A robust clustering algorithm using spatial fuzzy C-means for brain MR images
Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come with several problems such as noise and other imaging artifacts added during acquisition process. The researchers have actual challenges for seg...
Uloženo v:
| Vydáno v: | Egyptian informatics journal Ročník 21; číslo 1; s. 51 - 66 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.03.2020
Elsevier |
| Témata: | |
| ISSN: | 1110-8665 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come with several problems such as noise and other imaging artifacts added during acquisition process. The researchers have actual challenges for segmentation under the consideration of these effects. In medical images, a well-known clustering approach like Fuzzy C-Means widely used for segmentation. The performance of FCM algorithm is fast in noise-free images; however, this method did not consider the spatial context of the image due to which its performance suffers when images corrupted with noise and other imaging relics. In this paper, a weighted spatial Fuzzy C-Means (wsFCM) segmentation method is proposed that considered the spatial information of image. Moreover, a spatial function is also developed that integrate a membership function. In order assess this function, a neighborhood window is established around a pixel and more weights have been assigned to those pixels which have greater correlation with central pixel in local neighborhood. By integration of this spatial function in membership function, the modified membership function strengthens the original membership function in handling the noise and intensity inhomogeneity, which has the ability to preserves and maintains structural information like edges. A comprehensive set of experimentation is performed on publicly accessible simulated and real standard brain MRI datasets. The performance of the proposed method has been compared with existing state-of-the-art methods. The results show that the performance of the proposed method is better and robust in handling noise and intensity inhomogeneity than of the existing works. |
|---|---|
| AbstractList | Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come with several problems such as noise and other imaging artifacts added during acquisition process. The researchers have actual challenges for segmentation under the consideration of these effects. In medical images, a well-known clustering approach like Fuzzy C-Means widely used for segmentation. The performance of FCM algorithm is fast in noise-free images; however, this method did not consider the spatial context of the image due to which its performance suffers when images corrupted with noise and other imaging relics. In this paper, a weighted spatial Fuzzy C-Means (wsFCM) segmentation method is proposed that considered the spatial information of image. Moreover, a spatial function is also developed that integrate a membership function. In order assess this function, a neighborhood window is established around a pixel and more weights have been assigned to those pixels which have greater correlation with central pixel in local neighborhood. By integration of this spatial function in membership function, the modified membership function strengthens the original membership function in handling the noise and intensity inhomogeneity, which has the ability to preserves and maintains structural information like edges. A comprehensive set of experimentation is performed on publicly accessible simulated and real standard brain MRI datasets. The performance of the proposed method has been compared with existing state-of-the-art methods. The results show