21.9.6 Brain, Cortex, General Segmentation Issues

Chapter Contents (Back)
Brain. Segmentation.
See also Brain, Cortex, MRI Segmentation.

Shen, D., Herskovits, E.H., Davatzikos, C.,
An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures,
MedImg(20), No. 4, April 2001, pp. 257-270.
IEEE Top Reference. 0110

See also Adaptive-Focus Deformable Model Using Statistical and Geometric Information, An. BibRef

Zhang, T., Davatzikos, C.,
ODVBA: Optimally-Discriminative Voxel-Based Analysis,
MedImg(30), No. 8, August 2011, pp. 1441-1454.
IEEE DOI 1108
BibRef

Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.,
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements,
MedImg(26), No. 1, January 2007, pp. 93-105.
IEEE DOI 0701
BibRef

Hojjatoleslami, S.A., Kruggel, F.,
Segmentation of large brain lesions,
MedImg(20), No. 7, July 2001, pp. 666-669.
IEEE Top Reference. 0110
BibRef

Descombes, X., Kruggel, F., Wollny, G., Gertz, H.J.,
An Object-Based Approach for Detecting Small Brain Lesions: Application to Virchow-Robin Spaces,
MedImg(23), No. 2, February 2004, pp. 246-255.
IEEE Abstract. 0403
BibRef

Nain, D.[Delphine], Haker, S.[Steven], Bobick, A.F.[Aaron F.], Tannenbaum, A.[Allen],
Multiscale 3-D Shape Representation and Segmentation Using Spherical Wavelets,
MedImg(26), No. 4, April 2007, pp. 598-618.
IEEE DOI 0704

See also On the Laplace-Beltrami operator and brain surface flattening. Brain structures. encode shape variations in population. Diagnosis. BibRef

Liu, Y.X.[Yan-Xi], Collins, R.T., Rothfus, W.E.,
Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images,
MedImg(20), No. 3, March 2001, pp. 175-192.
IEEE Top Reference. 0110
BibRef

Prima, S.[Sylvain], Ourselin, S.[Sibastien], Ayache, N.J.[Nicholas J.],
Computation of the mid-sagittal plane in 3-D brain images,
MedImg(21), No. 2, February 2002, pp. 122-138.
IEEE Top Reference. 0204
BibRef
Earlier:
Computation of the Mid-Sagittal Plane in 3D Images of the Brain,
ECCV00(II: 685-701).
Springer DOI 0003
BibRef

Roche, A., Guimond, A., Ayache, N.J., Meunier, J.,
Multimodal Elastic Matching of Brain Images,
ECCV00(II: 511-527).
Springer DOI 0003
BibRef

Han, X.[Xiao], Xu, C.Y.[Chen-Yang], Braga-Neto, U.M., Prince, J.L.,
Topology Correction in Brain Cortex Segmentation Using a Multiscale, Graph-Based Algorithm,
MedImg(21), No. 2, February 2002, pp. 109-121.
IEEE Top Reference. 0204

See also Topology Preserving Brain Tissue Segmentation Using Graph Cuts. BibRef

Fan, Y.[Yong], Jiang, T.Z.[Tian-Zi], Evans, D.J.,
Volumetric segmentation of brain images using parallel genetic algorithms,
MedImg(21), No. 8, August 2002, pp. 904-909.
IEEE Top Reference. 0301
BibRef

Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.,
Cortex Segmentation: A Fast Variational Geometric Approach,
MedImg(21), No. 12, December 2002, pp. 1544-1551.
IEEE Top Reference. 0301
BibRef
Earlier: LevelSet01(xx-yy). 0106
BibRef

Tu, Z.W.[Zhuo-Wen], Narr, K.L., Dollar, P., Dinov, I.D., Thompson, P.M., Toga, A.W.[Arthur W.],
Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models,
MedImg(27), No. 4, April 2008, pp. 495-508.
IEEE DOI 0804
BibRef

Hou, Z., Qian, W., Huang, S., Hu, Q., Nowinski, W.L.,
Regularized fuzzy c-means method for brain tissue clustering,
PRL(28), No. 13, 1 October 2007, pp. 1788-1794.
Elsevier DOI 0709
Fuzzy c-means; Clustering; Regularization; Image segmentation; Spatial modeling; Noise level estimate BibRef

