A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data

We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition seq...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on medical imaging Vol. 21; no. 3; pp. 193 - 199
Main Authors Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.03.2002
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text
ISSN0278-0062
1558-254X
DOI10.1109/42.996338

Cover

Abstract We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
AbstractList In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm
In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
Author Yamany, S.M.
Mohamed, N.
Farag, A.A.
Moriarty, T.
Ahmed, M.N.
Author_xml – sequence: 1
  givenname: M.N.
  surname: Ahmed
  fullname: Ahmed, M.N.
  organization: Dept. of Electr. & Comput. Eng., Louisville Univ., KY, USA
– sequence: 2
  givenname: S.M.
  surname: Yamany
  fullname: Yamany, S.M.
– sequence: 3
  givenname: N.
  surname: Mohamed
  fullname: Mohamed, N.
– sequence: 4
  givenname: A.A.
  surname: Farag
  fullname: Farag, A.A.
– sequence: 5
  givenname: T.
  surname: Moriarty
  fullname: Moriarty, T.
BackLink http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=13622031$$DView record in Pascal Francis
https://www.ncbi.nlm.nih.gov/pubmed/11989844$$D View this record in MEDLINE/PubMed
BookMark eNqF0s1rFTEQAPAgFftaPXj1IEGw6mHbfEw2ybEUq4WKIAp6WrL5qCm7mzbZd2j_evPYZ4Ui7SmQ_GYymcke2pnS5BF6SckhpUQfATvUuuVcPUErKoRqmICfO2hFmFQNIS3bRXulXBJCQRD9DO1SqpVWACv06xiPycUQvcNhfXt7g20zejMVbIaLlOP8e8QhZdxHU3BVg8O-zHE0c0wTNpPDxV-MfpqXjRTwl29n2JnZPEdPgxmKf7Fd99GP04_fTz43518_nZ0cnzdWCDI3XnjpQk8YtRwos74HSlviuLMARhErXQ9aKi80AO97GTTTQgcRwBognu-jd0veq5yu17W4bozF-mEwk0_r0imliFZSkyoPHpSStgJE7eNjkCkulWLscUhr6bTdwPcPQtpKChxkSyt9c49epnWeagvrUwAUrUOt6PUWrfvRu-4q15Hkm-7vYCt4uwWmWDOEbCYbyz_HW8YI39x2tDibUynZh87GZZZzNnHoKOk2X6wD1i1frEZ8uBdxl_Q_9tVio_f-zm0P_wAUP9T4
CODEN ITMID4
CitedBy_id crossref_primary_10_1111_exsy_12206
crossref_primary_10_4015_S1016237216500253
crossref_primary_10_1109_JSTARS_2014_2330347
crossref_primary_10_1109_TFUZZ_2020_3044253
crossref_primary_10_1118_1_4929561
crossref_primary_10_1002_ima_22416
crossref_primary_10_1016_j_compmedimag_2008_08_004
crossref_primary_10_1016_j_patrec_2016_11_019
crossref_primary_10_1016_j_bspc_2010_07_005
crossref_primary_10_1016_j_patcog_2013_04_006
crossref_primary_10_1007_s40815_019_00787_8
crossref_primary_10_1016_j_patcog_2010_06_006
crossref_primary_10_1007_s11042_020_08753_5
crossref_primary_10_1016_j_jvcir_2018_04_007
crossref_primary_10_1016_j_cmpb_2014_03_003
crossref_primary_10_1166_jmihi_2021_3361
crossref_primary_10_1007_s11042_022_11904_5
crossref_primary_10_1016_j_cmpb_2008_06_012
crossref_primary_10_1155_2010_367297
crossref_primary_10_1109_ACCESS_2022_3230591
crossref_primary_10_1016_j_mri_2017_05_005
crossref_primary_10_1109_TFUZZ_2024_3435390
crossref_primary_10_1155_2015_240354
crossref_primary_10_1016_j_ins_2009_11_041
crossref_primary_10_1109_TEVC_2016_2524555
crossref_primary_10_1016_j_isprsjprs_2019_09_011
crossref_primary_10_1007_s10856_016_5717_2
crossref_primary_10_1016_j_sna_2022_113941
crossref_primary_10_1007_s00521_007_0091_0
crossref_primary_10_1016_j_cmpb_2008_06_005
crossref_primary_10_1016_j_ins_2019_07_027
crossref_primary_10_1007_s00371_023_02821_1
crossref_primary_10_1155_IJBI_2006_49515
crossref_primary_10_1016_j_dsp_2021_102963
crossref_primary_10_1016_j_measurement_2022_111414
crossref_primary_10_4028_www_scientific_net_AMR_487_622
crossref_primary_10_1109_ACCESS_2020_3015270
crossref_primary_10_1016_j_compbiomed_2013_10_029
crossref_primary_10_1016_j_bspc_2018_08_036
crossref_primary_10_1002_ima_22637
crossref_primary_10_1016_j_patrec_2013_05_002
crossref_primary_10_1016_j_asoc_2015_10_044
crossref_primary_10_1016_j_ins_2022_08_082
crossref_primary_10_1007_s00500_018_3594_y
crossref_primary_10_3724_SP_J_1087_2011_00375
crossref_primary_10_1016_j_neucom_2015_05_025
crossref_primary_10_3390_info10020074
crossref_primary_10_1016_j_eij_2012_01_004
crossref_primary_10_1016_j_mri_2011_09_008
crossref_primary_10_1007_s11042_023_15230_2
crossref_primary_10_1007_s13369_024_08950_6
crossref_primary_10_1049_iet_ipr_2016_0282
crossref_primary_10_1142_S0219622009003508
crossref_primary_10_1016_j_procs_2015_02_030
crossref_primary_10_3103_S0146411622010047
crossref_primary_10_1109_JSTARS_2017_2743338
crossref_primary_10_1007_s10489_024_05813_3
crossref_primary_10_1088_1742_6596_1783_1_012013
crossref_primary_10_3390_rs8020124
crossref_primary_10_1016_j_dsp_2018_08_022
crossref_primary_10_1016_j_patrec_2013_04_010
crossref_primary_10_1109_TFUZZ_2020_3029296
crossref_primary_10_21205_deufmd_2020226529
crossref_primary_10_1016_j_engappai_2023_107776
crossref_primary_10_1016_j_knosys_2016_01_001
crossref_primary_10_1016_j_measurement_2021_109076
crossref_primary_10_1080_02533839_2012_747064
crossref_primary_10_1016_j_jvcir_2022_103732
crossref_primary_10_1049_iet_ipr_2015_0236
crossref_primary_10_3724_SP_J_1016_2012_00375
crossref_primary_10_1016_j_bspc_2018_06_001
crossref_primary_10_26634_jip_2_2_3401
crossref_primary_10_1016_j_eswa_2014_07_026
crossref_primary_10_3233_JIFS_191579
crossref_primary_10_1016_j_patrec_2013_04_021
crossref_primary_10_1002_cem_2728
crossref_primary_10_1049_iet_ipr_2013_0178
crossref_primary_10_1109_TIP_2015_2488900
crossref_primary_10_1155_2021_1053242
crossref_primary_10_1049_iet_ipr_2019_1631
crossref_primary_10_1007_s42452_020_04110_1
crossref_primary_10_1016_j_inffus_2022_12_008
crossref_primary_10_1007_s10851_017_0759_8
crossref_primary_10_1007_s12209_013_2060_2
crossref_primary_10_1007_s13042_014_0227_3
crossref_primary_10_1016_j_asej_2016_01_010
crossref_primary_10_1109_TIP_2019_2930148
crossref_primary_10_1118_1_4907993
crossref_primary_10_1007_s40815_018_0537_9
crossref_primary_10_1007_s00500_021_05697_2
crossref_primary_10_1088_1361_6560_abb31f
crossref_primary_10_4304_jcp_9_4_1033_1039
crossref_primary_10_1016_j_artmed_2007_02_003
crossref_primary_10_1007_s00371_022_02430_4
crossref_primary_10_1016_j_sigpro_2012_08_024
crossref_primary_10_1007_s11042_022_12367_4
crossref_primary_10_3390_s110706697
crossref_primary_10_4236_jcc_2016_410002
crossref_primary_10_1007_s10462_012_9318_2
crossref_primary_10_1007_s11042_019_07787_8
crossref_primary_10_1007_s10044_023_01195_3
crossref_primary_10_1016_j_bspc_2013_07_010
crossref_primary_10_3390_app142311227