that the performance of the proposed method is better and robust in handling noise and intensity inhomogeneity than of the existing works. Keywords: Clustering algorithm, MRI, Fuzzy C-means Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come with several problems such as noise and other imaging artifacts added during acquisition process. The researchers have actual challenges for segmentation under the consideration of these effects. In medical images, a well-known clustering approach like Fuzzy C-Means widely used for segmentation. The performance of FCM algorithm is fast in noise-free images; however, this method did not consider the spatial context of the image due to which its performance suffers when images corrupted with noise and other imaging relics. In this paper, a weighted spatial Fuzzy C-Means (wsFCM) segmentation method is proposed that considered the spatial information of image. Moreover, a spatial function is also developed that integrate a membership function. In order assess this function, a neighborhood window is established around a pixel and more weights have been assigned to those pixels which have greater correlation with central pixel in local neighborhood. By integration of this spatial function in membership function, the modified membership function strengthens the original membership function in handling the noise and intensity inhomogeneity, which has the ability to preserves and maintains structural information like edges. A comprehensive set of experimentation is performed on publicly accessible simulated and real standard brain MRI datasets. The performance of the proposed method has been compared with existing state-of-the-art methods. The results show that the performance of the proposed method is better and robust in handling noise and intensity inhomogeneity than of the existing works. |
| Author | Siddiqi, Muhammad Hameed Javed, Muhammad Arshad Alruwaili, Madallah |
| Author_xml | – sequence: 1 givenname: Madallah surname: Alruwaili fullname: Alruwaili, Madallah email: madallah@ju.edu.sa – sequence: 2 givenname: Muhammad Hameed surname: Siddiqi fullname: Siddiqi, Muhammad Hameed email: mhsiddiqi@ju.edu.sa – sequence: 3 givenname: Muhammad Arshad surname: Javed fullname: Javed, Muhammad Arshad email: arsh_qau@ju.edu.sa |
| BookMark | eNp9kM9qAyEQhz2k0DTNA_TmC2yqcXV36SmE_gkkFEp7FlfHrctmDbopJE9f05QeeoiHkRn4fjN8N2jU-x4QuqNkRgkV9-0MXDubE1qlfkYIH6ExpZRkpRD8Gk1jbEl6gs5zLsZos8DB1_s4YN2lCsH1DVZd44MbPrd4H0993KnBqQ7b_fF4wMtsC6qP2PqA66Bcjzdv2G1VA_EWXVnVRZj-_hP08fT4vnzJ1q_Pq-VinemciCGriOHWMCZyzeqqtJpwC6Y0NSuBcM5JDZowwwpi8jqVUmgmWFWyec21KGo2QatzrvGqlbuQtoeD9MrJn4EPjVRhcLoDmeJMBTllVuR5oVU5tzy3ylCmleaMpyx6ztLBxxjA_uVRIk9KZSuTUnlSeholpYkp_jHaDcmR74ckpLtIPpxJSHq-HAQZtYNeg3EB9JDudxfob5BRlFA |
| CitedBy_id | crossref_primary_10_3103_S014641162470010X crossref_primary_10_1016_j_advengsoft_2022_103377 crossref_primary_10_1016_j_cmpb_2020_105841 crossref_primary_10_1016_j_knosys_2021_108008 crossref_primary_10_1038_s41598_025_00897_4 crossref_primary_10_1155_2021_7270908 crossref_primary_10_3390_app11135931 crossref_primary_10_1155_2023_4387134 crossref_primary_10_1007_s00500_023_08542_w crossref_primary_10_1007_s11042_023_15230_2 crossref_primary_10_7717_peerj_cs_654 crossref_primary_10_1016_j_ijar_2021_06_004 crossref_primary_10_1186_s12880_021_00683_4 crossref_primary_10_3390_app11114878 crossref_primary_10_32604_cmc_2024_046501 crossref_primary_10_1016_j_bspc_2023_104925 crossref_primary_10_1016_j_eswa_2023_120377 crossref_primary_10_1007_s10489_024_05813_3 crossref_primary_10_1155_2021_6611053 crossref_primary_10_3390_math9101095 crossref_primary_10_1016_j_enbuild_2021_111121 crossref_primary_10_1007_s00357_023_09443_1 crossref_primary_10_1007_s00371_023_02910_1 |