Schmitt, O.[Oliver], Bethke, S.[Sven], Sobe, P.[Peter], Prehn, S.[Steffen], Maehle, E.[Erik],
Parallelized segmentation of a serially sectioned whole human brain,
IVC(26), No. 2, 1 February 2008, pp. 289-301.
Elsevier DOI 0711
Segmentation; Adaptive fuzzy c-means; Human brain; Paraffin sections; Histologic sections; Block-face; Episcopic images BibRef

Pakura, M., Schmitt, O., Aach, T.,
Segmentation and analysis of nerve fibers in histologic sections of the cerebral human cortex,
Southwest02(62-66).
IEEE Top Reference. 0208
BibRef

Engel, K.[Karin], Tönnies, K.D.[Klaus D.], Brechmann, A.[André],
Part-based localisation and segmentation of landmark-related auditory cortical regions,
PR(44), No. 9, September 2011, pp. 2017-2033.
Elsevier DOI 1106
BibRef
Earlier:
Parcellation of the Auditory Cortex into Landmark-Related Regions of Interest,
CAIP09(631-638).
Springer DOI 0909
BibRef
Earlier:
A Two-level Dynamic Model for The Representation And Recognition of Cortical Folding Patterns,
ICIP05(I: 297-300).
IEEE DOI 0512
Shape decomposition; Shape abstraction; Graphical model; Cortical parcellation; Auditory cortex BibRef

Chaovalitwongse, W.[Wanpracha], Jeong, Y.[Youngseon], Jeong, M.K.[Myong K.], Danish, S.[Shabbar], Wong, S.[Stephen],
Pattern Recognition Approaches for Identifying Subcortical Targets during Deep Brain Stimulation Surgery,
IEEE_Int_Sys(26), No. 5, September-October 2011, pp. 54-63.
IEEE DOI 1110
BibRef

Engel, K.[Karin], Toennies, K.D.[Klaus D.],
Segmentation of the Midbrain in Transcranial Sonographies using a Two-Component Deformable Mode,
BMVA(2009), No. 4, 2009, pp. 1-12.
PDF File. 1209
BibRef

Spratling, M.W.,
Image Segmentation Using a Sparse Coding Model of Cortical Area V1,
IP(22), No. 4, April 2013, pp. 1631-1643.
IEEE DOI 1303
BibRef

Nanthagopal, A.P., Sukanesh, R.,
Wavelet statistical texture features-based segmentation and classification of brain computed tomography images,
IET-IPR(7), No. 1, 2013, pp. 25-32.
DOI Link 1303
BibRef

Deligianni, F., Varoquaux, G., Thirion, B., Sharp, D.J., Ledig, C., Leech, R., Rueckert, D.,
A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks,
MedImg(32), No. 12, 2013, pp. 2200-2214.
IEEE DOI 1312
Brain models BibRef

Ng, B., Varoquaux, G., Poline, J.B., Greicius, M., Thirion, B.,
Transport on Riemannian Manifold for Connectivity-Based Brain Decoding,
MedImg(35), No. 1, January 2016, pp. 208-216.
IEEE DOI 1601
Classification algorithms BibRef

Chen, Z.S.[Zeng-Si], Wang, J.W.[Jin-Wei], Kong, D.X.[De-Xing], Dong, F.F.[Fang-Fang],
A nonlocal energy minimization approach to brain image segmentation with simultaneous bias field estimation and denoising,
MVA(25), No. 2, February 2014, pp. 529-544.
WWW Link. 1402
BibRef

Pu, J., Wang, J., Yu, W., Shen, Z., Lv, Q., Zeljic, K., Zhang, C., Sun, B., Liu, G., Wang, Z.,
Discriminative Structured Feature Engineering for Macroscale Brain Connectomes,
MedImg(34), No. 11, November 2015, pp. 2333-2342.
IEEE DOI 1511
Brain modeling BibRef