crossref_primary_10_1109_TFUZZ_2020_3029285
crossref_primary_10_1155_2018_9476432
crossref_primary_10_1016_j_eswa_2020_114327
crossref_primary_10_1016_j_compbiomed_2010_04_001
crossref_primary_10_1007_s11760_013_0455_0
crossref_primary_10_3390_math11020285
crossref_primary_10_1155_2021_4683609
crossref_primary_10_1155_2013_316546
crossref_primary_10_1007_s00371_021_02319_8
crossref_primary_10_1016_j_compmedimag_2009_11_001
crossref_primary_10_1007_s11042_017_5589_6
crossref_primary_10_1007_s13042_023_02004_3
crossref_primary_10_1016_j_neuroimage_2005_09_054
crossref_primary_10_4236_ojmi_2013_34020
crossref_primary_10_3390_e19110578
crossref_primary_10_3182_20060920_3_FR_2912_00046
crossref_primary_10_1109_TCYB_2019_2921779
crossref_primary_10_1109_TFUZZ_2019_2917809
crossref_primary_10_1109_ACCESS_2021_3071754
crossref_primary_10_1016_j_asoc_2023_110829
crossref_primary_10_1002_jmri_22344
crossref_primary_10_1109_TCE_2010_5681159
crossref_primary_10_2174_1874120701913010134
crossref_primary_10_3390_rs15143463
crossref_primary_10_1007_s12652_020_02584_w
crossref_primary_10_1016_j_sigpro_2019_02_013
crossref_primary_10_4028_www_scientific_net_AMR_748_651
crossref_primary_10_1007_s40815_020_01015_4
crossref_primary_10_1155_2008_417293
crossref_primary_10_1109_TIM_2018_2884017
crossref_primary_10_1142_S1793524516500182
crossref_primary_10_1155_2016_9618706
crossref_primary_10_1051_matecconf_201823203011
crossref_primary_10_1016_j_dsp_2012_09_016
crossref_primary_10_1016_j_ins_2015_02_015
crossref_primary_10_1007_s00521_016_2300_1
crossref_primary_10_1016_j_asoc_2019_105758
crossref_primary_10_3390_rs14143490
crossref_primary_10_1016_j_compmedimag_2012_04_002
crossref_primary_10_4015_S1016237214500100
crossref_primary_10_1142_S0218001408006788
crossref_primary_10_3788_LOP231545
crossref_primary_10_1109_TGRS_2013_2281854
crossref_primary_10_1016_j_compbiomed_2025_110053
crossref_primary_10_1016_j_knosys_2023_110522
crossref_primary_10_1049_ipr2_12581
crossref_primary_10_1587_transinf_E97_D_1011
crossref_primary_10_3389_fnins_2021_662674
crossref_primary_10_1016_j_knosys_2016_12_006
crossref_primary_10_1016_j_measurement_2011_09_005
crossref_primary_10_1016_j_asoc_2012_03_060
crossref_primary_10_1109_ACCESS_2019_2937124
crossref_primary_10_4028_www_scientific_net_AMM_229_231_1356
crossref_primary_10_1002_cpe_6084
crossref_primary_10_1007_s11063_021_10441_w
crossref_primary_10_1016_j_cageo_2019_06_008
crossref_primary_10_1016_j_asoc_2015_12_022
crossref_primary_10_1016_j_eswa_2024_126035
crossref_primary_10_1080_21681163_2015_1027270
crossref_primary_10_1109_TIP_2016_2574992
crossref_primary_10_3906_elk_1510_37
crossref_primary_10_1007_s11042_015_2510_z
crossref_primary_10_1007_s12652_020_02682_9
crossref_primary_10_1016_j_cmpb_2012_01_005
crossref_primary_10_1109_TFUZZ_2023_3235392
crossref_primary_10_1007_s11432_012_4556_0
crossref_primary_10_1016_j_patcog_2014_01_017
crossref_primary_10_1016_j_jneumeth_2017_12_006
crossref_primary_10_1016_j_asoc_2018_01_003
crossref_primary_10_1016_j_jss_2011_12_020
crossref_primary_10_1109_ACCESS_2019_2928415
crossref_primary_10_1016_j_jvcir_2018_03_010
crossref_primary_10_1109_ACCESS_2019_2900089
crossref_primary_10_1109_TIP_2015_2456505
crossref_primary_10_1007_s40815_023_01490_5
crossref_primary_10_1007_s00500_022_07531_9
crossref_primary_10_1117_1_JMI_6_3_037001
crossref_primary_10_1007_s11760_013_0499_1
crossref_primary_10_1186_s12880_015_0097_5
crossref_primary_10_1007_s11517_016_1484_y
crossref_primary_10_1016_j_asoc_2012_02_010
crossref_primary_10_1049_iet_ipr_2011_0128
crossref_primary_10_1016_j_compmedimag_2009_12_002
crossref_primary_10_1007_s10462_024_11057_x
crossref_primary_10_1007_s11042_016_3884_2
crossref_primary_10_1515_jisys_2016_0241
crossref_primary_10_1088_1402_4896_ad2b3a
crossref_primary_10_1007_s00500_014_1481_8
crossref_primary_10_1016_j_neucom_2019_01_042
crossref_primary_10_1109_TMI_2011_2165342
crossref_primary_10_1140_epjs_s11734_022_00474_0
crossref_primary_10_1109_TITB_2012_2185852
crossref_primary_10_1088_1742_6596_1911_1_012003
crossref_primary_10_1049_iet_ipr_2019_0942
crossref_primary_10_1080_02564602_2014_906861
crossref_primary_10_1109_TSMCB_2004_831165
crossref_primary_10_1016_j_mri_2009_01_024
crossref_primary_10_1007_s11517_020_02221_w
crossref_primary_10_1142_S0218001405004447
crossref_primary_10_1117_1_1904066
crossref_primary_10_1016_j_patrec_2013_08_027
crossref_primary_10_1371_journal_pone_0151326
crossref_primary_10_1109_TCYB_2018_2830977
crossref_primary_10_1007_s40815_022_01288_x
crossref_primary_10_1007_s12652_020_02257_8
crossref_primary_10_1016_j_neucom_2016_03_046
crossref_primary_10_1007_s10462_011_9219_9
crossref_primary_10_1016_j_mri_2012_04_005
crossref_primary_10_1049_iet_ipr_2017_0760
crossref_primary_10_1007_s00371_021_02352_7
crossref_primary_10_1097_WNP_0b013e3182570f94
crossref_primary_10_1016_j_dsp_2021_103200
crossref_primary_10_1016_j_isprsjprs_2014_08_006
crossref_primary_10_1016_j_patcog_2020_107333
crossref_primary_10_1016_j_asoc_2024_111712
crossref_primary_10_1016_j_bspc_2016_06_012
crossref_primary_10_1016_j_patrec_2016_02_009
crossref_primary_10_1088_0957_0233_26_10_105402
crossref_primary_10_1007_s11063_024_11450_1
crossref_primary_10_1016_j_asoc_2018_03_054
crossref_primary_10_1016_j_jksuci_2019_04_006
crossref_primary_10_1007_s00500_020_05403_8
crossref_primary_10_1109_TFUZZ_2017_2756827
crossref_primary_10_1007_s10916_017_0821_5
crossref_primary_10_1080_1206212X_2024_2380975
crossref_primary_10_1016_j_sigpro_2015_12_007
crossref_primary_10_1007_s10489_022_03255_3
crossref_primary_10_1109_TMI_2012_2236349
crossref_primary_10_3390_app9224967
crossref_primary_10_1016_j_patrec_2005_03_019
crossref_primary_10_1007_s11042_022_13622_4
crossref_primary_10_1109_TFUZZ_2020_3037972
crossref_primary_10_3390_brainsci9100289
crossref_primary_10_1109_TFUZZ_2018_2796074
crossref_primary_10_1016_j_asoc_2015_12_003
crossref_primary_10_3390_rs9090967
crossref_primary_10_1080_01431161_2017_1420934
crossref_primary_10_1109_TIP_2018_2882925
crossref_primary_10_1016_j_patcog_2010_11_017
crossref_primary_10_1155_2019_4762490
crossref_primary_10_3390_rs9090960
crossref_primary_10_1007_s11227_022_04769_w
crossref_primary_10_1007_s10439_008_9520_1
crossref_primary_10_1016_j_patcog_2016_06_020
crossref_primary_10_1007_s11042_022_12840_0
crossref_primary_10_1007_s13042_025_02575_3
