{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:48:53Z","timestamp":1777704533796,"version":"3.51.4"},"reference-count":16,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2018,7,28]],"date-time":"2018-07-28T00:00:00Z","timestamp":1532736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,11,20]]},"abstract":"<jats:p>Numerous Fuzzy segmentation techniques have been proposed in the literature for Image segmentation. This paper proposes a new Novel Intuitionistic Fuzzy C-means (S-IFCM) incorporated with Spatial information to reduce noise\/outliers influence. This new clustering algorithm uses City-block distance to compute the rank between two pixels. Yager\u2019s type fuzzy complement is used to compute non-membership and further hesitation degree is calculated. The new intuitionistic membership obtained is incorporated with spatial information of image for robustness to noise. Experiments are performed on various noisy images including MRI brain image, to assess the performance of the proposed algorithm. Comparison is done with existing hard, fuzzy and intuitionistic methods on the basis of entropy based segmentation accuracy and validity index. Experimental results show the effectiveness of the proposed method in contrast with other conventional methods.<\/jats:p>","DOI":"10.3233\/jifs-169809","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T18:11:13Z","timestamp":1533060673000},"page":"5255-5264","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Robust spatial intuitionistic fuzzy C-means with city-block distance clustering for image segmentation"],"prefix":"10.1177","volume":"35","author":[{"given":"Jyoti","family":"Arora","sequence":"first","affiliation":[{"name":"Department of Information Technology, MSIT, Affiliated to GGSIPU, Delhi, India"}]},{"given":"Meena","family":"Tushir","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, MSIT, Affiliated to GGSIPU, Delhi, India"}]}],"member":"179","published-online":{"date-parts":[[2018,7,28]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/4582948"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.06.052"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2219547"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.07.011"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2005.10.001"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(86)80034-3"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.05.005"},{"key":"e_1_3_1_9_2","first-page":"2224","article-title":"Novel Intuitionistic Fuzzy C-Means Clustering for Linearly and Nonlinearly Separable Data","volume":"11","author":"Kaur P.","year":"2012","unstructured":"KaurP., SoniA.K. and AnjanaG., Novel Intuitionistic Fuzzy C-Means Clustering for Linearly and Nonlinearly Separable Data, Wseas Transactions on Computers11 (2012), 2224\u20132872.","journal-title":"Wseas Transactions on Computers"},{"key":"e_1_3_1_10_2","first-page":"1","article-title":"An Atanassov\u2019s intuitionistic Fuzzy Kernel Clustering for Medical Image Segmentation","author":"Chaira T.","year":"2013","unstructured":"ChairaT. and PanwarA., An Atanassov\u2019s intuitionistic Fuzzy Kernel Clustering for Medical Image Segmentation, in: International Journal of Computational Intelligence Systems, Taylor and Francis (2013), 1\u201311.","journal-title":"International Journal of Computational Intelligence Systems, Taylor and Francis"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30503-3_28"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.873580"},{"key":"e_1_3_1_13_2","first-page":"187","volume-title":"Proc 3rd Int Conf Intell Inf Database Syst","author":"Przybyla T.","year":"2011","unstructured":"PrzybylaT., JezewskiJ., HorobaK. and RojD., Hybrid fuzzy Clustering using LP norms, in: Proc 3rd Int Conf Intell Inf Database Syst (2011), 187\u2013196."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1264-2"},{"key":"e_1_3_1_15_2","first-page":"47","volume":"5","author":"Kadir A.","year":"2012","unstructured":"KadirA., NugrohoL.E., SusantoA. and SantosaP.I., Experiments of Distance Measurement in a Foliage Plant Retrieval System, In: International Journal of Signal Processing, Image Processing and Pattern Recognition5 (2012), 47\u201360.","journal-title":"Image Processing and Pattern Recognition"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(98)00244-9"},{"key":"e_1_3_1_17_2","first-page":"38","article-title":"An entropy-based objective evaluation method for image segmentation","volume":"5307","author":"Zhang H.","year":"2004","unstructured":"ZhangH., FrittsJ. and GoldmanS., An entropy-based objective evaluation method for image segmentation, in: Proc SPIE Storage Retrieval Methods Appl Multimedia5307 (2004), 38\u201349.","journal-title":"Proc SPIE Storage Retrieval Methods Appl Multimedia"}],"container-title":["Journal of Intelligent &amp; 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