{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T07:08:04Z","timestamp":1746860884868,"version":"3.37.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s10044-021-01021-8","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T05:24:20Z","timestamp":1629350660000},"page":"1685-1698","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Superpixel\/voxel medical image segmentation algorithm based on the regional interlinked value"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4397-7212","authenticated-orcid":false,"given":"Lingling","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"issue":"5","key":"1021_CR1","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.3390\/app10051800","volume":"10","author":"KP Win","year":"2020","unstructured":"Win KP, Kitjaidure Y, Hamamoto K et al (2020) Computer-assisted screening for cervical cancer using digital image processing of pap smear images. Appl Sci 10(5):1800","journal-title":"Appl Sci"},{"issue":"2","key":"1021_CR2","doi-asserted-by":"publisher","first-page":"116","DOI":"10.3390\/brainsci10020116","volume":"10","author":"Y Wang","year":"2020","unstructured":"Wang Y, Qi Q, Shen X (2020) Image segmentation of brain MRI based on LTriDP and superpixels of improved SLIC. Brain Sci 10(2):116","journal-title":"Brain Sci"},{"key":"1021_CR3","doi-asserted-by":"publisher","first-page":"107532","DOI":"10.1016\/j.patcog.2020.107532","volume":"108","author":"FL Galv\u00e3o","year":"2020","unstructured":"Galv\u00e3o FL, Guimar\u00e3es SJF, Falc\u00e3o AX (2020) Image segmentation using dense and sparse hierarchies of superpixels. Pattern Recognit 108:107532","journal-title":"Pattern Recognit"},{"key":"1021_CR4","doi-asserted-by":"publisher","first-page":"101945","DOI":"10.1016\/j.bspc.2020.101945","volume":"60","author":"H Ramadan","year":"2020","unstructured":"Ramadan H, Lachqar C, Tairi H (2020) Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images. Biomed Signal Process Control 60:101945","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"1021_CR5","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1080\/10255840903131878","volume":"13","author":"Z Ma","year":"2009","unstructured":"Ma Z et al (2009) A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Eng 13(2):235\u2013246","journal-title":"Comput Methods Biomech Biomed Eng"},{"issue":"8","key":"1021_CR6","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1080\/10255842.2012.723700","volume":"17","author":"A Ferreira","year":"2014","unstructured":"Ferreira A, Gentil F, Tavares JMRS (2014) Segmentation algorithms for ear image data towards biomechanical studies. Comput Methods Biomech Biomed Eng 17(8):888\u2013904","journal-title":"Comput Methods Biomech Biomed Eng"},{"issue":"2","key":"1021_CR7","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1080\/10255840903131878","volume":"13","author":"Z Ma","year":"2010","unstructured":"Ma Z et al (2010) A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Engin 13(2):235\u2013246","journal-title":"Comput Methods Biomech Biomed Engin"},{"issue":"3","key":"1021_CR8","first-page":"203","volume":"9","author":"PCT Gon\u00e7alves","year":"2009","unstructured":"Gon\u00e7alves PCT, Tavares JMRS, Jorge RMN (2009) Segmentation and simulation of objects represented in images using physical principles. Int Conf Comput Exp Eng Sci 9(3):203\u2013204","journal-title":"Int Conf Comput Exp Eng Sci"},{"key":"1021_CR9","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cmpb.2016.03.032","volume":"131","author":"RB Oliveira","year":"2016","unstructured":"Oliveira RB et al (2016) Computational methods for the image segmentation of pigmented skin lesions: a review. Comput Methods Programs Biomed 131:127\u2013141","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"1021_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41074-017-0037-0","volume":"10","author":"X Huang","year":"2018","unstructured":"Huang X, Yang C, Ranka S et al (2018) Supervoxel-based segmentation of 3D imagery with optical flow integration for spatiotemporal processing. IPSJ Trans Comput Vis Appl 10(1):1\u201316","journal-title":"IPSJ Trans Comput Vis Appl"},{"key":"1021_CR11","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.patcog.2018.07.003","volume":"84","author":"JF Randrianasoa","year":"2018","unstructured":"Randrianasoa JF, Kurtz C, Desjardin \u00c9 et al (2018) Binary Partition Tree construction from multiple features for image segmentation. Pattern Recogn 84:237\u2013250","journal-title":"Pattern Recogn"},{"issue":"12","key":"1021_CR12","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.3390\/app9122421","volume":"9","author":"W He","year":"2019","unstructured":"He W, Li C, Guo Y et al (2019) A two-stage gradient ascent-based superpixel framework for adaptive segmentation. Appl Sci 9(12):2421","journal-title":"Appl Sci"},{"key":"1021_CR13","doi-asserted-by":"crossref","unstructured":"Saha R, Bajger M, Lee G (2018) Segmentation of cervical nuclei using SLIC and pairwise regional contrast. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society. IEEE Engineering in Medicine and Biology Society. Annual conference 2018, pp 3422\u20133425","DOI":"10.1109\/EMBC.2018.8513021"},{"key":"1021_CR14","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.compmedimag.2016.07.006","volume":"55","author":"Z Gao","year":"2017","unstructured":"Gao Z, Wei Bu, Zheng Y et al (2017) Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach. Comput Med Imaging Graph 55:42\u201353","journal-title":"Comput Med Imaging Graph"},{"issue":"9","key":"1021_CR15","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.3390\/app8091586","volume":"8","author":"S Kim","year":"2018","unstructured":"Kim S, Bae W, Masuda K et al (2018) Semi-automatic segmentation of vertebral bodies in MR images of human lumbar spines. Appl Sci 8(9):1586","journal-title":"Appl Sci"},{"issue":"9","key":"1021_CR16","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.3390\/rs12091528","volume":"12","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Su F, Yan F (2020) Novel semi-supervised hyperspectral image classification based on a superpixel graph and discrete potential method. Remote Sens 12(9):1528","journal-title":"Remote Sens"},{"issue":"3","key":"1021_CR17","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1134\/S1054661818030161","volume":"28","author":"P Ghosh","year":"2018","unstructured":"Ghosh P, Mali K, Das SK (2018) Use of spectral clustering combined with normalized cuts (N-Cuts) in an iterative k-means clustering framework (NKSC) for superpixel segmentation with contour adherence. Pattern Recognit Image Anal 28(3):400\u2013409","journal-title":"Pattern Recognit Image Anal"},{"key":"1021_CR18","doi-asserted-by":"publisher","first-page":"101636","DOI":"10.1016\/j.media.2020.101636","volume":"61","author":"F Guo","year":"2020","unstructured":"Guo F, Ng M, Goubran M et al (2020) Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: a continuous kernel cut approach. Med Image Anal 61:101636","journal-title":"Med Image Anal"},{"issue":"1","key":"1021_CR19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s12065-019-00226-5","volume":"13","author":"C Rajarao","year":"2020","unstructured":"Rajarao C, Singh RP (2020) Improved normalized graph cut with generalized data for enhanced segmentation in cervical cancer detection. Evol Intel 13(1):3\u20138","journal-title":"Evol Intel"},{"issue":"2","key":"1021_CR20","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167\u2013181","journal-title":"Int J Comput Vis"},{"issue":"4","key":"1021_CR21","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1002\/jbio.201600007","volume":"10","author":"W Wu","year":"2017","unstructured":"Wu W, Lin J, Wang S et al (2017) A novel multiphoton microscopy images segmentation method based on superpixel and watershed. J Biophotonics 10(4):532\u2013541","journal-title":"J Biophotonics"},{"issue":"1","key":"1021_CR22","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s10278-019-00200-8","volume":"33","author":"M Kowal","year":"2020","unstructured":"Kowal M, \u017bejmo M, Skobel M et al (2020) Cell nuclei segmentation in cytological images using convolutional neural network and seeded watershed algorithm. J Digit Imaging 33(1):231\u2013242","journal-title":"J Digit Imaging"},{"issue":"4","key":"1021_CR23","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3390\/ijgi9040246","volume":"9","author":"M Zhang","year":"2020","unstructured":"Zhang M, Xue Y, Ge Y et al (2020) Watershed segmentation algorithm based on luv color space region merging for extracting slope hazard boundaries. ISPRS Int J Geo-Inf 9(4):246","journal-title":"ISPRS Int J Geo-Inf"},{"issue":"6","key":"1021_CR24","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/34.87344","volume":"13","author":"L