crossref_primary_10_1016_j_asoc_2014_12_034
crossref_primary_10_1016_j_dsp_2021_103351
crossref_primary_10_1016_j_procs_2015_06_078
crossref_primary_10_1016_j_knosys_2021_107769
crossref_primary_10_3906_elk_1607_103
crossref_primary_10_1007_s10851_012_0376_5
crossref_primary_10_1016_j_mri_2014_03_010
crossref_primary_10_1007_s11265_014_0898_8
crossref_primary_10_2465_jmps_171127
crossref_primary_10_1016_j_asoc_2018_04_031
crossref_primary_10_1016_j_jvcir_2020_102964
crossref_primary_10_1007_s11042_017_4683_0
crossref_primary_10_1109_JSTARS_2018_2846603
crossref_primary_10_1109_TAMD_2015_2416976
crossref_primary_10_1109_TIP_2022_3154922
crossref_primary_10_1002_int_22668
crossref_primary_10_1016_S0730_725X_03_00185_1
crossref_primary_10_1016_j_ejfs_2014_03_004
crossref_primary_10_1007_s00138_013_0546_5
crossref_primary_10_1155_2013_219407
crossref_primary_10_1109_JBHI_2018_2884208
crossref_primary_10_1109_JSTSP_2008_2010631
crossref_primary_10_1016_j_sigpro_2020_107483
crossref_primary_10_1007_s10489_022_04315_4
crossref_primary_10_1007_s11750_014_0333_0
crossref_primary_10_1155_2019_5984649
crossref_primary_10_1016_j_cmpb_2011_10_010
crossref_primary_10_1109_TIP_2011_2170702
crossref_primary_10_1016_j_eswa_2022_118811
crossref_primary_10_1016_j_jvcir_2014_01_014
crossref_primary_10_1016_j_bspc_2021_103207
crossref_primary_10_1155_2020_1782531
crossref_primary_10_1109_TFUZZ_2020_2973121
crossref_primary_10_1109_ACCESS_2020_3011224
crossref_primary_10_1080_21681163_2013_767085
crossref_primary_10_1007_s12559_021_09988_6
crossref_primary_10_1016_j_bspc_2018_11_008
crossref_primary_10_1002_ima_22078
crossref_primary_10_1016_j_neucom_2012_12_067
crossref_primary_10_1016_j_mri_2019_05_043
crossref_primary_10_3390_wevj10010001
crossref_primary_10_3233_XST_160563
crossref_primary_10_1016_j_ins_2014_09_023
crossref_primary_10_3390_s19245555
crossref_primary_10_1016_j_scijus_2014_04_005
crossref_primary_10_1371_journal_pone_0210803
crossref_primary_10_1016_j_asoc_2016_01_055
crossref_primary_10_1007_s11760_023_02565_4
crossref_primary_10_1016_j_eswa_2007_09_003
crossref_primary_10_1007_s00521_021_06677_1
crossref_primary_10_1038_s41598_024_81648_9
crossref_primary_10_1109_TNB_2012_2189414
crossref_primary_10_1016_j_engappai_2022_104672
crossref_primary_10_1088_1757_899X_490_7_072010
crossref_primary_10_3390_s20185097
crossref_primary_10_1016_j_ins_2013_01_021
crossref_primary_10_1088_0031_9155_49_17_020
crossref_primary_10_1007_s11760_016_0878_5
crossref_primary_10_1007_s10916_016_0507_4
crossref_primary_10_1016_j_bspc_2020_102089
crossref_primary_10_1016_j_compbiomed_2012_10_002
crossref_primary_10_1007_s12652_018_0762_y
crossref_primary_10_1016_j_isatra_2016_09_005
crossref_primary_10_1016_j_ins_2024_121205
crossref_primary_10_1118_1_4754654
crossref_primary_10_1108_IJICC_06_2016_0021
crossref_primary_10_1016_j_neucom_2010_08_021
crossref_primary_10_1016_j_patcog_2011_11_001
crossref_primary_10_1109_TGRS_2017_2702061
crossref_primary_10_4015_S1016237219500200
crossref_primary_10_1137_090753887
crossref_primary_10_1016_j_compmedimag_2015_11_005
crossref_primary_10_1016_j_measurement_2010_03_013
crossref_primary_10_3182_20050703_6_CZ_1902_02155
crossref_primary_10_1088_1361_6560_ac9a98
crossref_primary_10_1007_s11227_016_1897_2
crossref_primary_10_1016_j_neucom_2012_12_081
crossref_primary_10_1016_j_ins_2017_08_083
crossref_primary_10_1016_j_asoc_2016_07_051
crossref_primary_10_1109_TFUZZ_2019_2930478
crossref_primary_10_1177_1748301816668025
crossref_primary_10_1109_LSP_2013_2244080
crossref_primary_10_1016_j_dsp_2019_102615
crossref_primary_10_1016_j_asoc_2009_08_002
crossref_primary_10_1117_1_JMI_6_1_014002
crossref_primary_10_1016_j_spasta_2019_03_002
crossref_primary_10_1186_1751_0473_8_20
crossref_primary_10_1016_j_asoc_2019_105838
crossref_primary_10_1109_TBME_2014_2344660
crossref_primary_10_1007_s11804_011_1043_8
crossref_primary_10_1088_1742_6596_1237_3_032024
crossref_primary_10_1016_j_compag_2017_03_004
crossref_primary_10_1080_10798587_2015_1095479
crossref_primary_10_1007_s40815_023_01485_2
crossref_primary_10_1007_s00500_009_0528_8
crossref_primary_10_1016_j_eswa_2018_08_013
crossref_primary_10_1049_iet_ipr_2017_0473
crossref_primary_10_1109_JSTARS_2018_2792841
crossref_primary_10_1109_TFUZZ_2018_2889018
crossref_primary_10_1142_S0218001414550027
crossref_primary_10_3390_jimaging3040067
crossref_primary_10_3390_sym11060753
crossref_primary_10_3390_sym10110610
crossref_primary_10_1049_iet_cvi_2016_0278
crossref_primary_10_1007_s12530_012_9066_1
crossref_primary_10_1118_1_3584199
crossref_primary_10_1016_j_mri_2019_06_010
crossref_primary_10_1016_j_neuroimage_2009_06_039
crossref_primary_10_1007_s00371_021_02126_1
crossref_primary_10_1016_j_asoc_2020_106468
crossref_primary_10_1016_j_eswa_2010_09_040
crossref_primary_10_1088_0031_9155_55_14_013
crossref_primary_10_1016_j_cag_2013_10_012
crossref_primary_10_1002_ima_22267
crossref_primary_10_3390_e15083295
crossref_primary_10_1007_s00500_022_07269_4
crossref_primary_10_1080_03772063_2021_1913072
crossref_primary_10_3389_fncom_2024_1425008
crossref_primary_10_1016_j_sigpro_2017_07_023
crossref_primary_10_1016_j_jvcir_2016_03_027
crossref_primary_10_1186_1687_5281_2014_21
crossref_primary_10_1109_TCYB_2021_3064552
crossref_primary_10_1016_j_camwa_2015_11_003
crossref_primary_10_1016_j_matcom_2018_02_001
crossref_primary_10_1016_j_ins_2015_04_050
crossref_primary_10_7717_peerj_5416
crossref_primary_10_1016_j_mri_2018_10_005
crossref_primary_10_1016_j_compeleceng_2017_01_018
crossref_primary_10_1002_ima_22491
crossref_primary_10_1109_JSTARS_2024_3398361
crossref_primary_10_1016_j_patcog_2009_01_023
crossref_primary_10_1007_s11042_016_3399_x
crossref_primary_10_1016_j_visinf_2021_12_001
crossref_primary_10_4028_www_scientific_net_AMM_33_70
crossref_primary_10_1007_s00371_014_0978_6
crossref_primary_10_4103_digm_digm_7_19
crossref_primary_10_1118_1_4937597
crossref_primary_10_1016_j_compmedimag_2010_12_001
crossref_primary_10_1002_mrm_25751
crossref_primary_10_1109_LGRS_2012_2231662
crossref_primary_10_1016_j_ins_2020_10_039
crossref_primary_10_1117_1_JEI_27_3_033044
crossref_primary_10_1016_j_acra_2015_09_010
crossref_primary_10_1016_j_compmedimag_2007_08_007
crossref_primary_10_1109_TIP_2015_2451957
crossref_primary_10_1088_0031_9155_57_15_5035
crossref_primary_10_1109_JSTARS_2012_2191537