Zhao, S.J.[Shi-Jie], Han, J.W.[Jun-Wei], Lv, J.L.[Jing-Lei], Jiang, X.[Xi], Hu, X.T.[Xin-Tao], Zhao, Y.[Yu], Ge, B.[Bao], Guo, L.[Lei], Liu, T.M.[Tian-Ming],
Supervised Dictionary Learning for Inferring Concurrent Brain Networks,
MedImg(34), No. 10, October 2015, pp. 2036-2045.
IEEE DOI 1511
biomedical MRI BibRef

Hong, S.G.[Shang-Guan], Liu, Y.[Yi], Cui, X.Y.[Xue-Ying], Bai, Y.J.[Yun-Jiao], Zhang, Q.[Quan], Gui, Z.G.[Zhi-Guo],
Sparse-view statistical iterative head CT image reconstruction via joint regularization,
IJIST(26), No. 1, 2016, pp. 3-14.
DOI Link 1604
CT BibRef

Padma, A., Giridharan, N.,
Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images,
IJIST(26), No. 2, 2016, pp. 97-105.
DOI Link 1606
feature selection BibRef

Wade, B.S.C.[Benjamin S.C.], Joshi, S.H.[Shantanu H.], Gutman, B.A.[Boris A.], Thompson, P.M.[Paul M.],
Machine Learning on High Dimensional Shape Data from Subcortical Brain Surfaces: A Comparison of Feature Selection and Classification Methods,
PR(63), No. 1, 2017, pp. 731-739.
Elsevier DOI 1612
BibRef
Earlier: MLMI15(36-43).
Springer DOI 1511
Feature selection BibRef

Rabiei, H.[Hamed], Richard, F.J.P.[Frédéric J.P.], Coulon, O.[Olivier], Lefčvre, J.[Julien],
Local Spectral Analysis of the Cerebral Cortex: New Gyrification Indices,
MedImg(36), No. 3, March 2017, pp. 838-848.
IEEE DOI 1703
Complexity theory BibRef

Rabiei, H.[Hamed], Coulon, O.[Olivier], Lefčvre, J.[Julien], Richard, F.J.P.[Frédéric J. P.],
Surface Regularity via the Estimation of Fractional Brownian Motion Index,
IP(30), 2021, pp. 1453-1460.
IEEE DOI 2101
Indexes, Estimation, Eigenvalues and eigenfunctions, Surface treatment, Mathematical model, Linear regression, fetal cortical surface BibRef

Saleh, M.G.[Muhammad G.], Near, J.[Jamie], Alhamud, A.[Alqadafi], van der Kouwe, A.J.W.[André J. W.], Meintjes, E.M.[Ernesta M.],
Effects of tissue and gender on macromolecule suppressed gamma-aminobutyric acid,
IJIST(27), No. 2, 2017, pp. 144-152.
DOI Link 1706
GABA, gender, grey matter, tissue, white, matter BibRef

Mahbod, A.[Amirreza], Chowdhury, M.[Manish], Smedby, Ö.[Örjan], Wang, C.L.[Chun-Liang],
Automatic brain segmentation using artificial neural networks with shape context,
PRL(101), No. 1, 2018, pp. 74-79.
Elsevier DOI 1801
Brain segmentation BibRef

Qin, C.[Chen], Guerrero Moreno, R.[Ricardo], Bowles, C.[Christopher], Chen, L.[Liang], Dickie, D.A.[David Alexander], Valdes-Hernandez, M.D.C.[Maria Del C.], Wardlaw, J.[Joanna], Rueckert, D.[Daniel],
A large margin algorithm for automated segmentation of white matter hyperintensity,
PR(77), 2018, pp. 150-159.
Elsevier DOI 1802
Supervised learning, Semi-supervised learning, Segmentation, White matter hyperintensity, Brain MRI BibRef

Qin, C.[Chen], Guerrero Moreno, R.[Ricardo], Bowles, C.[Christopher], Ledig, C.[Christian], Scheltens, P.[Philip], Barkhof, F.[Frederik], Rhodius-Meester, H.[Hanneke], Tijms, B.[Betty], Lemstra, A.W.[Afina W.], van der Flier, W.M.[Wiesje M.], Glocker, B.[Ben], Rueckert, D.[Daniel],
A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation,
MLMI16(104-112).
Springer DOI 1611
BibRef