| Cites_doi | 10.1016/j.cviu.2013.05.001 10.3844/ajassp.2014.329.336 10.1016/S0734-189X(85)90153-7 10.1007/s11704-010-0393-8 10.1109/TIP.2015.2473099 10.4236/jbise.2011.42014 10.1016/j.compmedimag.2010.12.001 10.1002/hbm.10062 10.1016/j.compmedimag.2005.10.001 10.2307/1932409 10.1016/j.patcog.2006.07.011 10.1007/s13369-015-1791-x 10.1016/j.neucom.2016.03.046 10.1007/s40846-015-0096-6 10.1109/LSP.2006.884014 10.1155/2015/450341 10.1109/TST.2014.6961028 10.3390/ijms140918682 10.1109/TITB.2005.847500 10.1007/s11390-016-1643-5 10.1016/j.patcog.2010.06.006 10.1007/s00138-014-0606-5 10.1016/j.patrec.2013.04.021 10.1186/1475-925X-14-2 10.1016/j.asoc.2015.05.038 |
| ContentType | Journal Article |
| Copyright | 2019 |
| Copyright_xml | – notice: 2019 |
| DBID | 6I. AAFTH AAYXX CITATION DOA |
| DOI | 10.1016/j.eij.2019.10.005 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EndPage | 66 |
| ExternalDocumentID | oai_doaj_org_article_555d9e413f6447ca82f54fad13cac535 10_1016_j_eij_2019_10_005 S1110866519301823 |
| GroupedDBID | --K 0R~ 1B1 4.4 457 5VS 6I. AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAXUO ABMAC ACGFS ADBBV ADEZE ADVLN AEXQZ AFJKZ AFTJW AGHFR AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV E3Z EBS EJD FDB GROUPED_DOAJ HZ~ IPNFZ IXB KQ8 M41 O-L O9- OK1 RIG ROL SES SSZ AAYWO AAYXX ACVFH ADCNI AEUPX AFPUW AIGII AKBMS AKYEP APXCP CITATION |
| ID | FETCH-LOGICAL-c406t-90d5fd3364c3b98fc05fed8db38e05550bec03d370d4b70d86c3639832b5c67b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 27 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000518407500006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1110-8665 |
| IngestDate | Fri Oct 03 12:41:10 EDT 2025 Wed Oct 29 21:13:51 EDT 2025 Tue Nov 18 21:32:33 EST 2025 Sun Apr 06 06:54:25 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Fuzzy C-means MRI Clustering algorithm |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c406t-90d5fd3364c3b98fc05fed8db38e05550bec03d370d4b70d86c3639832b5c67b3 |
| OpenAccessLink | https://doaj.org/article/555d9e413f6447ca82f54fad13cac535 |
| PageCount | 16 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_555d9e413f6447ca82f54fad13cac535 crossref_primary_10_1016_j_eij_2019_10_005 crossref_citationtrail_10_1016_j_eij_2019_10_005 elsevier_sciencedirect_doi_10_1016_j_eij_2019_10_005 |
| PublicationCentury | 2000 |
| PublicationDate | March 2020 2020-03-00 2020-03-01 |
| PublicationDateYYYYMMDD | 2020-03-01 |
| PublicationDate_xml | – month: 03 year: 2020 text: March 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Egyptian informatics journal |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Yu, Fan (b0090) 2008 Cma.mgh.harvard.edu. Liu, Li, Wang, Wu, Liu, Pan (b0060) 2014; 19 Zhao, Jiao, Liu (b0105) 2011; 5 Pham (b0065) 2002; vol. 2 Lewis T, Writer S. Human brain: Facts, functions & anatomy; 2016. [accessed: 2019-03-20]. Jiang, Wang, He, Chen, Li (b0085) 2016; 207 Qiu, Xiao, Yu, Han, Iqbal (b0180) 2013; 34 Du, Lai, Leung, Pong (b0005) 2013; 14 Long-term study plays down risk of brain problems in HIV-positive patients. Dong, Xu (b0150) 2007; 14 Dice (b0165) 1945; 26 Miao, Lin, Liu (b0030) 2011; 4 Alruwaili, Javed, Javed (b0140) 2017; 17 Shen, Sandham, Granat, Sterr (b0070) 2005; 9 Ali, Elmogy, El-Daydamony, Atwan (b0125) 2015; 40 Kumar, Kumar (b0025) 2014; 11 Adhikari, Sing, Basu, Nasipuri (b0045) 2015; 34 Deng, Li, Gao, Zhang (b0130) 2016; 31 [accessed: 2019-03-20]. McAuliffe, Lalonde, McGarry, Gandler, Csaky, Trus (b0155) 2001 Riad, Atwan, El, Mostafa, Elminir, Mastorakis (b0040) 2010; vol. 5125 Caldairou, Passat, Habas, Studholme, Rousseau (b0100) 2011; 44 