Robust Detection and Segmentation for Diagnosis of Vertebral Diseases Using Routine MR Images
The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, li...
Saved in:
| Published in: | Computer graphics forum Vol. 33; no. 6; pp. 190 - 204 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Blackwell Publishing Ltd
01.09.2014
|
| Subjects: | |
| ISSN: | 0167-7055, 1467-8659 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X‐ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. Our approach consists of three major steps. First, vertebral centres are detected using a Viola–Jones like method, and then the vertebrae are segmented in a parallel manner, and finally, geometric diagnostic features are deduced in order to diagnose the three diseases. Our method was evaluated on 26 lumbar datasets containing 234 reference vertebrae. Vertebra detection has 7.1% false negatives and 1.3% false positives. The average Dice coefficient to manual reference is 79.3% and mean distance error is 1.76 mm. No severe case of the three illnesses was missed, and false alarms occurred rarely—0% for scoliosis, 3.9% for spondylolisthesis and 2.6% for vertebral fractures. The main advantages of our method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction.
The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, are part of the daily clinical routine. Very frequently, MRI data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X‐ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. |
|---|---|
| AbstractList | The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X‐ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. Our approach consists of three major steps. First, vertebral centres are detected using a Viola–Jones like method, and then the vertebrae are segmented in a parallel manner, and finally, geometric diagnostic features are deduced in order to diagnose the three diseases. Our method was evaluated on 26 lumbar datasets containing 234 reference vertebrae. Vertebra detection has 7.1% false negatives and 1.3% false positives. The average Dice coefficient to manual reference is 79.3% and mean distance error is 1.76 mm. No severe case of the three illnesses was missed, and false alarms occurred rarely—0% for scoliosis, 3.9% for spondylolisthesis and 2.6% for vertebral fractures. The main advantages of our method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction. The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X-ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. Our approach consists of three major steps. First, vertebral centres are detected using a Viola-Jones like method, and then the vertebrae are segmented in a parallel manner, and finally, geometric diagnostic features are deduced in order to diagnose the three diseases. Our method was evaluated on 26 lumbar datasets containing 234 reference vertebrae. Vertebra detection has 7.1% false negatives and 1.3% false positives. The average Dice coefficient to manual reference is 79.3% and mean distance error is 1.76 mm. No severe case of the three illnesses was missed, and false alarms occurred rarely--0% for scoliosis, 3.9% for spondylolisthesis and 2.6% for vertebral fractures. The main advantages of our method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction. [PUBLICATION ABSTRACT] The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very frequently, magnetic resonance image data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X‐ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. Our approach consists of three major steps. First, vertebral centres are detected using a Viola–Jones like method, and then the vertebrae are segmented in a parallel manner, and finally, geometric diagnostic features are deduced in order to diagnose the three diseases. Our method was evaluated on 26 lumbar datasets containing 234 reference vertebrae. Vertebra detection has 7.1% false negatives and 1.3% false positives. The average Dice coefficient to manual reference is 79.3% and mean distance error is 1.76 mm. No severe case of the three illnesses was missed, and false alarms occurred rarely—0% for scoliosis, 3.9% for spondylolisthesis and 2.6% for vertebral fractures. The main advantages of our method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction. The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, are part of the daily clinical routine. Very frequently, MRI data are used to diagnose these kinds of pathologies in order to avoid exposing patients to harmful radiation, like X‐ray. We present a method which detects and segments all acquired vertebral bodies, with minimal user intervention. This allows an automatic diagnosis to detect scoliosis, spondylolisthesis and crushed vertebrae. |
| Author | Vlasák, Aleš Zukić, Dženan Kolb, Andreas Nimsky, Christopher Hořínek, Daniel Egger, Jan |
