21.7.4 Medical Applications -- Skeleton, Bone

Chapter Contents (Back)
Bones. Bone Analysis. Skeletal.
See also Medical Applications, Arthritis.
See also Medical Applications, Knee Joints.
See also Medical Applications, Femur Analysis, Bone.
See also Medical Applications, Vertebra, Spine, Skeletons, Bone.
See also Medical Applications, Trabecular Bone, Spongy Bone.
See also Rehabilitation Systems, Prosthesis Systems, Control.

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Egmont-Petersen, M., Pelikan, E.,
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Elsevier DOI 0108
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Egmont-Petersen, M., Frangi, A.F., Niessen, W.J., Hogendoorn, P.C.W., Bloem, J.L., Viergever, M.A., Reiber, J.H.C.,
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IEEE DOI 0009
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Jennane, R., Ohley, W.J., Majumdar, S., Lemineur, G.,
Fractal analysis of bone X-ray tomographic microscopy projections,
MedImg(20), No. 5, May 2001, pp. 443-449.
IEEE Top Reference. 0110
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Pietka, E., Gertych, A., Pospiech, S., Cao, F.[Fei], Huang, H.K., Gilsanz, V.,
Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction,
MedImg(20), No. 8, August 2001, pp. 715-729.
IEEE Top Reference. 0110
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Hirano, S., Hata, Y.,
Fuzzy expert system for foot CT image segmentation,
IVC(19), No. 4, March 2001, pp. 207-216.
Elsevier DOI 0102
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Valstar, E.R.[Edward R.], Nelissen, R.G.H.H.[Rob G.H.H.], Reiber, J.H.C.[Johan H.C.], Rozing, P.M.[Piet M.],
The use of Roentgen stereophotogrammetry to study micromotion of orthopaedic implants,
PandRS(56), No. 5-6, August 2002, pp. 376-389.
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Taleb-Ahmed, A., Dubois, P., Duquenoy, E.,
Analysis methods of CT-scan images for the characterization of the bone texture: First results,
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Elsevier DOI 0304
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Culjat, M., Singh, R.S., Yoon, D.C., Brown, E.R.,
Imaging of human tooth enamel using ultrasound,
MedImg(22), No. 4, April 2003, pp. 526-529.
IEEE Abstract. 0306
BibRef

Kang, Y.[Yan], Engelke, K., Kalender, W.A.,
A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data,
MedImg(22), No. 5, May 2003, pp. 586-598.
IEEE Abstract. 0307
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Sarry, L., Tilmant, C., Boisgard, S., Boire, J.Y., Levai, J.P.,
Monitoring of polyethylene wear in nonmetal-backed acetubular cups by digitized anteroposterior pelvic radiography,
MedImg(22), No. 9, September 2003, pp. 1172-1182.
IEEE Abstract. 0309
BibRef

Yaniv, Z., Joskowicz, L.,
Long Bone Panoramas From Fluoroscopic X-Ray Images,
MedImg(23), No. 1, January 2004, pp. 26-35.
IEEE Abstract. 0403
BibRef

Yaniv, Z., Joskowicz, L.,
Precise Robot-Assisted Guide Positioning for Distal Locking of Intramedullary Nails,
MedImg(24), No. 5, May 2005, pp. 624-635.
IEEE Abstract. 0505
BibRef

Saha, P.K., Udupa, J.K., Falcao, A.X., Hirsch, B.E., Siegler, S.,
Iso-Shaping Rigid Bodies for Estimating Their Motion From Image Sequences,
MedImg(23), No. 1, January 2004, pp. 63-72.
IEEE Abstract. 0403
Mosaic. Registering multiple views to get the whole. BibRef

Xiao, G., Ong, S.H., Foong, K.W.C.,
Efficient partial-surface registration for 3D objects,
CVIU(98), No. 2, May 2005, pp. 271-293.
Elsevier DOI 0501
Use model to get registration. BibRef

Yin, T.K.[Tang-Kai], Chiu, N.T.[Nan-Tsing],
A computer-aided diagnosis for locating abnormalities in bone scintigraphy by a fuzzy system with a three-step minimization approach,
MedImg(23), No. 5, May 2004, pp. 639-654.
IEEE Abstract. 0406
BibRef

Burckhardt, K., Szekely, G., Notzli, H., Hodler, J., Gerber, C.,
Submillimeter Measurement of Cup Migration in Clinical Standard Radiographs,
MedImg(24), No. 5, May 2005, pp. 676-688.
IEEE Abstract. 0505
Assess the displacement of implants. Match template from CAD model to the image. BibRef

Yao, W., Abolmaesumi, P., Greenspan, M., Ellis, R.E.,
An Estimation/Correction Algorithm for Detecting Bone Edges in CT Images,
MedImg(24), No. 8, August 2005, pp. 997-1010.
IEEE DOI 0508
BibRef

Wang, L.I., Greenspan, M., Ellis, R.E.,
Validation of bone segmentation and improved 3-D registration using contour coherency in CT data,
MedImg(25), No. 3, March 2006, pp. 324-334.
IEEE DOI 0604
BibRef

Barratt, D.C., Penney, G.P., Chan, C.S.K., Slomczykowski, M., Carter, T.J., Edwards, P.J., Hawkes, D.J.,
Self-calibrating 3D-ultrasound-based bone registration for minimally invasive orthopedic surgery,
MedImg(25), No. 3, March 2006, pp. 312-323.
IEEE DOI 0604
BibRef