Vincent","year":"1991","unstructured":"Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583\u2013598","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"1021_CR25","doi-asserted-by":"publisher","first-page":"3317","DOI":"10.1109\/TIP.2017.2651389","volume":"26","author":"J Chen","year":"2017","unstructured":"Chen J, Li Z, Huang B (2017) Linear spectral clustering superpixel. IEEE Trans Image Process 26(7):3317\u20133330","journal-title":"IEEE Trans Image Process"},{"key":"1021_CR26","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neucom.2018.08.024","volume":"318","author":"J Duan","year":"2018","unstructured":"Duan J, Chen L, Chen CLP (2018) Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation. Neurocomputing 318:43\u201354","journal-title":"Neurocomputing"},{"issue":"3","key":"1021_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4172\/2167-0919.1000143","volume":"5","author":"R Ns","year":"2016","unstructured":"Ns R (2016) Image segmentation by using linear spectral clustering. J Telecommun Syst Manag 5(3):1\u20135","journal-title":"J Telecommun Syst Manag"},{"key":"1021_CR28","first-page":"1","volume":"11","author":"A Amami","year":"2018","unstructured":"Amami A, Azouz ZB, Alouane TH (2018) AdaSLIC: adaptive supervoxel generation for volumetric medical images. Multimed Tools Appl 11:1\u201323","journal-title":"Multimed Tools Appl"},{"issue":"13","key":"1021_CR29","doi-asserted-by":"publisher","first-page":"4448","DOI":"10.3390\/app10134448","volume":"10","author":"S Ortega","year":"2020","unstructured":"Ortega S, Fabelo H, Halicek M et al (2020) Hyperspectral superpixel-wise glioblastoma tumor detection in histological samples. Appl Sci 10(13):4448","journal-title":"Appl Sci"},{"issue":"3","key":"1021_CR30","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1587\/transinf.2019EDL8153","volume":"E103","author":"B Luo","year":"2020","unstructured":"Luo B, Xiong J, Xu L et al (2020) Superpixel segmentation based on global similarity and contour region transform. IEICE Trans Inf Syst E103(3):716\u2013719","journal-title":"IEICE Trans Inf Syst"},{"key":"1021_CR31","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cmpb.2019.01.005","volume":"170","author":"JOB Diniz","year":"2019","unstructured":"Diniz JOB, Diniz PHB, Valente TLA, Silva AC, Paiva AC (2019) Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks. Comput Methods Programs Biomed 170:53\u201367","journal-title":"Comput Methods Programs Biomed"},{"key":"1021_CR32","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.cmpb.2018.04.011","volume":"167","author":"PHB Diniz","year":"2018","unstructured":"Diniz PHB, Valente TLA, Diniz JOB, Silva AC, Gattass M, Ventura N et al (2018) Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network. Comput Methods Programs Biomed 167:49\u201363","journal-title":"Comput Methods Programs Biomed"},{"issue":"9","key":"1021_CR33","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1007\/s11517-020-02199-5","volume":"58","author":"GL da Silva","year":"2020","unstructured":"da Silva GL, Diniz PS, Ferreira JL, Fran\u00e7a JV, Silva AC, de Paiva AC, de Cavalcanti EA (2020) Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans. Med Biol Eng Comput 58(9):1947\u20131964","journal-title":"Med Biol Eng Comput"},{"issue":"11","key":"1021_CR34","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta R et al (2012) SLIC superpixels compared to state-of-the-artsuperpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274\u20132282","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"1021_CR35","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1111\/jmi.12595","volume":"268","author":"M Saha","year":"2017","unstructured":"Saha M, Arun I, Agarwal S, Ahmed R, Chatterjee S, Chakraborty C (2017) Imprint cytology-based breast malignancy screening: an efficient nuclei segmentation technique. J Microsc 268(2):155\u2013171","journal-title":"J Microsc"},{"key":"1021_CR36","doi-asserted-by":"crossref","unstructured":"da Silva GLF, Fran\u00e7a JVF, Diniz PS, et al (2020) Automatic prostate segmentation on 3D MRI scans using convolutional neural networks with residual connections and superpixels. In: 2020 international conference on systems, signals and image processing (IWSSIP). IEEE, pp 51\u201356","DOI":"10.1109\/IWSSIP48289.2020.9145218"},{"issue":"1","key":"1021_CR37","doi-asserted-by":"publisher","first-page":"13012","DOI":"10.1038\/s41598-018-31333-5","volume":"8","author":"I Aganj","year":"2018","unstructured":"Aganj I, Harisinghani MG, Weissleder R (2018) Unsupervised medical