crossref_primary_10_1109_TIP_2012_2219547
crossref_primary_10_1109_ACCESS_2016_2590440
crossref_primary_10_1016_j_patcog_2021_108201
crossref_primary_10_1109_TBME_2013_2279635
crossref_primary_10_1007_s11042_017_4687_9
crossref_primary_10_1016_j_neucom_2008_12_034
crossref_primary_10_1364_AO_58_004812
crossref_primary_10_3390_s22155906
crossref_primary_10_1016_j_asoc_2017_12_024
crossref_primary_10_1007_s10916_014_0068_3
crossref_primary_10_1007_s11235_008_9143_8
crossref_primary_10_3389_fpsyg_2014_00337
crossref_primary_10_1016_j_bspc_2023_105348
crossref_primary_10_1186_s40535_015_0010_x
crossref_primary_10_1142_S0218001413550057
crossref_primary_10_3390_rs14153704
crossref_primary_10_3389_fnins_2021_670745
crossref_primary_10_1016_j_ins_2012_08_026
crossref_primary_10_3233_IFS_151820
crossref_primary_10_3389_fpsyg_2014_00343
crossref_primary_10_1002_cpe_5214
crossref_primary_10_1016_j_knosys_2021_108008
crossref_primary_10_1109_TMI_2008_2004429
crossref_primary_10_1016_j_media_2012_09_004
crossref_primary_10_1002_col_22023
crossref_primary_10_1016_j_camwa_2019_06_010
crossref_primary_10_1134_S105466181703004X
crossref_primary_10_1016_j_mri_2014_05_003
crossref_primary_10_1080_02564602_2015_1027307
crossref_primary_10_1080_13682199_2023_2210400
crossref_primary_10_1631_jzus_C1100288
crossref_primary_10_3233_THC_213149
crossref_primary_10_3233_IFS_151811
crossref_primary_10_1016_j_compmedimag_2008_02_002
crossref_primary_10_1016_j_engappai_2020_103571
crossref_primary_10_1177_1094342013476120
crossref_primary_10_1007_s10278_018_0050_6
crossref_primary_10_1016_j_patcog_2012_03_009
crossref_primary_10_1016_j_patcog_2017_03_009
crossref_primary_10_1186_s12859_020_3466_1
crossref_primary_10_1155_2016_3406406
crossref_primary_10_1007_s11042_023_16569_2
crossref_primary_10_1007_s10489_024_06078_6
crossref_primary_10_1021_acs_langmuir_9b01603
crossref_primary_10_1049_el_20040313
crossref_primary_10_1016_j_artmed_2011_01_004
crossref_primary_10_1016_j_rse_2021_112297
crossref_primary_10_1109_JSTARS_2020_2987653
crossref_primary_10_3390_rs14071621
crossref_primary_10_1007_s10489_020_01977_w
crossref_primary_10_1016_j_eswa_2017_12_046
crossref_primary_10_1049_iet_ipr_2016_0539
crossref_primary_10_1109_TBME_2013_2244598
crossref_primary_10_1016_j_engfracmech_2022_109002
crossref_primary_10_1016_j_ijleo_2015_10_049
crossref_primary_10_1080_01431161_2018_1434325
crossref_primary_10_1002_widm_49
crossref_primary_10_1007_s00500_016_2210_2
crossref_primary_10_1029_2004WR003299
crossref_primary_10_3233_JIFS_191973
crossref_primary_10_1109_TFUZZ_2005_856558
crossref_primary_10_1016_j_jss_2008_07_019
crossref_primary_10_1109_TIP_2009_2019433
crossref_primary_10_1016_j_patrec_2008_04_016
crossref_primary_10_1016_j_asoc_2021_107245
crossref_primary_10_1016_j_mri_2019_04_011
crossref_primary_10_1016_j_asoc_2016_03_010
crossref_primary_10_1109_LSP_2012_2230626
crossref_primary_10_1016_j_patrec_2008_03_012
crossref_primary_10_1109_TFUZZ_2021_3099560
crossref_primary_10_4028_www_scientific_net_AMR_860_863_2868
crossref_primary_10_1007_s10115_014_0741_3
crossref_primary_10_3233_IDA_216058
crossref_primary_10_1016_j_engappai_2011_09_017
crossref_primary_10_1080_21655979_2020_1747834
crossref_primary_10_1016_j_dsp_2007_11_005
crossref_primary_10_1016_j_jbi_2013_02_001
crossref_primary_10_1142_S021800141860011X
crossref_primary_10_1016_j_asoc_2022_109939
crossref_primary_10_3390_rs12244115
crossref_primary_10_1007_s11517_014_1210_6
crossref_primary_10_1016_j_compbiomed_2024_108412
crossref_primary_10_1007_s41870_020_00496_8
crossref_primary_10_1049_iet_ipr_2019_1209
crossref_primary_10_4028_www_scientific_net_AMM_121_126_1151
crossref_primary_10_1016_j_eswa_2017_11_040
crossref_primary_10_1117_1_OE_56_12_123103
crossref_primary_10_1016_j_future_2016_03_004
crossref_primary_10_3390_rs14174419
crossref_primary_10_1155_2013_921721
crossref_primary_10_4028_www_scientific_net_AMR_411_497
crossref_primary_10_1109_ACCESS_2019_2963363
crossref_primary_10_1109_TIE_2006_874259
crossref_primary_10_1109_TFUZZ_2011_2160025
crossref_primary_10_1007_s10732_014_9267_9
crossref_primary_10_1007_s11430_017_9224_6
crossref_primary_10_1016_j_ejrs_2020_06_001
crossref_primary_10_1109_ACCESS_2020_3029773
crossref_primary_10_4028_www_scientific_net_AMM_333_335_938
crossref_primary_10_1007_s10278_023_00899_6
crossref_primary_10_1007_s41095_021_0239_3
crossref_primary_10_1109_TIP_2005_852199
crossref_primary_10_1080_09540091_2014_970126
crossref_primary_10_1109_TFUZZ_2020_2985930
crossref_primary_10_1109_LGRS_2017_2746625
crossref_primary_10_1049_iet_cvi_2018_5332
crossref_primary_10_1177_1729881420909600
crossref_primary_10_1002_jemt_24413
crossref_primary_10_1007_s00500_019_04593_0
crossref_primary_10_1016_j_fss_2018_01_019
crossref_primary_10_1007_s11042_017_5096_9
crossref_primary_10_1007_s11042_019_08041_x
crossref_primary_10_1175_WAF_D_16_0112_1
crossref_primary_10_1007_s10044_015_0462_6
crossref_primary_10_1049_ipr2_12078
crossref_primary_10_1371_journal_pone_0183943
crossref_primary_10_1016_j_media_2008_05_003
crossref_primary_10_4304_jmm_9_4_499_505
crossref_primary_10_1016_j_compmedimag_2005_10_001
crossref_primary_10_1088_0031_9155_52_12_016
crossref_primary_10_1109_TMI_2016_2636026
crossref_primary_10_1007_s12293_015_0165_y
crossref_primary_10_1007_s13369_013_0559_4
crossref_primary_10_1109_TMI_2009_2012896
crossref_primary_10_1016_j_patcog_2009_11_015
crossref_primary_10_2174_1573405617666211126154101
crossref_primary_10_1002_jmri_20695
crossref_primary_10_1155_2016_9871529
crossref_primary_10_1016_j_compbiolchem_2020_107247
crossref_primary_10_1016_j_proeng_2011_12_692
crossref_primary_10_1016_j_kijoms_2015_11_006
crossref_primary_10_1016_j_procs_2016_03_014
crossref_primary_10_1109_TFUZZ_2022_3144489
crossref_primary_10_1016_j_sigpro_2019_107347
crossref_primary_10_1109_TFUZZ_2013_2286993
crossref_primary_10_1007_s00034_019_01126_w
crossref_primary_10_1109_JSTARS_2014_2308531
crossref_primary_10_3233_THC_161208
crossref_primary_10_1108_EC_08_2023_0403
crossref_primary_10_1016_j_ijleo_2013_03_019
crossref_primary_10_1155_2016_8508329
crossref_primary_10_1007_s40846_017_0353_y
crossref_primary_10_1016_j_neucom_2022_12_023
crossref_primary_10_3390_rs13204163
crossref_primary_10_1007_s11042_016_4218_0
crossref_primary_10_1007_s11042_020_08898_3
crossref_primary_10_1016_j_neucom_2023_126842