Ogiela, M.R.[Marek R.], Tadeusiewicz, R.[Ryszard], Ogiela, L.[Lidia],
Graph image language techniques supporting radiological, hand image interpretations,
CVIU(103), No. 2, August 2006, pp. 112-120.
Elsevier DOI 0608
Medical image understanding; Intelligent information systems; Syntactic pattern recognition; Hand disease diagnostics BibRef

Ogiela, L.[Lidia], Ogiela, M.R.[Marek R.], Tadeusiewicz, R.[Ryszard],
Mathematical Linguistics in Cognitive Medical Image Interpretation Systems,
JMIV(34), No. 3, July 2009, pp. xx-yy.
Springer DOI 0906
BibRef
Earlier: A1, A3, A2:
Cognitive Approach to Visual Data Interpretation in Medical Information and Recognition Systems,
IWICPAS06(244-250).
Springer DOI 0608
BibRef

Leung, K.K., Holden, M., Saeed, N., Brooks, K.J., Buckton, J.B., Williams, A.A., Campbell, S.P., Changani, K., Reid, D.G., Zhao, Y., Wilde, M., Rueckert, D., Hajnal, J.V., Hill, D.L.G.,
Automatic Quantification of Changes in Bone in Serial MR Images of Joints,
MedImg(25), No. 12, December 2006, pp. 1617-1626.
IEEE DOI 0701
BibRef

Han, C.C.[Chin-Chuan], Lee, C.H.[Chang-Hsing], Peng, W.L.[Wen-Li],
Hand radiograph image segmentation using a coarse-to-fine strategy,
PR(40), No. 11, November 2007, pp. 2994-3004.
Elsevier DOI 0707
Bone age assessment; Watershed transform; Active contour model; Metaphyseal/epiphyseal region; Gradient vector flow BibRef

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.Y.[Zhi-Ye], 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
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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
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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


Wong, T.M.[Tak Ming], Moosmann, J.[Julian], Zeller-Plumhoff, B.[Berit],
VolRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces,
CVPRWS24(53-62)
IEEE DOI 2410
Training, Materials science and technology, Correlation, Deformation, Tomography, deep learning, optical flow, bone-implant materials 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,
CIAP23(II:134-143).
Springer DOI 2312
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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
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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 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
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
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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 BibRef

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
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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],
A Survey of Deep Learning Based Fully Automatic Bone Age Assessment Algorithms,
DLPR20(688-702).
Springer DOI 2103
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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
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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
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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],
Lytic Region Recognition in Hip Radiograms by Means of Statistical Dominance Transform,
ICCVG18(349-360).
Springer DOI 1810
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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,
PTVSBB17(115-121).
DOI Link 1805
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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],
Automated Measurement of Translational Margins and Rotational Shifts in Pelvic Structures Using CBCT Images of Rectal Cancer Patients,
PReMI17(103-109).
Springer DOI 1711
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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 BibRef

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
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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],
Towards a Supervised Incremental Learning System for Automatic Recognition of the Skeletal Age,
MCPR16(346-355).
Springer DOI 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 BibRef

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 BibRef

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 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],
A Comparative Study of Computer Aided System Preoperative Planning for High Tibial Osteotomy,
IVIC15(189-198).
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Shao, C.W., Chiu, H.L., Chang, S.K.,
A Study on Generic Representation of Skeletal Remains Replication of Prehistoric Burial,
CIPA15(379-386).
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Chu, C.W.[Cheng-Wen], Bai, J.J.[Jun-Jie], Liu, L.[Li], Wu, X.D.[Xiao-Dong], Zheng, G.Y.[Guo-Yan],
Fully Automatic Segmentation of Hip CT Images via Random Forest Regression-Based Atlas Selection and Optimal Graph Search-Based Surface Detection,
ACCV14(III: 640-654).
Springer DOI 1504
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Harrar, K., Hamami, L., Akkoul, S., Lespessailles, E., Jennane, R.,
Osteoporosis assessment using Multilayer Perceptron neural networks,
IPTA12(217-221)
IEEE DOI 1503
bone BibRef

Zaghlool, S.B.,
Methods for automated segmentation of trabecular bone structure,
IPTA14(1-4)
IEEE DOI 1503
bone BibRef

Gaudeau, Y., Lambert, J., Labonne, N., Moureaux, J.M.,
Compressed image quality assessment: Application to an interactive upper limb radiology atlas,
ICIP14(501-505)
IEEE DOI 1502
Biomedical imaging BibRef

Esfandiari, H., Amiri, S., Lichti, D.D., Anglin, C.,
A Fast, Accurate and Easy to Implement Method for Pose Recognition of an Intramedullary Nail using a Tracked C-arm,
CloseRange14(217-223).
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Pixel Based Meshfree Modeling of Skeletal Muscles,
CompIMAGE14(316-327).
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Mihail, R.P.[Radu Paul], Blomquist, G.[Gustav], Jacobs, N.[Nathan],
A CRF approach to fitting a generalized hand skeleton model,
WACV14(409-416)
IEEE DOI 1406
Bones BibRef

Bandyopadhyay, O.[Oishila], Biswas, A.[Arindam], Bhattacharya, B.B.[Bhargab B.],
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Automated MR Hip Bone Segmentation,
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See also Fast Active Appearance Model Search Using Canonical Correlation Analysis. BibRef

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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:Nov 26, 2024 at 16:40:19