image segmentation based on the local Center of Mass. Sci Rep 8(1):13012","journal-title":"Sci Rep"},{"issue":"10","key":"1021_CR38","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"H MenzeBjoern","year":"2015","unstructured":"MenzeBjoern H et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"key":"1021_CR39","doi-asserted-by":"publisher","first-page":"103351","DOI":"10.1016\/j.compbiomed.2019.103351","volume":"111","author":"T Cogan","year":"2019","unstructured":"Cogan T, Cogan M, Tamil L (2019) MAPGI: accurate identification of anatomical landmarks and diseased tissue in gastrointestinal tract using deep learning. Comput Biol Med 111:103351","journal-title":"Comput Biol Med"},{"issue":"2","key":"1021_CR40","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/JBHI.2017.2660527","volume":"22","author":"CH Wu","year":"2018","unstructured":"Wu CH, Tsai WH, Chen YH et al (2018) Model-based orthodontic assessments for dental panoramic radiographs. IEEE J Biomed Health Inform 22(2):545\u2013551","journal-title":"IEEE J Biomed Health Inform"},{"key":"1021_CR41","first-page":"98120Z","volume":"9812","author":"N Sang","year":"2015","unstructured":"Sang N, Chen X, Xu M et al (2015) Super pixel density based clustering automatic image classification method. Int Symp Multispectr Image Process Pattern Recognit 9812:98120Z-98120Z\u20137","journal-title":"Int Symp Multispectr Image Process Pattern Recognit"},{"issue":"6","key":"1021_CR42","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","volume":"61","author":"AM Khan","year":"2014","unstructured":"Khan AM, Rajpoot N, Treanor D et al (2014) A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Bio-Med Eng 61(6):1729\u20131738","journal-title":"IEEE Trans Bio-Med Eng"},{"issue":"2","key":"1021_CR43","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1080\/21681163.2019.1631887","volume":"8","author":"A Jo\u00e3o","year":"2020","unstructured":"Jo\u00e3o A, Gambaruto A, Pereira R, Sequeira A (2020) Robust and effective automatic parameter choice for medical image filtering. Comput Methods Biomech Biomed Eng Imaging Vis 8(2):152\u2013168","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"issue":"1","key":"1021_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bbe.2018.09.006","volume":"39","author":"W Wieclawek","year":"2019","unstructured":"Wieclawek W, Pietka E (2019) Granular filter in medical image noise suppression and edge preservation. Biocybern Biomed Eng 39(1):1\u201316","journal-title":"Biocybern Biomed Eng"},{"issue":"2","key":"1021_CR45","doi-asserted-by":"publisher","first-page":"563","DOI":"10.3390\/s18020563","volume":"18","author":"H Shi","year":"2018","unstructured":"Shi H, Zhang Q, Bian M et al (2018) A novel ship detection method based on gradient integral feature for single-polarization synthetic aperture radar imagery. Sensors 18(2):563","journal-title":"Sensors"},{"key":"1021_CR46","doi-asserted-by":"publisher","first-page":"107432","DOI":"10.1016\/j.measurement.2019.107432","volume":"153","author":"Z Tirandaz","year":"2020","unstructured":"Tirandaz Z, Akbarizadeh G, Kaabi H (2020) PolSAR image segmentation based on feature extraction and data compression using Weighted Neighborhood Filter Bank and Hidden Markov random field-expectation maximization. Measurement 153:107432","journal-title":"Measurement"},{"issue":"9","key":"1021_CR47","doi-asserted-by":"publisher","first-page":"4559","DOI":"10.1002\/mp.12449","volume":"44","author":"ME Lindemann","year":"2017","unstructured":"Lindemann ME, Oehmigen M, Blumhagen JO et al (2017) MR-based truncation and attenuation correction in integrated PET\/MR hybrid imaging using HUGE with continuous table motion. Med Phys 44(9):4559\u20134572","journal-title":"Med Phys"},{"key":"1021_CR48","first-page":"97881Q","volume":"9788","author":"B Gimi","year":"2016","unstructured":"Gimi B, Krol A, He Y et al (2016) Automatic segmentation of canine retinal OCT using adaptive gradient enhancement and region growing. SPIE Med Imaging 9788:97881Q-97881Q\u20137","journal-title":"SPIE Med Imaging"},{"issue":"9","key":"1021_CR49","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.3390\/s20092610","volume":"20","author":"Y Li","year":"2020","unstructured":"Li Y, Hong Z, Cai D et al (2020) A SVM and SLIC based detection method for paddy field boundary line. Sensors 20(9):2610","journal-title":"Sensors"},{"issue":"10","key":"1021_CR50","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1049\/el.2019.3890","volume":"56","author":"H