crossref_primary_10_4018_IJFSA_2018100105
crossref_primary_10_1002_stvr_408
crossref_primary_10_1007_s00357_023_09443_1
crossref_primary_10_1016_j_cmpb_2017_08_017
crossref_primary_10_1109_TFUZZ_2024_3373509
crossref_primary_10_1007_s10278_018_0149_9
crossref_primary_10_17485_ijst_2017_v10i36_112402
crossref_primary_10_1109_TMI_2016_2587836
crossref_primary_10_1002_nla_750
crossref_primary_10_1007_s11760_016_0992_4
crossref_primary_10_1118_1_3264615
crossref_primary_10_1002_cpe_3731
crossref_primary_10_1016_j_cam_2010_08_033
crossref_primary_10_1016_j_jvcir_2017_04_011
crossref_primary_10_1016_j_neuroimage_2007_03_021
crossref_primary_10_1016_j_artmed_2003_11_006
crossref_primary_10_1109_TFUZZ_2018_2883033
crossref_primary_10_1016_j_engappai_2021_104209
crossref_primary_10_1109_ACCESS_2020_3003138
crossref_primary_10_1109_TIP_2013_2263808
crossref_primary_10_3233_JIFS_202401
crossref_primary_10_1118_1_1944912
crossref_primary_10_1109_TFUZZ_2023_3319663
crossref_primary_10_7763_IJMLC_2014_V4_389
crossref_primary_10_1109_TFUZZ_2008_2005008
crossref_primary_10_1002_itl2_549
crossref_primary_10_1007_s11042_018_7003_4
crossref_primary_10_1109_TMI_2006_891486
crossref_primary_10_1016_j_ast_2017_12_039
crossref_primary_10_1080_01431161_2018_1552812
crossref_primary_10_1002_cem_2825
crossref_primary_10_1016_j_ins_2013_10_002
crossref_primary_10_1016_j_swevo_2020_100792
crossref_primary_10_1007_s13042_020_01151_1
crossref_primary_10_1007_s40846_018_0454_2
crossref_primary_10_1080_2150704X_2014_973075
crossref_primary_10_1016_j_jneumeth_2018_10_007
crossref_primary_10_1007_s40815_017_0411_1
crossref_primary_10_1155_2013_706919
crossref_primary_10_1109_LGRS_2013_2257674
crossref_primary_10_3390_s22249983
crossref_primary_10_1142_S0218126610006001
crossref_primary_10_1109_TFUZZ_2019_2930030
crossref_primary_10_1155_2015_450341
crossref_primary_10_1007_s11042_023_14703_8
crossref_primary_10_1166_jmihi_2021_3860
crossref_primary_10_1007_s10044_014_0413_7
crossref_primary_10_1016_j_bspc_2014_09_013
crossref_primary_10_17721_1812_5409_2022_2_6
crossref_primary_10_1002_hbm_20013
crossref_primary_10_1007_s11554_023_01371_y
crossref_primary_10_1002_col_22975
crossref_primary_10_1016_j_neucom_2011_03_010
crossref_primary_10_1109_TFUZZ_2022_3220925
crossref_primary_10_1016_j_eswa_2020_114063
crossref_primary_10_1016_j_neucom_2012_10_022
crossref_primary_10_1016_j_bbe_2018_04_003
crossref_primary_10_3233_JIFS_151345
crossref_primary_10_1109_ACCESS_2024_3426518
crossref_primary_10_1016_j_cviu_2014_04_010
crossref_primary_10_1016_j_asoc_2019_105888
crossref_primary_10_3390_math9192392
crossref_primary_10_1142_S146902680600185X
crossref_primary_10_1007_s12065_021_00689_5
crossref_primary_10_1109_TBME_2017_2688453
crossref_primary_10_1109_JTEHM_2019_2898870
crossref_primary_10_1016_j_ijar_2023_02_013
crossref_primary_10_1016_j_jvcir_2014_11_005
crossref_primary_10_1109_TIP_2020_2975717
crossref_primary_10_1088_0031_9155_58_23_8573
crossref_primary_10_1016_j_rsase_2020_100319
crossref_primary_10_1109_TFUZZ_2020_2991306
crossref_primary_10_1007_s11042_020_09981_5
crossref_primary_10_1155_2014_403095
crossref_primary_10_1007_s11760_016_0863_z
crossref_primary_10_1007_s11042_020_09160_6
crossref_primary_10_1007_s00034_024_02758_3
crossref_primary_10_4304_jcp_9_7_1678_1683
crossref_primary_10_1007_s10851_007_0058_x
crossref_primary_10_1109_TKDE_2021_3076521
crossref_primary_10_3390_diagnostics12102535
crossref_primary_10_1016_j_media_2014_06_014
crossref_primary_10_2174_1573405616666210104111218
crossref_primary_10_1002_hcs2_119
crossref_primary_10_1016_j_media_2005_09_004
crossref_primary_10_3390_a8010032
crossref_primary_10_1007_s11554_014_0468_0
crossref_primary_10_1109_ACCESS_2020_2995660
crossref_primary_10_1109_TFUZZ_2024_3460075
crossref_primary_10_5721_EuJRS20134617
crossref_primary_10_1016_j_neucom_2008_07_014
crossref_primary_10_1007_s10915_016_0183_z
crossref_primary_10_1007_s10489_021_02722_7
crossref_primary_10_1016_j_firesaf_2018_08_012
crossref_primary_10_1007_s11042_023_16806_8
crossref_primary_10_1155_2020_5648206
crossref_primary_10_1109_TGRS_2018_2872875
crossref_primary_10_1155_2014_747549
crossref_primary_10_1117_1_JBO_17_8_086008
crossref_primary_10_1088_1742_6596_1229_1_012020
crossref_primary_10_1049_iet_ipr_2017_0407
crossref_primary_10_1016_j_bbe_2016_01_001
crossref_primary_10_3390_rs14051117
crossref_primary_10_1155_2015_485495
crossref_primary_10_1007_s13246_024_01408_x
crossref_primary_10_1016_j_patcog_2006_07_011
crossref_primary_10_1007_s00500_020_04926_4
crossref_primary_10_1016_j_eswa_2021_115216
crossref_primary_10_1109_TCYB_2014_2352343
crossref_primary_10_1080_01431161_2010_484821
crossref_primary_10_1155_2015_185726
crossref_primary_10_1080_10255840903131878
crossref_primary_10_1016_j_dsp_2015_04_009
crossref_primary_10_1016_j_patcog_2009_04_013
crossref_primary_10_1134_S1061830924602162
crossref_primary_10_1007_s00500_017_2955_2
crossref_primary_10_1109_TSMC_2019_2931699
crossref_primary_10_1155_2019_5890794
crossref_primary_10_1016_j_eswa_2017_09_049
crossref_primary_10_1007_s11771_008_0161_1
crossref_primary_10_1016_j_engappai_2022_105335
crossref_primary_10_1007_s11390_016_1643_5
crossref_primary_10_3389_fninf_2016_00030
crossref_primary_10_1007_s00500_020_04728_8
crossref_primary_10_1007_s11042_018_6421_7
crossref_primary_10_1186_1687_5281_2014_8
crossref_primary_10_3233_JIFS_235967
crossref_primary_10_1007_s00500_010_0553_7
crossref_primary_10_1109_TFUZZ_2023_3333571
crossref_primary_10_1016_j_media_2008_06_014
crossref_primary_10_1142_S021800141850012X
crossref_primary_10_1109_TFUZZ_2021_3063818
crossref_primary_10_1016_j_patcog_2024_110681
crossref_primary_10_1109_TCYB_2022_3217897
crossref_primary_10_1007_s11042_019_08089_9
crossref_primary_10_1007_s11548_017_1552_2
crossref_primary_10_1007_s00500_015_1920_1
crossref_primary_10_1007_s11227_021_03928_9
crossref_primary_10_1109_TFUZZ_2017_2686804
crossref_primary_10_1007_s10489_016_0858_z
crossref_primary_10_1016_j_eswa_2024_124943
crossref_primary_10_1016_j_mri_2010_03_009
crossref_primary_10_1177_1558925020978323
crossref_primary_10_1142_S0219519415500761
crossref_primary_10_1007_s00034_022_02175_4
crossref_primary_10_1007_s00500_023_09379_z
crossref_primary_10_1109_TIM_2025_3527611
crossref_primary_10_4028_www_scientific_net_AMR_488_489_904
crossref_primary_10_1002_mp_12350