| Author_xml | – sequence: 1 givenname: Dženan surname: Zukić fullname: Zukić, Dženan email: dzenanz@gmail.com organization: Computer Graphics and Multimedia Systems Group, University of Siegen, Germany – sequence: 2 givenname: Aleš surname: Vlasák fullname: Vlasák, Aleš email: ales.vlasak@fnmotol.cz organization: Department of Neurosurgery, University Hospital Motol, Prague, Czech Republic – sequence: 3 givenname: Jan surname: Egger fullname: Egger, Jan email: egger@med.uni-marburg.de, nimsky@med.uni-marburg.de organization: Department of Neurosurgery, University Hospital Gießen and Marburg, Marburg, Germany – sequence: 4 givenname: Daniel surname: Hořínek fullname: Hořínek, Daniel email: horinek@med.uni-marburg.de organization: International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic – sequence: 5 givenname: Christopher surname: Nimsky fullname: Nimsky, Christopher organization: Department of Neurosurgery, University Hospital Gießen and Marburg, Marburg, Germany – sequence: 6 givenname: Andreas surname: Kolb fullname: Kolb, Andreas email: andreas.kolb@uni-siegen.de organization: Computer Graphics and Multimedia Systems Group, University of Siegen, Germany |
| BookMark | eNp1kE1PGzEQhq0qSE2gh_4DSz31sOCPtZ09otAEJD6kQMupsrze8cohsantiPLvu5C0B1R8GWv0PDOad4JGIQZA6DMlx3R4J7Z3x5Txmn9AY1pLVU2laEZoTOjwV0SIj2iS84oQUispxujnMrbbXPAZFLDFx4BN6PAt9BsIxbw2XEz4zJs-xOwzjg7_gFSgTWY9tDOYDBl_zz70eBm3xQfAV0t8sTE95CN04Mw6w6d9PUR38293s_Pq8mZxMTu9rGxNKK9cx5SEmracOmZYK5kkU-E6SzrDGbOt6khbC6e6Zmqaga2dMB0zcipsYwk_RF92Yx9T_LWFXPQqblMYNmoqpBCMNbIeqJMdZVPMOYHT1u9OLMn4taZEv0Sohwj1a4SD8fWN8Zj8xqTn_7L76U9-Dc_vg3q2mP81qp3hc4Hf_wyTHrRUXAl9f73Qaim54vfn-or_Ac_KkPk |
| CitedBy_id | crossref_primary_10_1016_j_chemolab_2025_105425 crossref_primary_10_1049_iet_smt_2015_0172 crossref_primary_10_1038_s41597_024_03090_w crossref_primary_10_1007_s11548_018_1818_3 crossref_primary_10_1371_journal_pone_0248303 crossref_primary_10_1016_j_engappai_2022_105451 crossref_primary_10_1016_j_cmpb_2017_12_013 crossref_primary_10_1109_JTEHM_2017_2717982 crossref_primary_10_1016_j_compbiomed_2023_106839 crossref_primary_10_1371_journal_pone_0143327 crossref_primary_10_1007_s11548_016_1350_2 crossref_primary_10_1016_j_cmpb_2019_05_003 crossref_primary_10_1016_j_procs_2016_07_005 crossref_primary_10_1038_s41598_024_64580_w crossref_primary_10_1109_ACCESS_2021_3131216 crossref_primary_10_3390_biomechanics3020015 crossref_primary_10_1038_srep05164 crossref_primary_10_1016_j_eswa_2025_126853 crossref_primary_10_1227_NEU_0000000000001853 crossref_primary_10_1177_2050312117740984 crossref_primary_10_1016_j_media_2019_02_005 crossref_primary_10_1109_ACCESS_2021_3084949 crossref_primary_10_1007_s10586_025_05122_5 crossref_primary_10_1016_j_cmpb_2023_107337 crossref_primary_10_1016_j_mri_2024_03_021 crossref_primary_10_1016_j_cmpb_2019_105102 crossref_primary_10_1155_2018_6213264 crossref_primary_10_1038_sdata_2019_3 crossref_primary_10_3389_fcomp_2022_858874 crossref_primary_10_1002_jmri_29101 crossref_primary_10_1109_JBHI_2018_2872810 crossref_primary_10_1007_s10278_019_00272_6 crossref_primary_10_1007_s11548_021_02502_1 crossref_primary_10_1155_2023_3018320 crossref_primary_10_1155_2023_8686471 crossref_primary_10_1371_journal_pone_0093389 crossref_primary_10_20965_jaciii_2019_p0502 |
| Cites_doi | 10.1016/j.cviu.2012.11.016 10.1088/0031-9155/57/24/8357 10.1016/j.media.2009.02.004 10.1007/978-94-015-8330-5_4 10.1088/0031-9155/56/23/011 10.1007/s00586-005-1053-9 10.1088/0031-9155/55/1/015 10.1007/BF01908877 10.1109/TMI.2003.819929 10.1088/0031-9155/50/19/007 10.1148/radiol.2243011266 10.1088/0031-9155/47/19/305 10.1097/01241398-198707060-00001 10.1111/1467-8659.00236 10.1109/42.921475 10.1097/BRS.0b013e3181cdde47 10.2214/ajr.183.4.1830949 10.1016/j.jsb.2003.09.010 10.1016/j.media.2012.09.007 10.1001/archinternmed.2008.543 10.1109/34.667881 10.2106/00004623-198365060-00007 10.1016/1049-9660(91)90028-N 10.1007/978-94-011-2546-8_20 10.1109/TMI.2009.2023362 10.1109/TVCG.2006.39 10.1007/BF01622258 10.1109/TBME.2009.2019765 10.1371/journal.pone.0031064 10.1016/j.imavis.2007.07.010 |
| ContentType | Journal Article |
| Copyright | 2014 The Authors Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Copyright © 2014 The Eurographics Association and John Wiley & Sons Ltd. |
| Copyright_xml | – notice: 2014 The Authors Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. – notice: Copyright © 2014 The Eurographics Association and John Wiley & Sons Ltd. |
| DBID | BSCLL AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1111/cgf.12343 |
| DatabaseName | Istex CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1467-8659 |
| EndPage | 204 |
| ExternalDocumentID | 3443494621 10_1111_cgf_12343 CGF12343 ark_67375_WNG_7R6373WH_M |
| Genre | article Feature |
