; Tetraplegia BibRef

Leloup, T., El Kazzi, W., Schuind, F., Warzee, N.,
A Novel Technique for Distal Locking of Intramedullary Nail Based on Two Non-constrained Fluoroscopic Images and Navigation,
MedImg(27), No. 9, September 2008, pp. 1202-1212.
IEEE DOI 0809
BibRef

Tan, S., Yao, J.H.[Jian-Hua], Ward, M.M., Yao, L., Summers, R.M.,
Computer Aided Evaluation of Ankylosing Spondylitis Using High-Resolution CT,
MedImg(27), No. 9, September 2008, pp. 1252-1267.
IEEE DOI 0809
Bone disease. BibRef

Kurazume, R.[Ryo], Nakamura, K.[Kaori], Okada, T.[Toshiyuki], Sato, Y.[Yoshinobu], Sugano, N.[Nobuhiko], Koyama, T.[Tsuyoshi], Iwashita, Y.[Yumi], Hasegawa, T.[Tsutomu],
3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic images,
CVIU(113), No. 2, February 2009, pp. 202-211.
Elsevier DOI 0901
Fluoroscopic image; Parametric femoral model; Registration; Medical image diagnosis BibRef

Thodberg, H.H., Kreiborg, S., Juul, A., Pedersen, K.D.,
The BoneXpert Method for Automated Determination of Skeletal Maturity,
MedImg(28), No. 1, January 2009, pp. 52-66.
IEEE DOI 0901
BibRef

Varshney, K.R., Paragios, N., Deux, J.F., Kulski, A., Raymond, R., Hernigou, P., Rahmouni, A.,
Postarthroplasty Examination Using X-Ray Images,
MedImg(28), No. 3, March 2009, pp. 469-474.
IEEE DOI 0903
BibRef

Zheng, G.Y.[Guo-Yan], Zhang, X.[Xuan],
A Novel Parameter Decomposition Based Optimization Approach for Automatic Pose Estimation of Distal Locking Holes From Single Calibrated Fluoroscopic Image,
PRL(30), No. 9, 1 July 2009, pp. 838-847.
Elsevier DOI 0905
BibRef
Earlier:
A Novel Parameter Decomposition Approach for Recovering Poses of Distal Locking Holes from Single Calibrated Fluoroscopic Image,
SCIA07(364-373).
Springer DOI 0706
Distal locking; Fluoroscopy; Pose estimation; Parameter decomposition; Hybrid optimization; Model-based method BibRef

Lee, S.L., Tan, E., Khullar, V., Gedroyc, W., Darzi, A., Yang, G.Z.,
Physical-Based Statistical Shape Modeling of the Levator Ani,
MedImg(28), No. 6, June 2009, pp. 926-936.
IEEE DOI 0906
Pelvic muscles can be damaged in childbirth. BibRef

Zhang, J., Yan, C.H., Chui, C.K., Ong, S.H.,
Accurate Measurement of Bone Mineral Density Using Clinical CT Imaging With Single Energy Beam Spectral Intensity Correction,
MedImg(29), No. 7, July 2010, pp. 1382-1389.
IEEE DOI 1007
BibRef

Macrini, D.[Diego], Dickinson, S.J.[Sven J.], Fleet, D.J.[David J.], Siddiqi, K.[Kaleem],
Bone graphs: Medial shape parsing and abstraction,
CVIU(115), No. 7, July 2011, pp. 1044-1061.
Elsevier DOI 1106
BibRef
Earlier: A1, A4, A2, Only:
From skeletons to bone graphs: Medial abstraction for object recognition,
CVPR08(1-8).
IEEE DOI 0806
Medial shape representation; Shape parsing; Graph-based object representation BibRef

Lilledahl, M.B., Pierce, D.M., Ricken, T., Holzapfel, G.A., Davies, C.D.L.,
Structural Analysis of Articular Cartilage Using Multiphoton Microscopy: Input for Biomechanical Modeling,
MedImg(30), No. 9, September 2011, pp. 1635-1648.
IEEE DOI 1109
BibRef

Lucas, B.C., Otake, Y., Armand, M., Taylor, R.H.,
An Active Contour Method for Bone Cement Reconstruction From C-Arm X-Ray Images,
MedImg(31), No. 4, April 2012, pp. 860-869.
IEEE DOI 1204
BibRef

Blanc, R., Szekely, G.,
Confidence Regions for Statistical Model Based Shape Prediction From Sparse Observations,
MedImg(31), No. 6, June 2012, pp. 1300-1310.
IEEE DOI 1206
Minimally invasive surgery. Bone shapes BibRef