Kalaiselvi, T., Sriramakrishnan, P.,
Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine,
IJIST(28), No. 3, September 2018, pp. 163-174.
WWW Link. 1808
BibRef

Wang, L.[Li], Nie, D.[Dong], Li, G.N.[Guan-Nan], Puybareau, É.[Élodie], Dolz, J.[Jose], Zhang, Q.[Qian], Wang, F.[Fan], Xia, J.[Jing], Wu, Z.W.[Zheng-Wang], Chen, J.W.[Jia-Wei], Thung, K.H.[Kim-Han], Bui, T.D.[Toan Duc], Shin, J.T.[Ji-Tae], Zeng, G.D.[Guo-Dong], Zheng, G.Y.[Guo-Yan], Fonov, V.S.[Vladimir S.], Doyle, A.[Andrew], Xu, Y.C.[Yong-Chao], Moeskops, P.[Pim], Pluim, J.P.W.[Josien P. W.], Desrosiers, C.[Christian], Ben Ayed, I.[Ismail], Sanroma, G.[Gerard], Benkarim, O.M.[Oualid M.], Casamitjana, A.[Adriŕ], Vilaplana, V.[Verónica], Lin, W.L.[Wei-Li], Li, G.[Gang], Shen, D.G.[Ding-Gang],
Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge,
MedImg(38), No. 9, September 2019, pp. 2219-2230.
IEEE DOI 1909
Image segmentation, Magnetic resonance imaging, Manuals, Pediatrics, Biomedical imaging, Testing, White matter, Infant, brain, challenge BibRef

Sun, Y.[Yue], Gao, K.[Kun], Wu, Z.W.[Zheng-Wang], Li, G.[Guannan], Zong, X.P.[Xiao-Peng], Lei, Z.H.[Zhi-Hao], Wei, Y.[Ying], Ma, J.[Jun], Yang, X.P.[Xiao-Ping], Feng, X.[Xue], Zhao, L.[Li], Phan, T.L.[Trung Le], Shin, J.[Jitae], Zhong, T.[Tao], Zhang, Y.[Yu], Yu, L.[Lequan], Li, C.[Caizi], Basnet, R.[Ramesh], Ahmad, M.O.[M. Omair], Swamy, M.N.S., Ma, W.[Wenao], Dou, Q.[Qi], Bui, T.D.[Toan Duc], Noguera, C.B.[Camilo Bermudez], Landman, B.[Bennett], Gotlib, I.H.[Ian H.], Humphreys, K.L.[Kathryn L.], Shultz, S.[Sarah], Li, L.C.[Long-Chuan], Niu, S.[Sijie], Lin, W.[Weili], Jewells, V.[Valerie], Shen, D.G.[Ding-Gang], Li, G.[Gang], Wang, L.[Li],
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge,
MedImg(40), No. 5, May 2021, pp. 1363-1376.
IEEE DOI 2105
Image segmentation, Testing, Training, Manuals, Magnetic resonance imaging, Pediatrics, Brain, deep learning BibRef

Gao, S., Xiong, Z.,
Deep Enhancement for 3D HDR Brain Image Compression,
ICIP19(714-718)
IEEE DOI 1910
Artifact reduction, brain image compression, convolutional neural network BibRef

Jang, S., Kim, S., Kim, M., Son, K., Lee, K., Ra, J.B.,
Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning,
MedImg(39), No. 5, May 2020, pp. 1636-1645.
IEEE DOI 2005
Filtered backprojection (FBP), head motion correction, helical CT scanning, motion estimation, motion-compensated reconstruction BibRef

Liu, L., Hu, X., Zhu, L., Fu, C., Qin, J., Heng, P.,
?-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation,
MedImg(39), No. 9, September 2020, pp. 2806-2817.
IEEE DOI 2009
Brain, Image segmentation, Semantics, Computer architecture, Convolutional neural networks, Aggregates, Logic gates, densely convolutional LSTM BibRef