crossref_primary_10_1007_s12145_020_00532_y
crossref_primary_10_3390_su15010639
crossref_primary_10_1007_s11265_008_0243_1
crossref_primary_10_1118_1_3488944
crossref_primary_10_1109_TITB_2005_847500
crossref_primary_10_4028_www_scientific_net_AMM_182_183_1998
crossref_primary_10_1016_j_fss_2023_108792
crossref_primary_10_4108_eetinis_v10i1_2942
crossref_primary_10_1007_s11063_017_9672_9
crossref_primary_10_1007_s11042_024_19080_4
crossref_primary_10_1016_j_bbe_2020_07_001
crossref_primary_10_1016_j_asoc_2007_12_008
crossref_primary_10_1155_2021_6747371
crossref_primary_10_1117_1_JRS_10_046017
crossref_primary_10_1016_j_asoc_2019_105928
crossref_primary_10_31590_ejosat_719062
crossref_primary_10_1016_j_asoc_2023_110460
crossref_primary_10_1142_S0219519411003934
crossref_primary_10_1049_iet_ipr_2018_6618
crossref_primary_10_3390_rs12050803
crossref_primary_10_3390_math10214056
crossref_primary_10_1109_JSTARS_2014_2303634
crossref_primary_10_3233_JIFS_181191
crossref_primary_10_1109_LGRS_2013_2292820
crossref_primary_10_1016_j_inffus_2012_09_004
crossref_primary_10_1016_j_fss_2009_08_002
crossref_primary_10_1016_j_artmed_2004_01_012
crossref_primary_10_1016_j_engappai_2024_109135
crossref_primary_10_1088_1742_6596_1879_2_022084
crossref_primary_10_4028_www_scientific_net_JBBBE_49_63
crossref_primary_10_1190_1_3374411
crossref_primary_10_1142_S0218488517500283
crossref_primary_10_1002_ima_22176
crossref_primary_10_1109_TITS_2018_2875159
crossref_primary_10_1016_j_eswa_2019_113159
crossref_primary_10_1007_s10877_006_9040_1
crossref_primary_10_1016_j_dsp_2021_103036
crossref_primary_10_1016_j_ijar_2021_06_004
crossref_primary_10_1109_TIP_2010_2040763
crossref_primary_10_1016_j_dsp_2020_102905
crossref_primary_10_1007_s12065_011_0048_1
crossref_primary_10_1142_S0218126610005913
crossref_primary_10_1155_2009_269525
crossref_primary_10_1118_1_3301610
crossref_primary_10_1016_j_dsp_2018_02_005
crossref_primary_10_1109_TCYB_2015_2501848
crossref_primary_10_1016_j_jss_2010_07_036
crossref_primary_10_1016_j_sigpro_2018_08_010
crossref_primary_10_1016_S1053_8119_03_00006_5
crossref_primary_10_1002_ima_22166
crossref_primary_10_3182_20080706_5_KR_1001_01628
crossref_primary_10_3182_20080706_5_KR_1001_01627
crossref_primary_10_1109_JAS_2020_1003420
crossref_primary_10_1117_1_3455990
crossref_primary_10_1016_j_fss_2015_06_017
crossref_primary_10_1016_j_asoc_2019_105727
crossref_primary_10_3233_JIFS_169589
crossref_primary_10_4028_www_scientific_net_AMR_108_111_1193
crossref_primary_10_1016_j_asoc_2015_05_038
crossref_primary_10_1016_j_inffus_2021_05_017
crossref_primary_10_1080_01431161_2013_838707
crossref_primary_10_1080_01431161_2019_1629718
crossref_primary_10_1016_j_patrec_2007_04_016
crossref_primary_10_1109_TMI_2013_2274804
crossref_primary_10_1007_s11460_011_0154_y
crossref_primary_10_1016_j_neucom_2015_08_125
crossref_primary_10_1049_iet_ipr_2009_0082
crossref_primary_10_1109_TGRS_2015_2407953
crossref_primary_10_1080_24699322_2017_1389398
crossref_primary_10_1186_1687_5281_2013_63
crossref_primary_10_1016_j_asoc_2013_11_015
crossref_primary_10_1109_TFUZZ_2020_2965896
crossref_primary_10_1109_ACCESS_2020_2996603
crossref_primary_10_1556_APhysiol_97_2010_3_3
crossref_primary_10_3103_S0146411618050048
crossref_primary_10_1109_TGRS_2015_2501162
crossref_primary_10_1016_j_dsp_2024_104492
crossref_primary_10_1049_iet_ipr_2010_0592
crossref_primary_10_1016_j_sigpro_2010_10_001
crossref_primary_10_1016_j_ins_2021_11_056
crossref_primary_10_1016_j_cmpb_2019_03_003
crossref_primary_10_1117_1_JEI_34_2_023020
crossref_primary_10_1016_j_asoc_2017_07_001
crossref_primary_10_1016_j_ics_2004_03_291
crossref_primary_10_1109_TCYB_2021_3099503
crossref_primary_10_1016_j_eswa_2018_10_023
crossref_primary_10_1117_1_JMI_4_4_044001
crossref_primary_10_26634_jip_2_3_3604
crossref_primary_10_1016_j_patcog_2007_02_006
crossref_primary_10_3390_s19153285
crossref_primary_10_1016_j_patcog_2007_02_005
crossref_primary_10_1080_23311916_2018_1475032
crossref_primary_10_1155_2016_6238295
crossref_primary_10_1155_2007_25182
crossref_primary_10_1016_j_artmed_2019_101769
crossref_primary_10_1088_2057_1976_ad555b
crossref_primary_10_1007_s11063_017_9763_7
crossref_primary_10_1142_S1469026819500184
crossref_primary_10_1016_j_acra_2005_08_035
crossref_primary_10_1177_1729881416673302
crossref_primary_10_1038_srep00826
crossref_primary_10_1088_1742_6596_2303_1_012063
crossref_primary_10_3182_20090812_3_DK_2006_0089
crossref_primary_10_1016_j_neucom_2008_04_038
crossref_primary_10_1007_s12524_013_0296_x
crossref_primary_10_1007_s11042_023_14861_9
crossref_primary_10_1109_ACCESS_2022_3145157
crossref_primary_10_3390_rs14153604
crossref_primary_10_1109_TIM_2023_3293887
crossref_primary_10_1007_s11760_022_02144_z
crossref_primary_10_3390_app9153065
crossref_primary_10_1007_s10044_019_00806_2
crossref_primary_10_1007_s11042_023_14512_z
crossref_primary_10_1016_j_neuroimage_2008_09_059
crossref_primary_10_3390_app9071332
crossref_primary_10_1080_13682199_2023_2256504
crossref_primary_10_1002_ima_22128
crossref_primary_10_3390_app12157385
crossref_primary_10_1007_s00138_014_0606_5
crossref_primary_10_1016_j_compbiomed_2007_09_003
crossref_primary_10_3233_JIFS_179934
crossref_primary_10_1007_s11071_011_0088_1
crossref_primary_10_1016_j_asoc_2020_106171
crossref_primary_10_1007_s11704_010_0393_8
crossref_primary_10_1007_s12065_019_00266_x
crossref_primary_10_1109_TNNLS_2012_2228227
crossref_primary_10_1049_iet_ipr_2017_0399
crossref_primary_10_1016_j_patcog_2019_106997
crossref_primary_10_1007_s10334_016_0549_0
crossref_primary_10_1016_j_compchemeng_2021_107226
crossref_primary_10_1016_j_dsp_2016_01_010
crossref_primary_10_1016_j_cmpb_2021_106597
crossref_primary_10_1016_j_rse_2015_10_005
crossref_primary_10_1155_2014_145780
crossref_primary_10_1593_tlo_13835
crossref_primary_10_3390_s19102385
crossref_primary_10_1016_j_ijleo_2022_169039
crossref_primary_10_1002_mrm_26986
crossref_primary_10_1109_ACCESS_2019_2897597
crossref_primary_10_1016_j_asoc_2012_05_026
crossref_primary_10_1016_j_cie_2019_03_012
crossref_primary_10_4028_www_scientific_net_AMR_760_762_1590
crossref_primary_10_1177_0161734615615838
crossref_primary_10_1142_S0218213014600045
crossref_primary_10_1016_j_asoc_2021_107778
crossref_primary_10_1016_j_dsp_2013_07_005
crossref_primary_10_1109_TNNLS_2018_2847309
crossref_primary_10_1007_s00034_010_9207_3