Ji, Sun, Xia (b0110) 2011; 35 Chuang, Tzeng, Chen, Wu, Chen (b0075) 2006; 30 Yang, Fan, Ai, Zhou, Tang, Wang (b0010) 2015; 14 Chen, Chen, Wu, Horng, Wu, Hsueh, Ho (b0115) 2015; 35 Shi, Ngan, Li, Paramesran, Li (b0170) 2015; 24 Cai, Chen, Zhang (b0080) 2007; 40 Wang, Song, Soh, Sim (b0095) 2013; 117 Alipour, Shanbehzadeh (b0120) 2014; 25 Haralick, Shapiro (b0050) 1985; 29 Bezdek (b0135) 2013 Roy S, Nag S, Maitra IK, Bandyopadhyay SK. A review on automated brain tumor detection and segmentation from MRI of brain. arXiv preprint arXiv:1312.6150. Smith (b0145) 2002; 17 Thung, Raveendran (b0175) 2009; 2009 Despotovic I, Goossens B, Philips W. Mri segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine; 2015. Zhao (10.1016/j.eij.2019.10.005_b0105) 2011; 5 Wang (10.1016/j.eij.2019.10.005_b0095) 2013; 117 Chen (10.1016/j.eij.2019.10.005_b0115) 2015; 35 Shi (10.1016/j.eij.2019.10.005_b0170) 2015; 24 Caldairou (10.1016/j.eij.2019.10.005_b0100) 2011; 44 Yang (10.1016/j.eij.2019.10.005_b0010) 2015; 14 Cai (10.1016/j.eij.2019.10.005_b0080) 2007; 40 Qiu (10.1016/j.eij.2019.10.005_b0180) 2013; 34 Jiang (10.1016/j.eij.2019.10.005_b0085) 2016; 207 Liu (10.1016/j.eij.2019.10.005_b0060) 2014; 19 Du (10.1016/j.eij.2019.10.005_b0005) 2013; 14 Riad (10.1016/j.eij.2019.10.005_b0040) 2010; vol. 5125 Adhikari (10.1016/j.eij.2019.10.005_b0045) 2015; 34 Thung (10.1016/j.eij.2019.10.005_b0175) 2009; 2009 10.1016/j.eij.2019.10.005_b0015 Yu (10.1016/j.eij.2019.10.005_b0090) 2008 10.1016/j.eij.2019.10.005_b0035 Haralick (10.1016/j.eij.2019.10.005_b0050) 1985; 29 10.1016/j.eij.2019.10.005_b0055 Bezdek (10.1016/j.eij.2019.10.005_b0135) 2013 Alruwaili (10.1016/j.eij.2019.10.005_b0140) 2017; 17 Alipour (10.1016/j.eij.2019.10.005_b0120) 2014; 25 Miao (10.1016/j.eij.2019.10.005_b0030) 2011; 4 Ali (10.1016/j.eij.2019.10.005_b0125) 2015; 40 Chuang (10.1016/j.eij.2019.10.005_b0075) 2006; 30 Smith (10.1016/j.eij.2019.10.005_b0145) 2002; 17 McAuliffe (10.1016/j.eij.2019.10.005_b0155) 2001 Shen (10.1016/j.eij.2019.10.005_b0070) 2005; 9 Ji (10.1016/j.eij.2019.10.005_b0110) 2011; 35 Dice (10.1016/j.eij.2019.10.005_b0165) 1945; 26 10.1016/j.eij.2019.10.005_b0020 10.1016/j.eij.2019.10.005_b0160 Kumar (10.1016/j.eij.2019.10.005_b0025) 2014; 11 Deng (10.1016/j.eij.2019.10.005_b0130) 2016; 31 Dong (10.1016/j.eij.2019.10.005_b0150) 2007; 14 Pham (10.1016/j.eij.2019.10.005_b0065) 2002; vol. 2 |
| References_xml | – volume: 34 start-page: 758 year: 2015 end-page: 769 ident: b0045 article-title: Conditional spatial fuzzy c-means clustering algorithm for segmentation of MRI images publication-title: Appl Soft Comput – volume: 40 start-page: 825 year: 2007 end-page: 838 ident: b0080 article-title: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation publication-title: Pattern Recognition – volume: 40 start-page: 3173 year: 2015 end-page: 3185 ident: b0125 article-title: Multi-resolution MRI brain image segmentation based on morphological pyramid and fuzzy c-mean clustering publication-title: Arab J Sci Eng – reference: Roy S, Nag S, Maitra IK, Bandyopadhyay SK. A review on automated brain tumor detection and segmentation from MRI of brain. arXiv preprint arXiv:1312.6150. – reference: Cma.mgh.harvard.edu. – volume: 35 start-page: 383 year: 2011 end-page: 397 ident: b0110 article-title: A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain mr image publication-title: Comput Med Imaging Graph – volume: 35 start-page: 724 year: 2015 end-page: 734 ident: b0115 article-title: Automatic contrast enhancement of brain mr images using hierarchical correlation histogram analysis publication-title: J Med Biol Eng – volume: 14 start-page: 2 year: 2015 ident: b0010 article-title: Brain MR image