Wu, J.[Jiong], Tang, X.Y.[Xiao-Ying],
Brain segmentation based on multi-atlas and diffeomorphism guided 3D fully convolutional network ensembles,
PR(115), 2021, pp. 107904.
Elsevier DOI 2104
Brain segmentation, Fully convolutional network, Multi-atlas, Diffeomorphism, Adaptive-size patches, Ensemble model BibRef

Wei, X.D.[Xiao-Dan], Liu, Q.H.[Qing-Hao], Liu, M.[Min], Wang, Y.[Yaonan], Meijering, E.[Erik],
3D Soma Detection in Large-Scale Whole Brain Images via a Two-Stage Neural Network,
MedImg(42), No. 1, January 2023, pp. 148-157.
IEEE DOI 2301
Soma, Brain, Mice, Image segmentation, Deep learning, Feature extraction, Soma detection, two-stage neural network, neuron reconstruction BibRef


Sun, Y.H.[Yong-Heng], Wang, F.[Fan], Shu, J.[Jun], Wang, H.F.[Hai-Feng], Wang, L.[Li], Meng, D.Y.[De-Yu], Lian, C.F.[Chun-Feng],
Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan,
ICCV23(21061-21071)
IEEE DOI Code:
WWW Link. 2401
BibRef

Laudicella, R.[Riccardo], Agnello, L.[Luca], Comelli, A.[Albert],
Unsupervised Brain Segmentation System Using K-Means and Neural Network,
AIRCAD22(441-449).
Springer DOI 2208
BibRef

Zhang, Y.[Yan], Kong, Y.Y.[You-Yong], Wu, J.S.[Jia-Song], Coatrieux, G.[Gouenou], Shu, H.Z.[Hua-Zhong],
Brain Tissue Segmentation based on Graph Convolutional Networks,
ICIP19(1470-1474)
IEEE DOI 1910
Magnetic resonance imaging, Brain tissue segmentation, Supervoxels, Graph Convolutional Networks BibRef

Xie, K., Wen, Y.,
LSTM-MA: A LSTM Method with Multi-Modality and Adjacency Constraint for Brain Image Segmentation,
ICIP19(240-244)
IEEE DOI 1910
LSTM, Multi-modality, Superpixel, Brain Segmentation, Noise Robustness BibRef

Liedlgruber, M.[Michael], Butz, K.[Kevin], Höller, Y.[Yvonne], Kuchukhidze, G.[Georgi], Taylor, A.[Alexandra], Thomschevski, A.[Aljoscha], Tomasi, O.[Ottavio], Trinka, E.[Eugen], Uhl, A.[Andreas],
Can SPHARM-Based Features from Automated or Manually Segmented Hippocampi Distinguish Between MCI and TLE?,
SCIA19(465-476).
Springer DOI 1906
BibRef

Dong, M., Liu, D., Xiong, Z., Yang, C., Chen, X., Zha, Z., Bi, G., Wu, F.,
3D CNN-Based Soma Segmentation from Brain Images at Single-Neuron Resolution,
ICIP18(126-130)
IEEE DOI 1809
Image segmentation, Brain, Neurons, Training, Image resolution, Signal to noise ratio, Brain images, weakly supervised learning BibRef

Kumar, P., Nagar, P., Arora, C., Gupta, A.,
U-Segnet: Fully Convolutional Neural Network Based Automated Brain Tissue Segmentation Tool,
ICIP18(3503-3507)
IEEE DOI 1809
Image segmentation, Training, Brain, Magnetic resonance imaging, Task analysis, Computer architecture BibRef

Madiraju, N.[Naveen], Singh, A.[Amarjot], Omkar, S.N.,
Level Set Segmentation of Brain Matter Using a Trans-Roto-Scale Invariant High Dimensional Feature,
MCBMIIA16(II: 595-609).
Springer DOI 1704
BibRef

Švihlík, J.[Jan], Kybic, J.[Jan], Habart, D.[David], Hlushak, H.[Hanna], Dvorák, J.[Jirí], Radochová, B.[Barbora],
Langerhans Islet Volume Estimation from 3D Optical Projection Tomography,
MCBMIIA16(II: 583-594).
Springer DOI 1704
BibRef