| GroupedDBID | .3N .4S .DC .GA .Y3 05W 0R~ 10A 15B 1OB 1OC 29F 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8VB 930 A03 AAESR AAEVG AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABDPE ABEML ABPVW ACAHQ ACBWZ ACCZN ACFBH ACGFS ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMLS ADNMO ADOZA ADXAS ADZMN AEFGJ AEGXH AEIGN AEIMD AEMOZ AENEX AEUYR AEYWJ AFBPY AFEBI AFFNX AFFPM AFGKR AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AHEFC AHQJS AIDQK AIDYY AIQQE AITYG AIURR AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CAG COF CS3 CWDTD D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBA EBO EBR EBS EBU EDO EJD EMK EST ESX F00 F01 F04 F5P FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K1G K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QWB R.K RDJ RIWAO RJQFR ROL RX1 SAMSI SUPJJ TH9 TN5 TUS UB1 V8K W8V W99 WBKPD WIH WIK WOHZO WQJ WXSBR WYISQ WZISG XG1 ZL0 ZZTAW ~IA ~IF ~WT AAYXX CITATION O8X 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c4013-fd276e41b31f2a2b626085fdc0da322cb7d0b45f7d98a92764f5ad2a685c9c03 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 63 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000342802200014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0167-7055 |
| IngestDate | Wed Aug 13 10:53:23 EDT 2025 Sat Nov 29 03:41:10 EST 2025 Tue Nov 18 21:18:12 EST 2025 Sun Sep 21 06:19:28 EDT 2025 Sun Sep 21 06:19:48 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4013-fd276e41b31f2a2b626085fdc0da322cb7d0b45f7d98a92764f5ad2a685c9c03 |
| Notes | ArticleID:CGF12343 ark:/67375/WNG-7R6373WH-M istex:4943CB4FF9A82A48285A459028C67661F80D749E SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
| PQID | 1565522964 |
| PQPubID | 30877 |
| PageCount | 15 |
| ParticipantIDs | proquest_journals_1565522964 crossref_citationtrail_10_1111_cgf_12343 crossref_primary_10_1111_cgf_12343 wiley_primary_10_1111_cgf_12343_CGF12343 istex_primary_ark_67375_WNG_7R6373WH_M |
| PublicationCentury | 2000 |
| PublicationDate | September 2014 |
| PublicationDateYYYYMMDD | 2014-09-01 |
| PublicationDate_xml | – month: 09 year: 2014 text: September 2014 |
| PublicationDecade | 2010 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Computer graphics forum |
| PublicationTitleAlternate | Computer Graphics Forum |
| PublicationYear | 2014 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | [HCLN09] Huang S.-H., Chu Y.-H., Lai S.-H., Novak C.: Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE Transactions on Medical Imaging 28, 10 (2009), 1595-1605. [ML13] Ma J., Lu L.: Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Computer Vision and Image Understanding 117, 9 (2013), 1072-1083. [KOE*09] Klinder T., Ostermann J., Ehm M., Franz A., Kneser R., Lorenz C.: Automated model-based vertebra detection, identification, and segmentation in {CT} images. Medical Image Analysis 13, 3 (2009), 471-482. [Aeb05] Aebi M.: The adult scoliosis. European Spine Journal 14, 10 (2005), 925-948. [CRS96] Cignoni P., Rocchini C., Scopigno R.: Metro: measuring error on simplified surfaces. Computer Graphics Forum 17, 2 (1998), 167-174. [WLJG95] Wu C., Li J., Jergas M., Genant H.: Comparison of semiquantitative and quantitative techniques for the assessment of prevalent and incident vertebral fractures. Osteoporosis International 5, 5 (1995), 354-370. [EBK*11] Egger J., Bauer M. H. A., Kuhnt D., Carl B., Kappus C., Freisleben B., Nimsky C.: Evaluation of a novel approach for automatic volume determination of glioblastomas based on several manual expert segmentations. CoRR abs/1103.1474 (2011). [FHA*09] Freburger J., Holmes G., Agans R., Jackman A., Darter J., Wallace A., Castel L., Kalsbeek W., Carey T.: The rising prevalence of chronic low back pain. Archives of Internal Medicine 169, 3 (2009), 251-258. [FL03] Fernández J.-J., Li S.: An improved algorithm for anisotropic nonlinear diffusion for denoising cryo-tomograms. Journal of Structural Biology 144, 1-2 (2003), 152-161. [NFE*12] Neubert A., Fripp J., Engstrom C., Schwarz R., Lauer L., Salvado O., Crozier S.: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Physics in Medicine and Biology 9 (2012), 8357-8376. [Cob48] Cobb J.: Outline for the study of scoliosis. Instructional Course Lectures 5 (1948), 261-275. [EKD*12] Egger J., Kapur T., Dukatz T., Kolodziej M., Zukić Dž., Freisleben B., Nimsky C.: Square-cut: A segmentation algorithm on the basis of a rectangle shape. PLoS ONE 7, 2 (2012), e31064. [ŠBSG06] Šereda P., Bartrolì A., Serlie I., Gerritsen F.: Visualization of boundaries in volumetric data sets using LH histograms. IEEE Transactions on Visualization and Computer Graphics 12, 2 (2006), 208-218. [HPE*08] He L., Peng Z., Everding B., Wang X., Han C. Y., Weiss K. L., Wee W. G.: A comparative study of deformable contour methods on medical image segmentation. Image and Vision Computing 26, 2 (2008), 141-163. [LRDG04] Lenchik L., Rogers L. F., Delmas P. D., Genant H. K.: Diagnosis of osteoporotic vertebral fractures: Importance of recognition and description by radiologists. American