denoising for Rician noise using pre-smooth non-local means filter publication-title: Biomed Eng Online – volume: 34 start-page: 1329 year: 2013 end-page: 1338 ident: b0180 article-title: A modified interval type-2 fuzzy c-means algorithm with application in mr image segmentation publication-title: Pattern Recogn Lett – volume: 17 start-page: 143 year: 2002 end-page: 155 ident: b0145 article-title: Fast robust automated brain extraction publication-title: Human Brain Mapping – reference: Lewis T, Writer S. Human brain: Facts, functions & anatomy; 2016. [accessed: 2019-03-20]. – volume: 30 start-page: 9 year: 2006 end-page: 15 ident: b0075 article-title: Fuzzy c-means clustering with spatial information for image segmentation publication-title: Comput Med Imaging Graph – reference: Long-term study plays down risk of brain problems in HIV-positive patients. – volume: 2009 start-page: 1 year: 2009 end-page: 4 ident: b0175 article-title: A survey of image quality measures, in, international conference for technical postgraduates (TECHPOS) publication-title: IEEE – reference: Despotovic I, Goossens B, Philips W. Mri segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine; 2015. – volume: vol. 2 year: 2002 ident: b0065 article-title: Fuzzy clustering with spatial constraints publication-title: Proceedings. International Conference on Image Processing – volume: 14 start-page: 193 year: 2007 end-page: 196 ident: b0150 article-title: A new directional weighted median filter for removal of random-valued impulse noise publication-title: IEEE Signal Process Lett – volume: 4 start-page: 100 year: 2011 end-page: 104 ident: b0030 article-title: Automatic segmentation of brain tissue based on improved fuzzy c means clustering algorithm publication-title: J Biomed Sci Eng – volume: 14 start-page: 18682 year: 2013 end-page: 18710 ident: b0005 article-title: Design of superparamagnetic nanoparticles for magnetic particle imaging (MPI) publication-title: Int J Mol Sci – year: 2013 ident: b0135 article-title: Pattern recognition with fuzzy objective function algorithms – start-page: 484 year: 2008 end-page: 493 ident: b0090 article-title: Three-level image segmentation based on maximum fuzzy partition entropy of 2-d histogram and quantum genetic algorithm publication-title: International Conference on Intelligent Computing – volume: 5 start-page: 45 year: 2011 end-page: 56 ident: b0105 article-title: Fuzzy c-means clustering with non local spatial information for noisy image segmentation publication-title: Front Comput Sci China – volume: 207 start-page: 22 year: 2016 end-page: 35 ident: b0085 article-title: Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints publication-title: Neurocomputing – reference: [accessed: 2019-03-20]. – volume: 44 start-page: 1916 year: 2011 end-page: 1927 ident: b0100 article-title: A non-local fuzzy segmentation method: application to brain MRI publication-title: Pattern Recogn – volume: 9 start-page: 459 year: 2005 end-page: 467 ident: b0070 article-title: MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization publication-title: IEEE Trans Inf Technol Biomed – volume: 26 start-page: 297 year: 1945 end-page: 302 ident: b0165 article-title: Measures of the amount of ecologic association between species publication-title: Ecology – volume: 31 start-page: 501 year: 2016 end-page: 511 ident: b0130 article-title: A modified fuzzy c-means algorithm for brain mr image segmentation and bias field correction publication-title: J Comput Sci Technol – volume: 11 start-page: 329 year: 2014 end-page: 336 ident: b0025 article-title: Performance analysis of brain tumor diagnosis based on soft computing techniques publication-title: Am J Appl Sci – volume: vol. 5125 start-page: 74 year: 2010 end-page: 83 ident: b0040 article-title: A new approach for segmentation of brain MR image publication-title: Proceedings of the WSEAS International Conference on Environment, Medicine and Health Sciences – volume: 19 start-page: 578 year: 2014 end-page: 595 ident: b0060 article-title: A survey of MRI-based brain tumor segmentation methods publication-title: Tsinghua Sci Technol – volume: 24 start-page: 5033 year: 2015 end-page: 5045 ident: b0170 article-title: Visual quality evaluation of image object segmentation: Subjective assessment and objective measure publication-title: IEEE Trans Image Process – volume: 117 start-page: 1412 year: 2013 end-page: 1420 ident: b0095 article-title: An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation publication-title: Comput Vis Image Underst – volume: 17 start-page: 252 year: 2017 ident: b0140 article-title: Hybrid genetic filter for restoration of brain MRI images corrupted with impulse noise publication-title: Int J Comput Sci Network Secur (IJCSNS) – volume: 29 start-page: 100 year: 1985 end-page: 132 ident: b0050 article-title: Image segmentation techniques publication-title: Computer Vision, Graphics, and Image Processing – volume: 25 start-page: 1469 year: 2014 end-page: 1488 ident: b0120 article-title: Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method publication-title: Mach Vision Appl – start-page: 381 year: 2001 end-page: 386 ident: b0155 article-title: Medical image processing, analysis and visualization in clinical research publication-title: Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001 – volume: 117 start-page: 1412 issue: 10 year: 2013 ident: 10.1016/j.eij.2019.10.005_b0095 article-title: An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation publication-title: Comput Vis Image Underst doi: 10.1016/j.cviu.2013.05.001 – volume: vol. 2 year: 2002 ident: 10.1016/j.eij.2019.10.005_b0065 article-title: Fuzzy clustering with spatial constraints – volume: 11 start-page: 329 issue: 2 year: 2014 ident: 10.1016/j.eij.2019.10.005_b0025 article-title: Performance analysis of brain tumor diagnosis based on soft computing techniques publication-title: Am J Appl Sci doi: 10.3844/ajassp.2014.329.336 – ident: 10.1016/j.eij.2019.10.005_b0020 – ident: 10.1016/j.eij.2019.10.005_b0160 – volume: 29 start-page: 100 issue: 1 year: 1985 ident: 10.1016/j.eij.2019.10.005_b0050 article-title: Image segmentation techniques publication-title: Computer Vision, Graphics, and Image Processing doi: 10.1016/S0734-189X(85)90153-7 – volume: 5 start-page: 45 issue: 1 year: 2011 ident: 10.1016/j.eij.2019.10.005_b0105 article-title: Fuzzy c-means clustering with non local spatial information for noisy image segmentation publication-title: Front Comput Sci China doi: 10.1007/s11704-010-0393-8 – volume: 24 start-page: 5033 issue: 12 year: 2015 ident: 10.1016/j.eij.2019.10.005_b0170 article-title: Visual quality evaluation of image object segmentation: Subjective assessment and objective measure publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2015.2473099 – volume: vol. 5125 start-page: 74 year: 2010 ident: 10.1016/j.eij.2019.10.005_b0040 article-title: A new approach for segmentation of brain MR image – volume: 4 start-page: 100 year: 2011 ident: 10.1016/j.eij.2019.10.005_b0030 article-title: Automatic segmentation of brain tissue based on improved fuzzy c means clustering algorithm publication-title: J Biomed Sci Eng doi: 10.4236/jbise.2011.42014 – volume: 35 start-page: 383 issue: 5 year: 2011 ident: 10.1016/j.eij.2019.10.005_b0110 article-title: A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain mr image