Lin","year":"2020","unstructured":"Lin H, Yuan F, Xing C, Yang J (2020) An edge attention-based geodesic distance for PolSAR image superpixel segmentation. Electron Lett 56(10):510\u2013512","journal-title":"Electron Lett"},{"issue":"3","key":"1021_CR51","doi-asserted-by":"publisher","first-page":"479","DOI":"10.3390\/s19030479","volume":"19","author":"B Zu","year":"2019","unstructured":"Zu B, Xia K, Li T et al (2019) SLIC superpixel-based l2,1-norm robust principal component analysis for hyperspectral image classification. Sensors 19(3):479","journal-title":"Sensors"},{"key":"1021_CR52","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.image.2017.04.007","volume":"56","author":"M Wang","year":"2017","unstructured":"Wang M, Liu X, Gao Y (2017) Superpixel segmentation: a benchmark. Signal Process Image Commun 56:28\u201339","journal-title":"Signal Process Image Commun"},{"issue":"1","key":"1021_CR53","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1002\/ima.22499","volume":"31","author":"L Ait Mohamed","year":"2020","unstructured":"Ait Mohamed L et al (2020) Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation. Int J Imaging Syst Technol 31(1):288\u2013301","journal-title":"Int J Imaging Syst Technol"},{"issue":"2","key":"1021_CR54","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1007\/s10586-017-0792-9","volume":"20","author":"C-Y Han","year":"2017","unstructured":"Han C-Y (2017) Improved SLIC imagine segmentation algorithm based on K-means. Clust Comput 20(2):1017\u20131023","journal-title":"Clust Comput"},{"issue":"3","key":"1021_CR55","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1515\/ipc-2016-0017","volume":"21","author":"A Bakkari","year":"2016","unstructured":"Bakkari A, Fabija\u0144ska A (2016) Features determination from super-voxels obtained with relative linear interactive clustering. Image Process Commun 21(3):69\u201379","journal-title":"Image Process Commun"},{"issue":"19","key":"1021_CR56","doi-asserted-by":"publisher","first-page":"6165","DOI":"10.1049\/joe.2019.0193","volume":"2019","author":"N Shao","year":"2019","unstructured":"Shao N, Zou H, Chen C et al (2019) Superpixel cosegmentation algorithm for synthetic aperture radar image change detection. J Eng 2019(19):6165\u20136169","journal-title":"J Eng"},{"issue":"5","key":"1021_CR57","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.irbm.2019.04.005","volume":"40","author":"M Angulakshmi","year":"2019","unstructured":"Angulakshmi M, Priya GGL (2019) Walsh hadamard transform for simple linear iterative clustering (SLIC) superpixel based spectral clustering of multimodal MRI brain tumor segmentation. Irbm 40(5):253\u2013262","journal-title":"Irbm"},{"issue":"3","key":"1021_CR58","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s11517-018-1906-0","volume":"57","author":"A Albayrak","year":"2019","unstructured":"Albayrak A, Bilgin G (2019) Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. Med Biol Eng Comput 57(3):653\u2013665","journal-title":"Med Biol Eng Comput"},{"issue":"3","key":"1021_CR59","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/sym9030031","volume":"9","author":"H Wang","year":"2017","unstructured":"Wang H, Peng X, Xiao X et al (2017) BSLIC: SLIC superpixels based on boundary term. Symmetry 9(3):31","journal-title":"Symmetry"},{"issue":"4","key":"1021_CR60","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1134\/S1054661817040101","volume":"27","author":"XX Li","year":"2017","unstructured":"Li XX, Shen XJ, Chen HP et al (2017) Image clustering segmentation based on SLIC superpixel and transfer learning. Pattern Recognit Image Anal 27(4):838\u2013845","journal-title":"Pattern Recognit Image Anal"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-021-01021-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-021-01021-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-021-01021-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,5]],"date-time":"2023-02-05T20:07:14Z","timestamp":1675627634000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-021-01021-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,19]]},"references-count":60,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["1021"],"URL":"https:\/\/doi.org\/10.1007\/s10044-021-01021-8","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2021,8,19]]},"assertion":[{"value":"1 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}