Journal of Roentgenology 183, 4 (2004), 949-958. [TF88] Terzopoulos D., Fleischer K.: Deformable models. The Visual Computer 4, 6 (1988), 306-331. [VLP05] Vrtovec T., Likar B., Pernuš F.: Automated curved planar reformation of 3D spine images. Physics in Medicine and Biology 50, 19 (2005), 4527. [ŠLPV10] Štern D., Likar B., Pernuš F., Vrtovec T.: Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine. Physics in Medicine and Biology 55, 1 (2010), 247-264. [KWZ*13] Kelm B. M., Wels M., Zhou S. K., Seifert S., Suehling M., Zheng Y., Comaniciu D.: Spine detection in CT and MR using iterated marginal space learning. Medical Image Analysis 17, 8 (2013), 1283-1292. [WW83] Wiltse L. L., Winter R. B.: Terminology and measurement of spondylolisthesis. The Journal of Bone and Joint Surgery 65, 6 (July 1983), 768-772. [HM02] Hoad C. L., Martel A. L.: Segmentation of MR images for computer-assisted surgery of the lumbar spine. Physics in Medicine and Biology 47, 19 (2002), 3503. [Coh91] Cohen L. D.: On active contour models and balloons. CVGIP: Image Understanding 53, 2 (1991), 211-218. [SMB11] Schulze F., Major D., Bühler K.: Fast and memory efficient feature detection using multiresolution probabilistic boosting trees. In Proceedings of the 19th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) 19, 1 (2011), 33-40. [DLSH02] Davatzikos C., Liu D., Shen D., Herskovits E. H.: Spatial normalization of spine MR images for statistical correlation of lesions with clinical symptoms. Radiology 224, 3 (2002), 919-926. [CGNT09] Couprie C., Grady L. J., Najman L., Talbot H.: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest. In Proceedings of ICCV (2009), pp. 731-738. [RGMB10] Richards P. J., George J., Metelko M., Brown M.: Spine computed tomography doses and cancer induction. Spine 35, 4 (2010), 430-433. [SHD01] Shen D., Herskovits E. H., Davatzikos C.: An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures. IEEE Transactions on Medical Imaging 20, 4 (2001), 257-270. [NFS*11] Neubert A., Fripp J., Shen K., Salvado O., Schwarz R., Lauer L., Engstrom C., Crozier S.: Automated 3D segmentation of vertebral bodies and intervertebral discs from MRI. In Proceedings of IEEE DICTA (2011), pp. 19-24. [ŠLPV11] Štern D., Likar B., Pernuš F., Vrtovec T.: Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Physics in Medicine and Biology 56, 23 (2011), 7505-7522. [KHDM98] Kittler J., Hatef M., Duin R., Matas J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 3 (1998), 226-239. [CGBM04] Carballido-Gamio J., Belongie S., Majumdar S.: Normalized cuts in 3-D for spinal MRI segmentation. IEEE Transactions on Medical Imaging 23, 1 (2004), 36-44. [Sar87] Saraste H.: Long-term clinical and radiological follow-up of spondylolysis and spondylolisthesis. Journal of Pediatric Orthopaedics 7, 6 (1987), 631-638. [ZSS96] Zorin D., Schröder P., Sweldens W.: Interpolating subdivision for meshes with arbitrary topology. In Proceedings of ACM SIGGRAPH (1996), pp. 189-192. [MCP*09] Michopoulou S., Costaridou L., Panagiotopoulos E., Speller R., Panayiotakis G., Todd-Pokropek A.: Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Transactions on Biomedical Engineering 56, 9 (2009), 2225-2231. 2010; 55 2010; 35 2012 2006; 12 2011 1991; 53 2004; 23 2004; 183 1987; 7 2009 1996 1994 2011; 56 1992 2011; 19 1998; 20 1995; 5 2001; 20 2009; 28 2009; 56 2002; 47 1988; 4 1998; 17 2009; 13 2013; 17 2001 2002; 224 2013; 117 2008; 26 1983; 65 2013 2005; 50 2012; 7 1948 2009; 169 2003; 144 2005; 14 e_1_2_10_23_1 e_1_2_10_21_1 e_1_2_10_22_1 e_1_2_10_20_1 Viola P. (e_1_2_10_36_1) 2001 Ayed I. B. (e_1_2_10_3_1) 2012 Neubert A. (e_1_2_10_24_1) 2011 Egger J. (e_1_2_10_10_1) 2011 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_19_1 e_1_2_10_16_1 e_1_2_10_39_1 e_1_2_10_17_1 e_1_2_10_38_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_37_1 e_1_2_10_7_1 e_1_2_10_15_1 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_31_1 e_1_2_10_30_1 Zorin D. (e_1_2_10_40_1) 1996 Zukić Dž. (e_1_2_10_41_1) 2012 Couprie C. (e_1_2_10_5_1) 2009 Schulze F. (e_1_2_10_33_1) 2011; 19 e_1_2_10_29_1 Rak M. (e_1_2_10_26_1) 2013 e_1_2_10_27_1 e_1_2_10_28_1 Cobb J. (e_1_2_10_6_1) 1948 e_1_2_10_25_1 |