publication-title: Comput Med Imaging Graph doi: 10.1016/j.compmedimag.2010.12.001 – volume: 17 start-page: 143 issue: 3 year: 2002 ident: 10.1016/j.eij.2019.10.005_b0145 article-title: Fast robust automated brain extraction publication-title: Human Brain Mapping doi: 10.1002/hbm.10062 – ident: 10.1016/j.eij.2019.10.005_b0055 – volume: 30 start-page: 9 issue: 1 year: 2006 ident: 10.1016/j.eij.2019.10.005_b0075 article-title: Fuzzy c-means clustering with spatial information for image segmentation publication-title: Comput Med Imaging Graph doi: 10.1016/j.compmedimag.2005.10.001 – volume: 26 start-page: 297 issue: 3 year: 1945 ident: 10.1016/j.eij.2019.10.005_b0165 article-title: Measures of the amount of ecologic association between species publication-title: Ecology doi: 10.2307/1932409 – volume: 40 start-page: 825 issue: 3 year: 2007 ident: 10.1016/j.eij.2019.10.005_b0080 article-title: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation publication-title: Pattern Recognition doi: 10.1016/j.patcog.2006.07.011 – volume: 40 start-page: 3173 issue: 11 year: 2015 ident: 10.1016/j.eij.2019.10.005_b0125 article-title: Multi-resolution MRI brain image segmentation based on morphological pyramid and fuzzy c-mean clustering publication-title: Arab J Sci Eng doi: 10.1007/s13369-015-1791-x – volume: 207 start-page: 22 year: 2016 ident: 10.1016/j.eij.2019.10.005_b0085 article-title: Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.03.046 – volume: 35 start-page: 724 issue: 6 year: 2015 ident: 10.1016/j.eij.2019.10.005_b0115 article-title: Automatic contrast enhancement of brain mr images using hierarchical correlation histogram analysis publication-title: J Med Biol Eng doi: 10.1007/s40846-015-0096-6 – volume: 2009 start-page: 1 year: 2009 ident: 10.1016/j.eij.2019.10.005_b0175 article-title: A survey of image quality measures, in, international conference for technical postgraduates (TECHPOS) publication-title: IEEE – volume: 14 start-page: 193 issue: 3 year: 2007 ident: 10.1016/j.eij.2019.10.005_b0150 article-title: A new directional weighted median filter for removal of random-valued impulse noise publication-title: IEEE Signal Process Lett doi: 10.1109/LSP.2006.884014 – ident: 10.1016/j.eij.2019.10.005_b0015 doi: 10.1155/2015/450341 – volume: 19 start-page: 578 issue: 6 year: 2014 ident: 10.1016/j.eij.2019.10.005_b0060 article-title: A survey of MRI-based brain tumor segmentation methods publication-title: Tsinghua Sci Technol doi: 10.1109/TST.2014.6961028 – volume: 14 start-page: 18682 issue: 9 year: 2013 ident: 10.1016/j.eij.2019.10.005_b0005 article-title: Design of superparamagnetic nanoparticles for magnetic particle imaging (MPI) publication-title: Int J Mol Sci doi: 10.3390/ijms140918682 – volume: 9 start-page: 459 issue: 3 year: 2005 ident: 10.1016/j.eij.2019.10.005_b0070 article-title: MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization publication-title: IEEE Trans Inf Technol Biomed doi: 10.1109/TITB.2005.847500 – year: 2013 ident: 10.1016/j.eij.2019.10.005_b0135 – ident: 10.1016/j.eij.2019.10.005_b0035 – volume: 31 start-page: 501 issue: 3 year: 2016 ident: 10.1016/j.eij.2019.10.005_b0130 article-title: A modified fuzzy c-means algorithm for brain mr image segmentation and bias field correction publication-title: J Comput Sci Technol doi: 10.1007/s11390-016-1643-5 – volume: 44 start-page: 1916 issue: 9 year: 2011 ident: 10.1016/j.eij.2019.10.005_b0100 article-title: A non-local fuzzy segmentation method: application to brain MRI publication-title: Pattern Recogn doi: 10.1016/j.patcog.2010.06.006 – start-page: 381 year: 2001 ident: 10.1016/j.eij.2019.10.005_b0155 article-title: Medical image processing, analysis and