Chan, A., Wood, I.A., Fripp, J.,
Maximum Pseudolikelihood Estimation for Mixture-Markov Random Field Segmentation of the Brain,
DICTA16(1-7)
IEEE DOI 1701
Australia BibRef

Tamajka, M., Benesova, W.,
Automatic brain segmentation method based on supervoxels,
WSSIP16(1-4)
IEEE DOI 1608
biomedical MRI BibRef

Ng, B.[Bernard], Milazzo, A.C.[Anna-Clare], Altmann, A.[Andre],
Node-Based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs,
MLMI15(44-51).
Springer DOI 1511
BibRef

de Brebisson, A.[Alexandre], Montana, G.[Giovanni],
Deep neural networks for anatomical brain segmentation,
BioImage15(20-28)
IEEE DOI 1510
Biological neural networks BibRef

Wang, L.P.[Li-Ping], Zeng, Z.M.[Zi-Ming], Zwiggelaar, R.[Reyer],
An Improved BET Method for Brain Segmentation,
ICPR14(3221-3226)
IEEE DOI 1412
Brain modeling BibRef

Lefevre, J.[Julien], Auzias, G.[Guillaume], Germanaud, D.[David],
Brain Lobes Revealed by Spectral Clustering,
ICPR14(562-567)
IEEE DOI 1412
Arrays BibRef

Firat, O.[Orhan], Oztekin, I.[Ilke], Vural, F.T.Y.[Fatos T. Yarman],
Deep learning for brain decoding,
ICIP14(2784-2788)
IEEE DOI 1502
Computer architecture BibRef

Onal, I.[Itir], Aksan, E.[Emre], Velioglu, B.[Burak], Firat, O.[Orhan], Ozay, M.[Mete], Oztekin, I.[Ilke], Vural, F.T.Y.[Fatos T. Yarman],
Modeling the Brain Connectivity for Pattern Analysis,
ICPR14(3339-3344)
IEEE DOI 1412
Bayes methods BibRef

Sut, B.[Bolan], Dinh, T.A.[Thien Anh], Ambastha, A.K.[Abhinit Kumar], Gong, T.X.[Tian-Xia], Silander, T.[Tomi], Lu, S.J.[Shi-Jian], Lim, C.C.T.[C.C. Tchoyoson], Pang, B.C.[Boon Chuan], Lee, C.K.[Cheng Kiang], Leong, T.Y.[Tze-Yun], Tan, C.L.[Chew Lim],
Automated Prediction of Glasgow Outcome Scale for Traumatic Brain Injury,
ICPR14(3245-3250)
IEEE DOI 1412
Brain modeling BibRef

Sanroma, G.[Gerard], Wu, G.R.[Guo-Rong], Thung, K.[Kim], Guo, Y.R.[Yan-Rong], Shen, D.G.[Ding-Gang],
Novel Multi-Atlas Segmentation by Matrix Completion,
MLMI14(207-214).
Springer DOI 1410
BibRef

Cabeen, R.P.[Ryan P.], Laidlaw, D.H.[David H.],
White Matter Supervoxel Segmentation by Axial DP-Means Clustering,
MCV13(95-104).
Springer DOI 1405
BibRef

Jensen, R.R.[Rasmus R.], Thorup, S.S.[Signe S.], Paulsen, R.R.[Rasmus R.], Darvann, T.A.[Tron A.], Hermann, N.V.[Nuno V.], Larsen, P.[Per], Kreiborg, S.[Sven], Larsen, R.[Rasmus],
Genus zero graph segmentation: Estimation of intracranial volume,
PRL(49), No. 1, 2014, pp. 259-263.
Elsevier DOI 1410
Intracranial volume BibRef

Jensen, R.R.[Rasmus R.], Thorup, S.S.[Signe S.], Paulsen, R.R.[Rasmus R.], Darvann, T.A.[Tron A.], Hermann, N.V.[Nuno V.],
Genus Zero Graph Segmentation: Estimation of Intracranial Volume [Conf],
SCIA13(290-298).
Springer DOI 1311
BibRef