| References_xml | – reference: [FL03] Fernández J.-J., Li S.: An improved algorithm for anisotropic nonlinear diffusion for denoising cryo-tomograms. Journal of Structural Biology 144, 1-2 (2003), 152-161. – reference: [ZSS96] Zorin D., Schröder P., Sweldens W.: Interpolating subdivision for meshes with arbitrary topology. In Proceedings of ACM SIGGRAPH (1996), pp. 189-192. – reference: [FHA*09] Freburger J., Holmes G., Agans R., Jackman A., Darter J., Wallace A., Castel L., Kalsbeek W., Carey T.: The rising prevalence of chronic low back pain. Archives of Internal Medicine 169, 3 (2009), 251-258. – reference: [CRS96] Cignoni P., Rocchini C., Scopigno R.: Metro: measuring error on simplified surfaces. Computer Graphics Forum 17, 2 (1998), 167-174. – reference: [NFS*11] Neubert A., Fripp J., Shen K., Salvado O., Schwarz R., Lauer L., Engstrom C., Crozier S.: Automated 3D segmentation of vertebral bodies and intervertebral discs from MRI. In Proceedings of IEEE DICTA (2011), pp. 19-24. – reference: [WLJG95] Wu C., Li J., Jergas M., Genant H.: Comparison of semiquantitative and quantitative techniques for the assessment of prevalent and incident vertebral fractures. Osteoporosis International 5, 5 (1995), 354-370. – reference: [KWZ*13] Kelm B. M., Wels M., Zhou S. K., Seifert S., Suehling M., Zheng Y., Comaniciu D.: Spine detection in CT and MR using iterated marginal space learning. Medical Image Analysis 17, 8 (2013), 1283-1292. – reference: [ŠBSG06] Šereda P., Bartrolì A., Serlie I., Gerritsen F.: Visualization of boundaries in volumetric data sets using LH histograms. IEEE Transactions on Visualization and Computer Graphics 12, 2 (2006), 208-218. – reference: [RGMB10] Richards P. J., George J., Metelko M., Brown M.: Spine computed tomography doses and cancer induction. Spine 35, 4 (2010), 430-433. – reference: [SMB11] Schulze F., Major D., Bühler K.: Fast and memory efficient feature detection using multiresolution probabilistic boosting trees. In Proceedings of the 19th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) 19, 1 (2011), 33-40. – reference: [TF88] Terzopoulos D., Fleischer K.: Deformable models. The Visual Computer 4, 6 (1988), 306-331. – reference: [LRDG04] Lenchik L., Rogers L. F., Delmas P. D., Genant H. K.: Diagnosis of osteoporotic vertebral fractures: Importance of recognition and description by radiologists. American Journal of Roentgenology 183, 4 (2004), 949-958. – reference: [Coh91] Cohen L. D.: On active contour models and balloons. CVGIP: Image Understanding 53, 2 (1991), 211-218. – reference: [Cob48] Cobb J.: Outline for the study of scoliosis. Instructional Course Lectures 5 (1948), 261-275. – reference: [Aeb05] Aebi M.: The adult scoliosis. European Spine Journal 14, 10 (2005), 925-948. – reference: [HPE*08] He L., Peng Z., Everding B., Wang X., Han C. Y., Weiss K. L., Wee W. G.: A comparative study of deformable contour methods on medical image segmentation. Image and Vision Computing 26, 2 (2008), 141-163. – reference: [EBK*11] Egger J., Bauer M. H. A., Kuhnt D., Carl B., Kappus C., Freisleben B., Nimsky C.: Evaluation of a novel approach for automatic volume determination of glioblastomas based on several manual expert segmentations. CoRR abs/1103.1474 (2011). – reference: [KOE*09] Klinder T., Ostermann J., Ehm M., Franz A., Kneser R., Lorenz C.: Automated model-based vertebra detection, identification, and segmentation in {CT} images. Medical Image Analysis 13, 3 (2009), 471-482. – reference: [ML13] Ma J., Lu L.: Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Computer Vision and Image Understanding 117, 9 (2013), 1072-1083. – reference: [WW83] Wiltse L. L., Winter R. B.: Terminology and measurement of spondylolisthesis. The Journal of Bone and Joint Surgery 65, 6 (July 1983), 768-772. – reference: [CGBM04] Carballido-Gamio J., Belongie S., Majumdar S.: Normalized cuts in 3-D for spinal MRI segmentation. IEEE Transactions on Medical Imaging 23, 1 (2004), 36-44. – reference: [SHD01] Shen D., Herskovits E. H., Davatzikos C.: An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures. IEEE Transactions on Medical Imaging 20, 4 (2001), 257-270. – reference: [HCLN09] Huang S.-H., Chu Y.-H., Lai S.-H., Novak C.: Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE Transactions on Medical Imaging 28, 10 (2009), 1595-1605. – reference: [VLP05] Vrtovec T., Likar B., Pernuš F.: Automated curved planar reformation of 3D spine images. Physics in Medicine and Biology 50, 19 (2005), 4527. – reference: [DLSH02] Davatzikos C., Liu D., Shen D., Herskovits E. H.: Spatial normalization of spine MR images for statistical correlation of lesions with clinical symptoms. Radiology 224, 3 (2002), 919-926. – reference: [ŠLPV11] Štern D., Likar B., Pernuš F., Vrtovec T.: Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Physics in Medicine and Biology 56, 23 (2011), 7505-7522. – reference: [CGNT09] Couprie C., Grady L. J., Najman L., Talbot H.: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest. In Proceedings of ICCV (2009), pp. 731-738. – reference: [NFE*12] Neubert A., Fripp J., Engstrom C., Schwarz R., Lauer L., Salvado O., Crozier S.: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Physics in Medicine and Biology 9 (2012), 8357-8376. – reference: [MCP*09] Michopoulou S., Costaridou L., Panagiotopoulos E., Speller R., Panayiotakis G., Todd-Pokropek A.: Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Transactions on Biomedical Engineering 56, 9 (2009), 2225-2231. – reference: [EKD*12] Egger J., Kapur T., Dukatz T., Kolodziej M., Zukić Dž., Freisleben