crossref_primary_10_1007_s13721_025_00504_6
crossref_primary_10_1016_j_jvcir_2016_10_013
crossref_primary_10_1002_ima_22582
crossref_primary_10_1109_LGRS_2021_3070984
crossref_primary_10_1016_j_bspc_2024_105996
crossref_primary_10_1016_j_asoc_2017_04_023
crossref_primary_10_1109_TFUZZ_2006_890673
crossref_primary_10_3390_math7030221
crossref_primary_10_1002_ima_22104
crossref_primary_10_1016_j_eswa_2022_118751
crossref_primary_10_1109_JOE_2018_2863961
crossref_primary_10_1049_iet_ipr_2020_0383
crossref_primary_10_1016_j_asoc_2015_03_029
crossref_primary_10_1007_s00500_017_2608_5
crossref_primary_10_1007_s11277_021_09031_9
crossref_primary_10_1016_j_asoc_2017_05_055
crossref_primary_10_1016_j_asoc_2015_01_039
crossref_primary_10_1109_JSTARS_2016_2516014
crossref_primary_10_1109_TFUZZ_2024_3364970
crossref_primary_10_1007_s40846_015_0096_6
crossref_primary_10_3390_rs16173209
crossref_primary_10_1016_j_bspc_2010_08_004
crossref_primary_10_1002_mrm_26524
crossref_primary_10_1080_00207454_2020_1750390
crossref_primary_10_1007_s11517_019_02050_6
crossref_primary_10_1016_j_eswa_2022_117019
crossref_primary_10_1007_s00500_024_10338_5
crossref_primary_10_1007_s10278_006_1043_4
crossref_primary_10_1007_s13042_024_02419_6
crossref_primary_10_1016_j_asoc_2018_12_005
crossref_primary_10_1016_j_cviu_2023_103765
crossref_primary_10_1016_j_engappai_2023_107267
crossref_primary_10_1002_ima_22321
crossref_primary_10_1155_2022_5906877
crossref_primary_10_26634_jse_7_1_1956
crossref_primary_10_1080_21681163_2016_1244017
crossref_primary_10_1109_TCYB_2020_2994235
crossref_primary_10_1016_j_displa_2021_102106
crossref_primary_10_1002_mrm_25664
crossref_primary_10_1016_j_bbe_2021_03_008
crossref_primary_10_1016_j_eswa_2023_119967
crossref_primary_10_1109_TVCG_2017_2745258
crossref_primary_10_1016_j_cviu_2012_06_002
crossref_primary_10_1186_1746_1596_6_103
crossref_primary_10_1080_17445760903546618
crossref_primary_10_1016_j_eswa_2020_113989
crossref_primary_10_1016_j_neuroimage_2006_03_020
crossref_primary_10_1080_01431161_2019_1707898
crossref_primary_10_1155_2013_930301
crossref_primary_10_1118_1_4945275
crossref_primary_10_1007_s11042_018_6154_7
crossref_primary_10_1002_mrm_26306
crossref_primary_10_1002_mp_14903
crossref_primary_10_2174_1573405613666171123160609
crossref_primary_10_1186_s13638_019_1338_z
crossref_primary_10_3390_sym16101370
crossref_primary_10_1016_j_image_2019_05_006
crossref_primary_10_1109_TIP_2020_2990346
crossref_primary_10_1016_j_ijleo_2018_09_156
crossref_primary_10_1007_s10854_019_00918_9
crossref_primary_10_1016_j_patcog_2022_108686
crossref_primary_10_1109_TIP_2010_2095023
crossref_primary_10_1007_s11760_020_01721_4
crossref_primary_10_1007_s11760_024_03705_0
crossref_primary_10_1155_2022_8624617
crossref_primary_10_1007_s10489_021_02937_8
crossref_primary_10_1007_s40031_018_0314_z
crossref_primary_10_1117_1_JRS_18_024501
crossref_primary_10_3233_JIFS_200197
crossref_primary_10_1016_j_jfranklin_2022_11_017
crossref_primary_10_1002_int_22339
crossref_primary_10_1016_j_asoc_2016_10_030
Cites_doi 10.1006/gmip.1996.0021
10.1002/mrm.1910320117
10.1016/0895-6111(94)90058-2
10.1259/0007-1285-60-709-83
10.1007/3-540-63046-5_33
10.1109/42.232267
10.1118/1.597000
10.1109/42.251128
10.1007/BFb0046948
10.1080/01969727308546046
10.1007/BFb0046950
10.1109/CVPR.1999.786947
10.1109/42.370400
10.1109/TPAMI.1980.4766964
10.1016/0730-725X(93)90023-7
10.1097/00004728-199809000-00030
10.1109/BIA.1998.692393
10.1109/icassp.1999.757579
10.1109/42.700729
10.1109/42.668698
10.1109/42.491417
10.1109/72.159057
10.1109/42.802752
10.1118/1.595967
10.1109/42.585758
10.1016/S0167-8655(99)00116-6
10.1109/42.511747
ContentType Journal Article
Copyright 2002 INIST-CNRS
Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2002
Copyright_xml – notice: 2002 INIST-CNRS
– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2002
DBID RIA
RIE
AAYXX
CITATION
IQODW
CGR
CUY
CVF
ECM
EIF
NPM
7QF
7QO
7QQ
7SC
7SE
7SP
7SR
7TA
7TB
7U5
8BQ
8FD
F28
FR3
H8D
JG9
JQ2
KR7
L7M
L~C
L~D
NAPCQ
P64
7X8
DOI 10.1109/42.996338
DatabaseName IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
Pascal-Francis
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
Aluminium Industry Abstracts
Biotechnology Research Abstracts
Ceramic Abstracts
Computer and Information Systems Abstracts
Corrosion Abstracts
Electronics & Communications Abstracts
Engineered Materials Abstracts
Materials Business File
Mechanical & Transportation Engineering Abstracts
Solid State and Superconductivity Abstracts
METADEX
Technology Research Database
ANTE: Abstracts in New Technology & Engineering
Engineering Research Database
Aerospace Database
Materials Research Database
ProQuest Computer Science Collection
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
Nursing & Allied Health Premium
Biotechnology and BioEngineering Abstracts
MEDLINE - Academic
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Materials Research Database
Civil Engineering Abstracts
Aluminium Industry Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Mechanical & Transportation Engineering Abstracts
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Ceramic Abstracts
Materials Business File
METADEX
Biotechnology and BioEngineering Abstracts
Computer and Information Systems Abstracts Professional
Aerospace Database
Nursing & Allied Health Premium
Engineered Materials Abstracts
Biotechnology Research Abstracts
Solid State and Superconductivity Abstracts
Engineering Research Database
Corrosion Abstracts
Advanced Technologies Database with Aerospace
ANTE: Abstracts in New Technology & Engineering
MEDLINE - Academic
DatabaseTitleList MEDLINE
Technology Research Database
Technology Research Database

MEDLINE - Academic
Materials Research Database
Engineering Research Database

Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
– sequence: 3
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
Engineering
EISSN 1558-254X
EndPage 199
ExternalDocumentID 615568
2430955551
11989844
13622031
10_1109_42_996338
996338
Genre orig-research
Research Support, U.S. Gov't, P.H.S
Comparative Study
Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: NCI NIH HHS
  grantid: CA79178-01
GroupedDBID ---
-DZ
-~X
.GJ
0R~
29I
4.4
53G
5GY
5RE
5VS
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACGFS
ACIWK
ACNCT
ACPRK
AENEX
AETIX
AFRAH
AGQYO
AGSQL
AHBIQ
AI.