Igual, L.[Laura], Soliva, J.C.[Joan Carles], Gimeno, R.[Roger], Escalera, S.[Sergio], Vilarroya, O.[Oscar], Radeva, P.I.[Petia I.],
Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention-Deficit/Hyperactivity Disorder,
ICIAR12(II: 222-229).
Springer DOI 1206
BibRef

Zhang, D.Q.[Dao-Qiang], Guo, Q.[Qimiao], Wu, G.R.[Guo-Rong], Shen, D.G.[Ding-Gang],
Sparse Patch-Based Label Fusion for Multi-Atlas Segmentation,
MBIA12(94-102).
Springer DOI 1210
BibRef

Cheng, B.[Bo], Zhang, D.Q.[Dao-Qiang], Chen, S.C.[Song-Can], Shen, D.G.[Ding-Gang],
Predicting Clinical Scores Using Semi-supervised Multimodal Relevance Vector Regression,
MLMI11(241-248).
Springer DOI 1109
clinical scores of neurological diseases BibRef

Last, C.[Carsten], Winkelbach, S.[Simon], Wahl, F.M.[Friedrich M.], Eichhorn, K.W.G.[Klaus W.G.], Bootz, F.[Friedrich],
A Model-Based Approach to the Segmentation of Nasal Cavity and Paranasal Sinus Boundaries,
DAGM10(333-342).
Springer DOI 1009
BibRef

Izhar, L.I.[Lila Iznita], Asirvadam, V.S.[Vijanth Sagayan], Lee, S.N.[San Nien],
Segmentation of Sinus Images for Grading of Severity of Sinusitis,
IVIC09(202-212).
Springer DOI 0911
BibRef

Tong, H.L.[Hau-Lee], Fauzi, M.F.A.[Mohammad Faizal Ahmad], Komiya, R.[Ryoichi],
Automated Segmentation and Retrieval System for CT Head Images,
IVIC09(97-109).
Springer DOI 0911
BibRef

Pérez, N.[Noel], Valdés, J.A.[José A.], Guevara, M.A.[Miguel A.], Rodríguez, L.A.[Luis A.], Molina, J.M.,
Set of Methods for Spontaneous ICH Segmentation and Tracking from CT Head Images,
CIARP07(212-220).
Springer DOI 0711
BibRef

Ciofolo, C.[Cybčle], Barillot, C.[Christian],
Shape Analysis and Fuzzy Control for 3D Competitive Segmentation of Brain Structures with Level Sets,
ECCV06(I: 458-470).
Springer DOI 0608
BibRef

Mohr, J., Hess, A., Scholz, M., Obermayer, K.,
Segmentation of 2 1/2 D brain image stacks with automatic extraction and visualization of functional information,
ICIP03(II: 1089-1092).
IEEE DOI 0312
BibRef

Ruan, S., Fadili, J., Xue, J., Bloyet, B.,
Unsupervised Segmentation of Three-dimensional Brain Images,
ICPR00(Vol III: 405-408).
IEEE DOI 0009
BibRef

Aurdal, L., Bloch, I., Maitre, H., Graffigne, C., and Adamsbaum, C.,
Continuous Label Bayesian Segmentation, Applications to Medical Brain Images,
ICIP97(II: 128-131).
IEEE DOI BibRef 9700

Mangin, J.F., Regis, J., Bloch, I., Frouin, V., Samson, Y., Lopez-Krahe, J.,
A MRF Based Random Graph Modelling the Human Cortical Topography,
CVRMed95(XX-YY) BibRef 9500

Loncaric, S.[Sven], Kovacevic, D.[Domagoj],
A method for segmentation of CT head images,
CIAP97(II: 388-395).
Springer DOI 9709
BibRef

Suzuki, H., Yoshizaki, K., Matsuo, M., Kashio, J.,
A Supporting System for Getting Tomograms and Screening with a Computerized 3D Brain Atlas and a Knowledge Database,
CVRMed95(XX-YY) BibRef 9500

Cawley, M.G., Natarajan, K.,
Model based segmentation of radiological images of the cranium,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Cortex, MRI Analysis, Models, 3-D .


Last update:Mar 16, 2024 at 20:36:19