B., Nimsky C.: Square-cut: A segmentation algorithm on the basis of a rectangle shape. PLoS ONE 7, 2 (2012), e31064. – reference: [ŠLPV10] Štern D., Likar B., Pernuš F., Vrtovec T.: Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine. Physics in Medicine and Biology 55, 1 (2010), 247-264. – reference: [KHDM98] Kittler J., Hatef M., Duin R., Matas J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 3 (1998), 226-239. – reference: [HM02] Hoad C. L., Martel A. L.: Segmentation of MR images for computer-assisted surgery of the lumbar spine. Physics in Medicine and Biology 47, 19 (2002), 3503. – reference: [Sar87] Saraste H.: Long-term clinical and radiological follow-up of spondylolysis and spondylolisthesis. Journal of Pediatric Orthopaedics 7, 6 (1987), 631-638. – volume: 65 start-page: 768 issue: 6 year: 1983 end-page: 772 article-title: Terminology and measurement of spondylolisthesis publication-title: The Journal of Bone and Joint Surgery – start-page: 189 year: 1996 end-page: 192 article-title: Interpolating subdivision for meshes with arbitrary topology publication-title: Proceedings of ACM SIGGRAPH – start-page: 731 year: 2009 end-page: 738 article-title: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest publication-title: Proceedings of ICCV – volume: 5 start-page: 354 issue: 5 year: 1995 end-page: 370 article-title: Comparison of semiquantitative and quantitative techniques for the assessment of prevalent and incident vertebral fractures publication-title: Osteoporosis International – start-page: 8357 year: 2012 end-page: 8376 article-title: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models publication-title: Physics in Medicine and Biology 9 – volume: 56 start-page: 7505 issue: 23 year: 2011 end-page: 7522 article-title: Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images publication-title: Physics in Medicine and Biology – volume: 117 start-page: 1072 issue: 9 year: 2013 end-page: 1083 article-title: Hierarchical segmentation and identification of thoracic vertebra using learning‐based edge detection and coarse‐to‐fine deformable model publication-title: Computer Vision and Image Understanding – start-page: 345 year: 1992 end-page: 381 – start-page: 135 year: 2012 end-page: 142 – volume: 19 start-page: 33 issue: 1 year: 2011 end-page: 40 article-title: Fast and memory efficient feature detection using multiresolution probabilistic boosting trees publication-title: Proceedings of the 19th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) – volume: 55 start-page: 247 issue: 1 year: 2010 end-page: 264 article-title: Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine publication-title: Physics in Medicine and Biology – start-page: 520 year: 2012 end-page: 527 – volume: 224 start-page: 919 issue: 3 year: 2002 end-page: 926 article-title: Spatial normalization of spine MR images for statistical correlation of lesions with clinical symptoms publication-title: Radiology – volume: 47 start-page: 3503 issue: 19 year: 2002 article-title: Segmentation of MR images for computer‐assisted surgery of the lumbar spine publication-title: Physics in Medicine and Biology – volume: 13 start-page: 471 issue: 3 year: 2009 end-page: 482 article-title: Automated model‐based vertebra detection, identification, and segmentation in {CT} images publication-title: Medical Image Analysis – volume: 56 start-page: 2225 issue: 9 year: 2009 end-page: 2231 article-title: Atlas‐based segmentation of degenerated lumbar intervertebral discs from MR images of the spine publication-title: IEEE Transactions on Biomedical Engineering – volume: 7 start-page: e31064 issue: 2 year: 2012 article-title: Square‐cut: A segmentation algorithm on the basis of a rectangle shape publication-title: PLoS ONE – volume: 144 start-page: 152 issue: 1–2 year: 2003 end-page: 161 article-title: An improved algorithm for anisotropic nonlinear diffusion for denoising cryo‐tomograms publication-title: Journal of Structural Biology – volume: 17 start-page: 167 issue: 2 year: 1998 end-page: 174 article-title: Metro: measuring error on simplified surfaces publication-title: Computer Graphics Forum – start-page: 511 year: 2001 end-page: 518 – volume: 50 start-page: 4527 issue: 19 year: 2005 article-title: Automated curved planar reformation of 3D spine images publication-title: Physics in Medicine and Biology – volume: 23 start-page: 36 issue: 1 year: 2004 end-page: 44 article-title: Normalized cuts in 3‐D for spinal MRI segmentation publication-title: IEEE Transactions on Medical Imaging – volume: 183 start-page: 949 issue: 4 year: 2004 end-page: 958 article-title: Diagnosis of osteoporotic vertebral fractures: Importance of recognition and description by radiologists publication-title: American Journal of Roentgenology – volume: 20 start-page: 226 issue: 3 year: 1998 end-page: 239 article-title: On combining classifiers publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 4 start-page: 306 issue: 6 year: 1988 end-page: 331 article-title: Deformable models publication-title: The Visual Computer – volume: 20 start-page: 257 issue: 4 year: 2001 end-page: 270 article-title: An adaptive‐focus statistical shape model for segmentation and shape modeling of 3‐D brain structures publication-title: IEEE Transactions on Medical Imaging – start-page: 261 year: 1948 end-page: 275 article-title: Outline for the study of scoliosis publication-title: Instructional Course Lectures 5 – volume: 12 start-page: 208 issue: 2 year: 2006 end-page: 218 article-title: Visualization of boundaries in volumetric data sets using LH histograms publication-title: IEEE Transactions on Visualization and Computer Graphics – volume: 26 start-page: 141 issue: 2 year: 2008 end-page: 163 article-title: A comparative study of deformable contour methods on medical image segmentation publication-title: Image and Vision Computing – volume: 14 start-page: 925 issue: 10 year: 2005 end-page: 948 article-title: The adult scoliosis publication-title: European Spine Journal – start-page: 51 year: 1994 end-page: 67 – volume: 7 start-page: 631 issue: 6 year: 1987 end-page: 638 article-title: Long‐term clinical and radiological follow‐up of spondylolysis and spondylolisthesis publication-title: Journal of Pediatric Orthopaedics – volume: 53 start-page: 211 issue: 2 year: 1991 end-page: 218 article-title: On active contour models and balloons publication-title: CVGIP: Image Understanding – year: 2011 article-title: Evaluation of a novel approach for automatic volume determination of glioblastomas based on several manual expert segmentations publication-title: CoRR abs/1103.1474 – volume: 35 start-page: 430 issue: 4 year: 2010 end-page: 433 article-title: Spine computed tomography doses and cancer induction publication-title: Spine – start-page: 137 year: 2013 end-page: 144 – volume: 28 start-page: 1595 issue: 10 year: 2009 end-page: 1605 article-title: Learning‐based vertebra detection and iterative normalized‐cut segmentation for spinal MRI publication-title: IEEE Transactions on Medical Imaging – start-page: 19 year: 2011 end-page: 24 article-title: Automated 3D segmentation of vertebral bodies and intervertebral discs from MRI publication-title: Proceedings of IEEE DICTA – volume: 17 start-page: 1283 issue: 8 year: 2013 end-page: 1292 article-title: Spine detection in CT and MR using iterated marginal space learning publication-title: Medical Image Analysis – volume: 169 start-page: 251 issue: 3 year: 2009 end-page: 258 article-title: The rising prevalence of chronic low back pain publication-title: Archives of Internal Medicine – ident: e_1_2_10_22_1 doi: 10.1016/j.cviu.2012.11.016 – start-page: 520 volume-title: Proceedings of MICCAI year: 2012 ident: e_1_2_10_3_1 – start-page: 19 year: 2011 ident: e_1_2_10_24_1 article-title: Automated 3D segmentation of vertebral bodies and intervertebral discs from MRI publication-title: Proceedings of IEEE DICTA – ident: e_1_2_10_23_1 doi: 10.1088/0031-9155/57/24/8357 – year: 2011 ident: e_1_2_10_10_1 article-title: Evaluation of a novel approach for automatic volume determination of glioblastomas based on several manual expert segmentations publication-title: CoRR abs/1103.1474 – ident: e_1_2_10_18_1 doi: 10.1016/j.media.2009.02.004 – start-page: 189 year: 1996 ident: e_1_2_10_40_1 article-title: Interpolating subdivision for meshes with arbitrary topology publication-title: Proceedings of ACM SIGGRAPH – ident: e_1_2_10_25_1 doi: 10.1007/978-94-015-8330-5_4 – ident: e_1_2_10_32_1 doi: 10.1088/0031-9155/56/23/011 – ident: e_1_2_10_2_1 doi: 10.1007/s00586-005-1053-9 – start-page: 261 year: 1948 ident: e_1_2_10_6_1 article-title: Outline for the study of scoliosis publication-title: Instructional Course Lectures 5 – ident: e_1_2_10_31_1 doi: 10.1088/0031-9155/55/1/015 – ident: e_1_2_10_34_1 doi: 10.1007/BF01908877 – ident: e_1_2_10_4_1 doi: 10.1109/TMI.2003.819929 – start-page: 137 volume-title: Proceedings of Vision, Modeling and Visualization year: 2013 ident: e_1_2_10_26_1 – ident: e_1_2_10_37_1 doi: 10.1088/0031-9155/50/19/007 – ident: e_1_2_10_9_1 doi: 10.1148/radiol.2243011266 – ident: e_1_2_10_15_1 doi: 10.1088/0031-9155/47/19/305 – ident: e_1_2_10_28_1 doi: 10.1097/01241398-198707060-00001 – ident: e_1_2_10_8_1 doi: 10.1111/1467-8659.00236 – start-page: 731 year: 2009 ident: e_1_2_10_5_1 article-title: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest publication-title: Proceedings of ICCV – volume: 19 start-page: 33 issue: 1 year: 2011 ident: e_1_2_10_33_1 article-title: Fast and memory efficient feature detection using multiresolution probabilistic boosting trees publication-title: Proceedings of the 19th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) – ident: e_1_2_10_30_1 doi: 10.1109/42.921475 – ident: e_1_2_10_27_1 doi: 