AIBXA
AKJIK
AKQYR
ALLEH
ALMA_UNASSIGNED_HOLDINGS
ASUFR
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
F5P
HZ~
H~9
IBMZZ
ICLAB
IFIPE
IFJZH
IPLJI
JAVBF
LAI
M43
MS~
O9-
OCL
P2P
PQQKQ
RIA
RIE
RNS
RXW
TAE
TN5
VH1
AAYXX
CITATION
IQODW
RIG
AAYOK
CGR
CUY
CVF
ECM
EIF
NPM
PKN
Z5M
7QF
7QO
7QQ
7SC
7SE
7SP
7SR
7TA
7TB
7U5
8BQ
8FD
F28
FR3
H8D
JG9
JQ2
KR7
L7M
L~C
L~D
NAPCQ
P64
7X8
ID FETCH-LOGICAL-c550t-e5e7dfb021c3412ceb41160d3dc44a80c7db4978e59443bb7f92959f5f4ca40e3
IEDL.DBID RIE
ISSN 0278-0062
IngestDate Sun Sep 28 04:23:50 EDT 2025
Sat Sep 27 18:43:23 EDT 2025
Sun Sep 28 14:06:43 EDT 2025
Sun Sep 28 04:23:51 EDT 2025
Sun Sep 28 03:09:58 EDT 2025
Mon Jun 30 03:54:42 EDT 2025
Wed Feb 19 01:34:55 EST 2025
Mon Jul 21 09:17:59 EDT 2025
Wed Oct 01 06:32:30 EDT 2025
Thu Apr 24 23:01:19 EDT 2025
Tue Aug 26 20:52:25 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 3
Keywords Human
Biomedical data processing
Evaluation
Heterogeneity
Fuzzy algorithm
Segmentation
Bias
Signal processing
Mammary gland
Error correction
Signal analysis
Nuclear magnetic resonance imaging
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
CC BY 4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c550t-e5e7dfb021c3412ceb41160d3dc44a80c7db4978e59443bb7f92959f5f4ca40e3
Notes ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
PMID 11989844
PQID 884481278
PQPubID 23500
PageCount 7
ParticipantIDs proquest_miscellaneous_888098790
proquest_miscellaneous_28378822
crossref_citationtrail_10_1109_42_996338
pascalfrancis_primary_13622031
pubmed_primary_11989844
crossref_primary_10_1109_42_996338
proquest_journals_884481278
ieee_primary_996338
proquest_miscellaneous_21497162
proquest_miscellaneous_71654563
proquest_miscellaneous_1671434761
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2002-03-01
PublicationDateYYYYMMDD 2002-03-01
PublicationDate_xml – month: 03
  year: 2002
  text: 2002-03-01
  day: 01
PublicationDecade 2000
PublicationPlace New York, NY
PublicationPlace_xml – name: New York, NY
– name: United States
– name: New York
PublicationTitle IEEE transactions on medical imaging
PublicationTitleAbbrev TMI
PublicationTitleAlternate IEEE Trans Med Imaging
PublicationYear 2002
Publisher IEEE
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: Institute of Electrical and Electronics Engineers
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref12
ref15
ref14
ref11
ref10
ref2
ref1
ref17
ref19
ref18
Bezdek (ref16) 1991
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref22
  doi: 10.1006/gmip.1996.0021
– ident: ref4
  doi: 10.1002/mrm.1910320117
– ident: ref25
  doi: 10.1016/0895-6111(94)90058-2
– ident: ref1
  doi: 10.1259/0007-1285-60-709-83
– ident: ref19
  doi: 10.1007/3-540-63046-5_33
– ident: ref10
  doi: 10.1109/42.232267
– ident: ref24
  doi: 10.1118/1.597000
– ident: ref12
  doi: 10.1109/42.251128
– ident: ref14
  doi: 10.1007/BFb0046948
– ident: ref17
  doi: 10.1080/01969727308546046
– ident: ref13
  doi: 10.1007/BFb0046950
– ident: ref21
  doi: 10.1109/CVPR.1999.786947
– ident: ref9
  doi: 10.1109/42.370400
– ident: ref18
  doi: 10.1109/TPAMI.1980.4766964
– ident: ref3
  doi: 10.1016/0730-725X(93)90023-7
– ident: ref28
  doi: 10.1097/00004728-199809000-00030
– ident: ref8
  doi: 10.1109/BIA.1998.692393
– ident: ref27
  doi: 10.1109/icassp.1999.757579
– ident: ref11
  doi: 10.1109/42.700729
– ident: ref7
  doi: 10.1109/42.668698
– ident: ref6
  doi: 10.1109/42.491417
– ident: ref26
  doi: 10.1109/72.159057
– ident: ref20
  doi: 10.1109/42.802752
– ident: ref2
  doi: 10.1118/1.595967
– ident: ref15
  doi: 10.1109/42.585758
– volume-title: Fuzzy Models for Pattern Recognition
  year: 1991
  ident: ref16
– ident: ref23
  doi: 10.1016/S0167-8655(99)00116-6
– ident: ref5
  doi: 10.1109/42.511747
SSID ssj0014509
Score 2.3605363
Snippet We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI...
In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using...
Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
SourceID proquest
pubmed
pascalfrancis
crossref
ieee
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 193
SubjectTerms Algorithms
Biological and medical sciences
Brain - anatomy & histology
Brain Neoplasms - diagnosis
Classification algorithms
Cluster Analysis
Coils
Computerized, statistical medical data processing and models in biomedicine
Data acquisition
Efficiency
Fuzzy
Fuzzy Logic
Fuzzy set theory
Fuzzy sets
General aspects. Methods
Humans
Image Enhancement - methods
Image segmentation
Imaging phantoms
Inhomogeneities
Investigative techniques, diagnostic techniques (general aspects)
Labeling
Magnetic resonance imaging
Magnetic Resonance Imaging - instrumentation
Magnetic Resonance Imaging - methods
Marking
Medical sciences
Miscellaneous. Technology
Nonuniform electric fields
Phantoms, Imaging
Polynomials
Radio frequency
Radiodiagnosis. Nmr imagery. Nmr spectrometry
Segmentation
Sensitivity and Specificity
Spatial variables measurement
Stochastic Processes
Studies
Title A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
URI https://ieeexplore.ieee.org/document/996338
https://www.ncbi.nlm.nih.gov/pubmed/11989844
https://www.proquest.com/docview/884481278
https://www.proquest.com/docview/1671434761
https://www.proquest.com/docview/21497162
https://www.proquest.com/docview/28378822
https://www.proquest.com/docview/71654563
https://www.proquest.com/docview/888098790
Volume 21
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 1558-254X
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0014509
  issn: 0278-0062
  databaseCode: RIE
  dateStart: 19820101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELZoDwgOPJbXUigGceCSrZOM7fhYIaqCVA6ISuUUxY-0K7pJ1SSH7q9nbGe3FHUrblEyjpTxYz7H33xDyEcrQYuKQaKB5wlYI5JCVyqxytpUaeFV1zzb4rs4PIZvJ_xk1NkOuTDOuUA-czN_Gc7ybWsG_6tsD7E57qi2yJaUKqZqrQ8MgEc2R-YFY5nIRhGhlKk9yGaxoRcI9RShAuBGFAplVTwpsurQL3UsaLEZcYbIc_A4pnR3QbDQE05-z4Zez8zyHznH__yoJ-TRiEDpfhwyT8k910zIw790CSfk_tF44v6M_Nqni9bOa0SqtB6WyytqkoXD-Ear89P2ct6fLSjiXqrnVUcDHY563Y6YEEmrxtLOnS7GBKeGtjU9-vGVel7qc3J88OXn58NkLMeQGNzG9InjTtpaIygwGPoy4zSkqWA2twagKpiRVvt6dY4rgFxrWSP04qrmNZgKmMtfkO2mbdwrQl0akILVDNGMYqA5LgWQmVwbA4hppuTTqntKM2qV-5IZ52XYszBVQlZGx03Jh7XpRRTouM1o4h2_Nljd3b3R5dftMapnuNpNyc5qDJTj1O7KAocPoiKJzd-vn-Kc9ActVePaoStT4avKgxT4hncbbDLcmnr1rjssgtZ_doeF9KloXKC76AaLApdnVUjFpuRlHMPXHzlOhde3-maHPAh1bwLb7g3Z7i8H9xbhV693w8T7AwqFKko
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELagSDwOPJZSlkJrEAcu2TrOOImPFaLaQrcH1ErlFMWPlBXdpGqyB_bXM7azW4q6FbcoGUfK-DGf42--IeSjyUClJYNIgUgiMDqNclXKyEhjYqlSp7rm2BbH6fgUvp6Js15n2-fCWGs9-cyO3KU_yzeNnrtfZXuIzXFHdZ88EAAZD8laqyMDEIHPwZ1kLEt5LyMUM7kHfBSaOolQRxLKAW7EIV9YxdEiyxY9U4WSFusxp489B89CUnfrJQsd5eTXaN6pkV78I-j4n5_1nDztMSjdD4PmBbln6wF58pcy4YA8nPRn7i_Jj306a8y0QqxKq_li8ZvqaGYxwtHy4ry5mnY_ZxSRL1XTsqWeEEedckdIiaRlbWhrz2d9ilNNm4pOvh9Sx0zdJKcHX04-j6O-IEOkcSPTRVbYzFQKYYHG4Me1VRDHKTOJ0QBlznRmlKtYZ4UESJTKKgRfQlaiAl0Cs8krslE3tX1NqI09VjCKIZ6RDJTAxQC4TpTWgKhmSD4tu6fQvVq5K5pxUfhdC5MF8CI4bkg-rEwvg0THbUYD5_iVwfLuzo0uv26PcZ3jejck28sxUPSTuy1yHD6IizJs_n71FGelO2opa9vM2yJOXV15yFJ8w-4aG46bU6ffdYeFV_vnd1hkLhlNpOguusYixwVa5plkQ7IVxvD1R_ZT4c2tvtklj8Ynk6Pi6PD42zZ57KvgeO7dW7LRXc3tOwRjndrxk_APuIYtlQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+modified+fuzzy+C-means+algorithm+for+bias+field+estimation+and+segmentation+of+MRI+data&rft.jtitle=IEEE+transactions+on+medical+imaging&rft.au=Ahmed%2C+Mohamed+N&rft.au=Yamany%2C+Sameh+M&rft.au=Mohamed%2C+Nevin&rft.au=Farag%2C+Aly+A&rft.date=2002-03-01&rft.issn=0278-0062&rft.volume=21&rft.issue=3&rft.spage=193&rft_id=info:doi/10.1109%2F42.996338&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-0062&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-0062&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-0062&client=summon