visualization in clinical research – start-page: 484 year: 2008 ident: 10.1016/j.eij.2019.10.005_b0090 article-title: Three-level image segmentation based on maximum fuzzy partition entropy of 2-d histogram and quantum genetic algorithm – volume: 25 start-page: 1469 issue: 6 year: 2014 ident: 10.1016/j.eij.2019.10.005_b0120 article-title: Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method publication-title: Mach Vision Appl doi: 10.1007/s00138-014-0606-5 – volume: 34 start-page: 1329 issue: 12 year: 2013 ident: 10.1016/j.eij.2019.10.005_b0180 article-title: A modified interval type-2 fuzzy c-means algorithm with application in mr image segmentation publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2013.04.021 – volume: 14 start-page: 2 issue: 1 year: 2015 ident: 10.1016/j.eij.2019.10.005_b0010 article-title: Brain MR image denoising for Rician noise using pre-smooth non-local means filter publication-title: Biomed Eng Online doi: 10.1186/1475-925X-14-2 – volume: 17 start-page: 252 issue: 2 year: 2017 ident: 10.1016/j.eij.2019.10.005_b0140 article-title: Hybrid genetic filter for restoration of brain MRI images corrupted with impulse noise publication-title: Int J Comput Sci Network Secur (IJCSNS) – volume: 34 start-page: 758 year: 2015 ident: 10.1016/j.eij.2019.10.005_b0045 article-title: Conditional spatial fuzzy c-means clustering algorithm for segmentation of MRI images publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2015.05.038 |
| SSID | ssj0000612456 |
| Score | 2.3328428 |
| Snippet | Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come... |
| SourceID | doaj crossref elsevier |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 51 |
| SubjectTerms | Clustering algorithm Fuzzy C-means MRI |
| Title | A robust clustering algorithm using spatial fuzzy C-means for brain MR images |
| URI | https://dx.doi.org/10.1016/j.eij.2019.10.005 https://doaj.org/article/555d9e413f6447ca82f54fad13cac535 |
| Volume | 21 |
| WOSCitedRecordID | wos000518407500006&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: PRVAON databaseName: DOAJ Open Access Full Text issn: 1110-8665 databaseCode: DOA dateStart: 20100101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: false ssIdentifier: ssj0000612456 providerName: Directory of Open Access Journals |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA4iHvQgPnF9kYMnoZpumm1yVHHxoIuIireSTBJd2YfsQ3B_vZO0K_WgXrz0ENKkTKbzzZCPbwg5kgxYpplKgDksUFxqEsVTnmipJUhIJYuyi4_Xeacjn57Uba3VV-CElfLApeFOhRBWOQy1HpE7By2bXmRe25SDBsGjeinLVa2YKmNwGm70YmcVDDRB1G1-pRnJXa77Gmhd6iQyu8Q3UIra_TVsquFNe42sVokiPSs_cJ0suMEGWanJB26SmzM6GprpeEKhNw2CBzhKde95iAX_S58GSvszHQfKNC7kp7PZB71I-g7BiWKqSk3oDkFv7mi3j0FlvEUe2pf3F1dJ1R4hAUThSaKYFd5y3sqAGyU9MOGdldZw6YKMF8PjYdzynNnM4EO2gGM-gr-wEdDKDd8mi4PhwO0Q2vQScq8y7j0WfNwanVnd1KCka2nPoEHY3D4FVNrhoYVFr5iTxF4LNGkRTBqG0KQNcvz1ylspnPHb5PNg9K-JQfM6DqAnFJUnFH95QoNk8yMrqvShTAtwqe7Pe-_-x957ZLkZCvFITtsni5PR1B2QJXifdMejw-ibn3uH5jQ |
| linkProvider | Directory of Open Access Journals |
| 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+robust+clustering+algorithm+using+spatial+fuzzy+C-means+for+brain+MR+images&rft.jtitle=Egyptian+informatics+journal&rft.au=Madallah+Alruwaili&rft.au=Muhammad+Hameed+Siddiqi&rft.au=Muhammad+Arshad+Javed&rft.date=2020-03-01&rft.pub=Elsevier&rft.issn=1110-8665&rft.volume=21&rft.issue=1&rft.spage=51&rft.epage=66&rft_id=info:doi/10.1016%2Fj.eij.2019.10.005&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_555d9e413f6447ca82f54fad13cac535 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1110-8665&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1110-8665&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1110-8665&client=summon |