Hao, S.Y.[Shi-Ying],
Analysis of the Self-Calibration Process in a Displacement Sensor in Applications of Hip or Knee Implants,
Sensors(141), No. 6, June 2012, pp. 106-118.
HTML Version. 1207
BibRef

Wu, J.[Jie], Belle, A.[Ashwin], Hargraves, R.H.[Rosalyn H.], Cockrell, C.[Charles], Tang, Y.[Yang], Najarian, K.[Kayvan],
Bone segmentation and 3D visualization of CT images for traumatic pelvic injuries,
IJIST(24), No. 1, 2014, pp. 29-38.
DOI Link 1403
traumatic pelvic injury BibRef

Chen, X.[Xin], Graham, J., Hutchinson, C., Muir, L.,
Automatic Generation of Statistical Pose and Shape Models for Articulated Joints,
MedImg(33), No. 2, February 2014, pp. 372-383.
IEEE DOI 1403
biomedical MRI BibRef

Paulano, F.[Félix], Jiménez, J.J.[Juan J.], Pulido, R.[Rubén],
3D segmentation and labeling of fractured bone from CT images,
VC(30), No. 6-8, June 2014, pp. 939-948.
WWW Link. 1407
BibRef

Lin, H.H.[Hsiu-Hsia], Shu, S.G.[San-Ging], Lin, Y.H.[Yueh-Huang], Yu, S.S.[Shyr-Shen],
Bone age cluster assessment and feature clustering analysis based on phalangeal image rough segmentation,
PR(45), No. 1, 2012, pp. 322-332.
Elsevier DOI 1410
Bone age assessment BibRef

Diotte, B., Fallavollita, P., Wang, L., Weidert, S., Euler, E., Thaller, P., Navab, N.,
Multi-Modal Intra-Operative Navigation During Distal Locking of Intramedullary Nails,
MedImg(34), No. 2, February 2015, pp. 487-495.
IEEE DOI 1502
Bones BibRef

Thomas, G.W., Johns, B.D., Kho, J.Y., Anderson, D.D.,
The Validity and Reliability of a Hybrid Reality Simulator for Wire Navigation in Orthopedic Surgery,
HMS(45), No. 1, February 2015, pp. 119-125.
IEEE DOI 1502
biomedical education BibRef

Zielinski, B.[Bartosz], Skomorowski, M.[Marek], Wojciechowski, W.[Wadim], Korkosz, M.[Mariusz], Sprezak, K.[Kamila],
Computer aided erosions and osteophytes detection based on hand radiographs,
PR(48), No. 7, 2015, pp. 2304-2317.
Elsevier DOI 1504
Medical imaging BibRef

Aarya, I.[Isshaa], Jiang, D.[Danchi],
Automated and optimal detection of 3D articular cartilage using undecimated wavelets in MRI,
SIViP(9), No. 1 Supp, December 2015, pp. 305-314.
WWW Link. 1601
BibRef

Bandyopadhyay, O.[Oishila], Chanda, B.[Bhabatosh], Bhattacharya, B.B.[Bhargab B.],
Automatic Segmentation of Bones in X-ray Images Based on Entropy Measure,
IJIG(16), No. 01, 2016, pp. 1650001.
DOI Link 1603
BibRef

Belagiannis, V.[Vasileios], Wang, X.C.[Xin-Chao], Shitrit, H.B.B.[Horesh Beny Ben], Hashimoto, K.[Kiyoshi], Stauder, R.[Ralf], Aoki, Y.[Yoshimitsu], Kranzfelder, M.[Michael], Schneider, A.[Armin], Fua, P.[Pascal], Ilic, S.[Slobodan], Feussner, H.[Hubertus], Navab, N.[Nassir],
Parsing human skeletons in an operating room,
MVA(27), No. 7, October 2016, pp. 1035-1046.
Springer DOI 1610
BibRef

Xiao, C., Wang, S., Zheng, L., Zhang, X., Chaovalitwongse, W.A.,
A Patient-Specific Model for Predicting Tibia Soft Tissue Insertions From Bony Outlines Using a Spatial Structure Supervised Learning Framework,
HMS(46), No. 5, October 2016, pp. 638-646.
IEEE DOI 1610
biological tissues BibRef