10.1097/BRS.0b013e3181cdde47 – start-page: 511 volume-title: Proceedings of IEEE Computer Vision & Pattern Recognition year: 2001 ident: e_1_2_10_36_1 – ident: e_1_2_10_20_1 doi: 10.2214/ajr.183.4.1830949 – ident: e_1_2_10_13_1 doi: 10.1016/j.jsb.2003.09.010 – ident: e_1_2_10_19_1 doi: 10.1016/j.media.2012.09.007 – ident: e_1_2_10_12_1 doi: 10.1001/archinternmed.2008.543 – ident: e_1_2_10_17_1 doi: 10.1109/34.667881 – ident: e_1_2_10_39_1 doi: 10.2106/00004623-198365060-00007 – ident: e_1_2_10_7_1 doi: 10.1016/1049-9660(91)90028-N – ident: e_1_2_10_35_1 doi: 10.1007/978-94-011-2546-8_20 – start-page: 135 volume-title: Proceedings of Vision, Modeling and Visualization year: 2012 ident: e_1_2_10_41_1 – ident: e_1_2_10_14_1 doi: 10.1109/TMI.2009.2023362 – ident: e_1_2_10_29_1 doi: 10.1109/TVCG.2006.39 – ident: e_1_2_10_38_1 doi: 10.1007/BF01622258 – ident: e_1_2_10_21_1 doi: 10.1109/TBME.2009.2019765 – ident: e_1_2_10_11_1 doi: 10.1371/journal.pone.0031064 – ident: e_1_2_10_16_1 doi: 10.1016/j.imavis.2007.07.010 |
| SSID | ssj0004765 |
| Score | 2.3619187 |
| Snippet | The diagnosis of certain spine pathologies, such as scoliosis, spondylolisthesis and vertebral fractures, is part of the daily clinical routine. Very... |
| SourceID | proquest crossref wiley istex |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 190 |
| SubjectTerms | Analysis Computer graphics detection diagnosis efficient I.4.6 [Image processing and computer vision]: Segmentation Pixel classification Image processing systems inflation Medical diagnosis Medical imaging MRI Pathology Scoliosis segmentation Spine Studies vertebra Vertebrae |
| Title | Robust Detection and Segmentation for Diagnosis of Vertebral Diseases Using Routine MR Images |
| URI | https://api.istex.fr/ark:/67375/WNG-7R6373WH-M/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcgf.12343 https://www.proquest.com/docview/1565522964 |
| Volume | 33 |
| WOSCitedRecordID | wos000342802200014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library customDbUrl: eissn: 1467-8659 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004765 issn: 0167-7055 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS9xAFH_obg_20FardK0tgxTpJbKbzGQSeipuVwu6lNWqFxnmcxE1K5tY-uf3zSRZV2hB6G0IL8nwvt_Mm98AfKJU0oHO0og5piKMEFmEUc5GRg84t4obFYDnz4_5eJxdXuY_VuBLexamxodYLLh5ywj-2hu4VOWSkeup20e3S5NV6Maot7QD3eFk9PP48VgkT1kL7e1BYxpgId_Is3j5STjqes7-fpJrLmesIeSMXv_XZN_AqybTJF9r1ViHFVtswMsl_MG3cDWZqYeyIkNbhY6sgsjCkFM7vWtOJBUEc1oyrNvxrksyc-Tcziu_2XyLj8PeTklC2wHxvUX4aXIyId_v0EuVm3A2-nZ2cBQ19y1E2ldZkTMxTy0dqGTgYhkrX-tkzBndNxLtXqPk-ooyx02eyRxpqWPSxDLNmM51P9mCTjEr7DsgseFaK4t6wBlVKVdUSo1DTnPH0G304HPLdaEbLHJ_JcataGsSZJgIDOvB7oL0vgbg-BvRXhDdgkLOb3zHGmfiYnwo-CRNeHJxJE56sNPKVjTGWgosYRmmoXlKcV5Biv_-kzg4HIXB9vNJ38Maplm07kzbgU41f7Af4IX-VV2X84-N1v4BfBDuDg |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3da9swED-6ZLD1Yd-j6bpNjDH24pHYkmXDXkazNGVJGFnW9mUIfZay1imxW_rn7yTbWQobDPYmzNkW933S6SeAt5RKOtBZGjHHVIQRIoswytnI6AHnVnGjAvD80YTPZtnJSf51Cz62Z2FqfIj1gpu3jOCvvYH7BekNK9en7gP6XZrcgS5FNWId6A7no--T3-ciecpabG-PGtMgC_lOnvXLt-JR17P25layuZmyhpgzevh_s30ED5pck3yqleMxbNniCWxvIBA-hR_zpboqKzK0VejJKogsDPlmTy-aM0kFwayWDOuGvLOSLB05sqvKbzef4-Owu1OS0HhAfHcRfppM5-TwAv1U-QwWo8-L_XHU3LgQaV9nRc7EPLV0oJKBi2WsfLWTMWd030i0fI2y6yvKHDd5JnOkpY5JE8s0YzrX_eQ5dIplYXeAxIZrrSxqAmdUpVxRKTUOOc0dQ8fRg_ct24Vu0Mj9pRjnoq1KkGEiMKwHb9aklzUEx5-I3gXZrSnk6qfvWeNMHM8OBJ-nCU-Ox2Lag71WuKIx11JgEcswEc1TivMKYvz7n8T-wSgMdv-d9DXcGy-mEzE5nH15Afcx6aJ1n9oedKrVlX0Jd_V1dVauXjUq_AvC4fH- |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS9xAFD5Yt5T2ofZKt9V2KKX0JWU3mckk0JfiGpWui2yt-lKGuYpUs7KJxZ_vmUmyrmBB6FsIJ8lwzpxb5ptvAD5RKulQZ2nEHFMRZogswixnI6OHnFvFjQrE84djPplkx8f5_gp86_bCNPwQix9u3jNCvPYObi-MW_JyfeK-YtylyQPoUZan6Ja90bT4Nb7ZF8lT1nF7e9aYllnII3kWD9_KRz2v2qtbxeZyyRpyTrH2f6N9Bk_bWpN8bybHc1ix5Qt4ssRA-BJ-T2fqsqrJyNYBk1USWRry056ct3uSSoJVLRk1gLzTiswcObTz2i83n-HtsLpTkQA8IB5dhK8me1Oye45xqnoFB8XWweZO1J64EGnfZ0XOxDy1dKiSoYtlrHy3kzFn9MBI9HyNthsoyhw3eSZzlKWOSRPLNGM614PkNayWs9K-ARIbrrWyOBM4oyrlikqp8ZLT3DEMHH340qld6JaN3B-KcSa6rgQVJoLC-vBxIXrRUHDcJfQ52G4hIed_PGaNM3E02RZ8miY8OdoRe31Y74wrWnetBDaxDAvRPKU4rmDGf39JbG4X4eLt_UU_wKP9USHGu5Mf7-Ax1ly0gamtw2o9v7Qb8FD_rU-r-ft2Bl8DYhbxeQ |
| 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=Robust+Detection+and+Segmentation+for+Diagnosis+of+Vertebral+Diseases+Using+Routine+MR+Images&rft.jtitle=Computer+graphics+forum&rft.au=Zuki%C4%87%2C+D%C5%BEenan&rft.au=Vlas%C3%A1k%2C+Ale%C5%A1&rft.au=Egger%2C+Jan&rft.au=Ho%C5%99%C3%ADnek%2C+Daniel&rft.date=2014-09-01&rft.issn=0167-7055&rft.eissn=1467-8659&rft.volume=33&rft.issue=6&rft.spage=190&rft.epage=204&rft_id=info:doi/10.1111%2Fcgf.12343&rft.externalDBID=10.1111%252Fcgf.12343&rft.externalDocID=CGF12343 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0167-7055&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0167-7055&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0167-7055&client=summon |