Onchis, D.M.[Darian Moaca], Zappalá, S.[Simone], Gotia, S.L.[Smaranda Laura], Real, P.[Pedro], Pricop, M.[Marius],
Detection of the mandibular canal in orthopantomography using a Gabor-filtered anisotropic generalized Hough transform,
PRL(83, Part 1), No. 1, 2016, pp. 85-90.
Elsevier DOI 1609
Generalized Hough transform BibRef

Bandyopadhyay, O.[Oishila], Biswas, A.[Arindam], Bhattacharya, B.B.[Bhargab B.],
Automated Analysis of Orthopaedic X-ray Images based on Digital-Geometric Techniques,
ELCVIA(15), No. 2, 2016, pp. 7-9.
DOI Link 1611
BibRef

Shirvaikar, M.[Mukul], Lagadapati, Y.[Yamuna], Dong, X.L.N.[Xuan-Liang N.],
Semivariogram analysis of bone images implemented on FPGA architectures,
RealTimeIP(13), No. 1, March 2017, pp. 161-180.
Springer DOI 1704
BibRef

Lin, H.H., Peng, S.L., Wu, J., Shih, T.Y., Chuang, K.S., Shih, C.T.,
A Novel Two-Compartment Model for Calculating Bone Volume Fractions and Bone Mineral Densities From Computed Tomography Images,
MedImg(36), No. 5, May 2017, pp. 1094-1105.
IEEE DOI 1705
Attenuation, Bone tissue, Bones, Computed tomography, Osteoporosis, Phantoms, 3D-printed trabecular structural phantom, bone equivalent uniform phantom, bone mineral density, bone volume fraction, computed tomography, lumbar vertebra, partial, volume, effect BibRef

Behrooz, A., Kask, P., Meganck, J., Kempner, J.,
Automated Quantitative Bone Analysis in In Vivo X-ray Micro-Computed Tomography,
MedImg(36), No. 9, September 2017, pp. 1955-1965.
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, BibRef

Maté-González, M.Á.[Miguel Ángel], Aramendi, J.[Julia], González-Aguilera, D.[Diego], Yravedra, J.[José],
Statistical Comparison between Low-Cost Methods for 3D Characterization of Cut-Marks on Bones,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Tu, L., Styner, M., Vicory, J., Elhabian, S., Wang, R., Hong, J., Paniagua, B., Prieto, J.C., Yang, D., Whitaker, R., Pizer, S.M.,
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 BibRef

Sabeti, M.[Malihe], Boostani, R.[Reza], Davoodi, B.[Bita],
Improved particle swarm optimisation to estimate bone age,
IET-IPR(12), No. 2, February 2018, pp. 179-187.
DOI Link 1801
BibRef

Huang, C., Nguyen, M.,
X-Ray Enhancement Based on Component Attenuation, Contrast Adjustment, and Image Fusion,
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, parametric contrast adjustment model BibRef

Ai, Z.[Zexiu],
Quantitative CT study of martial arts sports injuries based on image quality,
JVCIR(60), 2019, pp. 417-425.
Elsevier DOI 1903
Image quality, Wushu, Joint damage, Quantitative CT BibRef

Morooka, K.[Ken'ichi], Matsubara, R.[Ryota], Miyauchi, S.[Shoko], Fukuda, T.[Takaichi], Sugii, T.[Takeshi], Kurazume, R.[Ryo],
Ancient pelvis reconstruction from collapsed component bones using statistical shape models,
MVA(30), No. 1, February 2019, pp. 59-69.
Springer DOI 1904
BibRef

Linares, O.C.[Oscar Cuadros], Bianchi, J.[Jonas], Raveli, D.[Dirceu], Neto, J.B.[João Batista], Hamann, B.[Bernd],
Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering,
VC(35), No. 10, October 2018, pp. 1461-1474.
Springer DOI 1909
BibRef

Cao, S.M.[Shao-Meng], Chen, Z.[Zhiye], Li, C.S.[Cong-Sheng], Lv, C.F.[Chuan-Feng], Wu, T.N.[Tong-Ning], Lv, B.[Bin],
Landmark-based multi-region ensemble convolutional neural networks for bone age assessment,
IJIST(29), No. 4, 2019, pp. 457-464.
DOI Link 1911
bone age assessment, convolutional neural network, deep learning, landmark localization, segmentation BibRef

Buonamici, F.[Francesco], Furferi, R.[Rocco], Governi, L.[Lapo], Lazzeri, S.[Simone], McGreevy, K.S.[Kathleen S.], Servi, M.[Michaela], Talanti, E.[Emiliano], Uccheddu, F.[Francesca], Volpe, Y.[Yary],
A practical methodology for computer-aided design of custom 3D printable casts for wrist fractures,
VC(36), No. 2, February 2020, pp. 375-390.
Springer DOI 2002
BibRef

Tiulpin, A.[Aleksei], Finnilä, M.[Mikko], Lehenkari, P.[Petri], Nieminen, H.J.[Heikki J.], Saarakkala, S.[Simo],
Deep-learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography,
ACIVS20(131-138).
Springer DOI 2003
BibRef

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,
MedImg(39), No. 5, May 2020, pp. 1359-1368.
IEEE DOI 2005
Bones, Aging, Shape, Calibration, Biomedical measurement, Signal to noise ratio, Osteoporosis, Spatio-temporal atlas, DXA, osteoporosis BibRef

Zhang, Y., Li, F., Qiu, L., Xu, L., Niu, X., Sui, Y., Zhang, S., Zhang, Q., Zhang, L.,
Toward Precise Osteotomies: A Coarse-to-Fine 3D Cut Plane Planning Method for Image-Guided Pelvis Tumor Resection Surgery,
MedImg(39), No. 5, May 2020, pp. 1511-1523.
IEEE DOI 2005
Tumors, Planning, Bones, Surgery, Pelvis, Cut plane planning, 3D cut plane refinement BibRef

Kayal, E.B.[Esha Baidya], Kandasamy, D.[Devasenathipathy], Sharma, R.[Raju], Bakhshi, S.[Sameer], Mehndiratta, A.[Amit],
Segmentation of osteosarcoma tumor using diffusion weighted MRI: A comparative study using nine segmentation algorithms,
SIViP(14), No. 4, June 2020, pp. 727-735.
Springer DOI 2005
BibRef

Eslami, M., Tabarestani, S., Albarqouni, S., Adeli, E., Navab, N., Adjouadi, M.,
Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography,
MedImg(39), No. 7, July 2020, pp. 2553-2565.
IEEE DOI 2007
Bone suppression, chest X-Ray, CXR imaging, image-to-image translation, image-to-images translation, pix2pix BibRef

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 Structural Priors,
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 Learning for Diagnosing Developmental Dysplasia of the Hip,
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 Multiple Structures on Temporal Bone CT Scans,
IP(30), 2021, pp. 739-753.
IEEE DOI 2012
Image segmentation, Bones, Computed tomography, Shape, Biomedical imaging, Training, PWD-3DNet BibRef

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], Hosseini, F.[Farahnaz],
Measuring panoramic radiomorphometric indices for mandible bone using active shape model and Bayesian information criterion-support vector machine,
IJIST(31), No. 3, 2021, pp. 1711-1730.
DOI Link 2108
cortex thickness, mandible, panoramic system, ramus BibRef

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.
DOI Link 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 BibRef

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 BibRef

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 BibRef

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 BibRef

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 BibRef

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 BibRef

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,
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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],
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ICPR22(4285-4291)
IEEE DOI 2212
Learning systems, Deep learning, Hospitals, Estimation, Network architecture, Aging, Bones, Bone age estamation, Attention mechanism BibRef

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 BibRef

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
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Bütüner, S., Sehirli, E.,
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Branch Profiles for Shape Analysis,
DICTA20(1-7)
IEEE DOI 2201
Shape, Digital images, Rats, Biology, Cancellous bone, Strain BibRef

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Comparisons of Hybrid Mechanisms Based on Their Singularities for Bone Reduction Surgery: 3-PRP-3-RPS and 3-RPS-3-PRP,
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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],
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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],
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Springer DOI 2011
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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).
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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],
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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 BibRef

Hosseinian, S., Arefi, H.,
Photogrammetry in 3D Modelling of Human Bone Structures From Radiographs,
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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],
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Kaipala, J.[Jukka], López, M.B.[Miguel Bordallo], Saarakkala, S.[Simo], Thevenot, J.[Jérôme],
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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 BibRef

Masson-Sibut, A., Nakib, A.,
Real-time assessment of bone structure positions via ultrasound imaging,
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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).
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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],
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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)
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Bones BibRef

Nguyen, C.T., Havlicek, J.P., Chakrabarty, J.H., Duong, Q., Vesely, S.K.,
Towards automatic 3D bone marrow segmentation,
Southwest16(9-12)
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Biomedical measurement BibRef

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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 BibRef

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 BibRef

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],
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bone BibRef

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ICIP14(501-505)
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications, Trabecular Bone, Spongy Bone .

Last update:Sep 28, 2024 at 17:47:54