{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T09:00:00Z","timestamp":1774688400355,"version":"3.50.1"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"35","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18762-3","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:07:44Z","timestamp":1710270464000},"page":"81903-81932","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Possibilistic exponential spatial fuzzy clustering based cancer segmentation in multi-parametric prostate MRI"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8257-562X","authenticated-orcid":false,"given":"Gaurav","family":"Garg","sequence":"first","affiliation":[]},{"given":"Mamta","family":"Juneja","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"18762_CR1","unstructured":"https:\/\/cancerstatisticscenter.cancer.org\/. Accessed on 20\u201301\u20132021"},{"issue":"5","key":"18762_CR2","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.acuroe.2010.12.001","volume":"35","author":"J Ferr\u00eds-I-Tortajada","year":"2011","unstructured":"Ferr\u00eds-I-Tortajada J, Berbel-Tornero O, Garcia-i-Castell J, L\u00f3pez-Andreu JA, Sobrino-Najul E, Ortega-Garc\u00eda JA (2011) Non-dietary environmental risk factors in prostate cancer. Actas Urol\u00f3gicas Espa\u00f1olas (English Edition) 35(5):289\u2013295","journal-title":"Actas Urol\u00f3gicas Espa\u00f1olas (English Edition)"},{"key":"18762_CR3","first-page":"596","volume":"2","author":"S Jain","year":"2014","unstructured":"Jain S, Saxena S, Kumar A (2014) Epidemiology of prostate cancer in India. Elsevier B.V. 2:596\u2013605","journal-title":"Elsevier B.V."},{"key":"18762_CR4","first-page":"1035","volume":"37","author":"JV Hegde","year":"2013","unstructured":"Hegde JV, Mulkern RV, Panych LP, Fennessy FM, Fedorov A, Maier SE, Tempany CM (2013) Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. Wiley Periodicals 37:1035\u20131054","journal-title":"Wiley Periodicals"},{"key":"18762_CR5","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1118\/1.4918318","volume":"42","author":"JT Kwak","year":"2015","unstructured":"Kwak JT, Xu S, Wood BJ, Turkbey B, Choyke PL, Pinto PA, Wang S, Summers RM (2015) Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging. Med Phys 42:2368\u20132378","journal-title":"Med Phys"},{"key":"18762_CR6","doi-asserted-by":"crossref","unstructured":"Litjens GJ, Vos PC, Barentsz JO, Karssemeijer N, Huisman HJ. Automatic computer aided detection of abnormalities in multi-parametric prostate MRI. InMedical Imaging 2011: Computer-Aided Diagnosis 2011 Mar 4 (Vol. 7963, p 79630T). Int Soc Opt Photon","DOI":"10.1117\/12.877844"},{"issue":"1","key":"18762_CR7","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1118\/1.3521470","volume":"38","author":"R Lopes","year":"2011","unstructured":"Lopes R, Ayache A, Makni N, Puech P, Villers A, Mordon S, Betrouni N (2011) Prostate cancer characterization on MR images using fractal features. Med Phys 38(1):83\u201395","journal-title":"Med Phys"},{"issue":"2","key":"18762_CR8","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3390\/info8020049","volume":"8","author":"L Rundo","year":"2017","unstructured":"Rundo L, Militello C, Russo G, Garufi A, Vitabile S, Gilardi MC, Mauri G (2017) Automated prostate gland segmentation based on an unsupervised fuzzy C-means clustering technique using multispectral T1w and T2w MR imaging. Information 8(2):49","journal-title":"Information"},{"key":"18762_CR9","doi-asserted-by":"crossref","unstructured":"Niaf E, Rouvi\u00e8re O, Lartizien C. Computer-aided diagnosis for prostate cancer detection in the peripheral zone via multisequence MRI. InMedical Imaging 2011: Computer-Aided Diagnosis 2011 Mar 9 (Vol. 7963, p 79633P). Int Soc Opt Photon","DOI":"10.1117\/12.877231"},{"key":"18762_CR10","doi-asserted-by":"publisher","first-page":"115686","DOI":"10.1016\/j.eswa.2021.115686","volume":"186","author":"A Kaur","year":"2021","unstructured":"Kaur A, Chauhan APS, Aggarwal AK (2021) An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network. Expert Syst Appl 186:115686","journal-title":"Expert Syst Appl"},{"issue":"04","key":"18762_CR11","doi-asserted-by":"publisher","first-page":"2257001","DOI":"10.1142\/S0218001422570014","volume":"36","author":"SD Ramlal","year":"2022","unstructured":"Ramlal SD, Sachdeva J, Ahuja CK, Khandelwal N (2022) Multimodal medical image fusion using nonsubsampled Shearlet transform and smallest uni-value segment assimilating nucleus. Int J Pattern Recognit Artif Intell 36(04):2257001","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"18762_CR12","unstructured":"Srivastava A, Singhal V, Aggarawal AK (2017) Comparative analysis of multimodal medical image fusion using PCA and wavelet transforms. Int J Latest Technol Eng Manag Appl Sci (IJLTEMAS) VI"},{"issue":"105","key":"18762_CR13","first-page":"990","volume":"10","author":"S Shambhu","year":"2023","unstructured":"Shambhu S, Koundal D, Das P (2023) Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images. Int J Adv Technol Eng Explor 10(105):990","journal-title":"Int J Adv Technol Eng Explor"},{"key":"18762_CR14","doi-asserted-by":"publisher","first-page":"102915","DOI":"10.1016\/j.media.2023.102915","volume":"89","author":"P Pati","year":"2023","unstructured":"Pati P, Jaume G, Ayadi Z, Thandiackal K, Bozorgtabar B, Gabrani M, Goksel O (2023) Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer. Med Image Anal 89:102915","journal-title":"Med Image Anal"},{"key":"18762_CR15","doi-asserted-by":"crossref","unstructured":"Fetisov N, Hall L, Goldgof D and Schabath M (2023) Unsupervised prostate cancer histopathology image segmentation via meta-learning. In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) (pp 838\u2013844). IEEE","DOI":"10.1109\/CBMS58004.2023.00329"},{"key":"18762_CR16","doi-asserted-by":"crossref","unstructured":"Shukla PK, Chandanan AK, Maheshwari P and Jena S (2023) A Computer-Aided Detection (CAD) System for the recognition of prostate cancer grounded on classification. In 2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP) (pp 454\u2013458). IEEE","DOI":"10.1109\/IHCSP56702.2023.10127119"},{"issue":"2","key":"18762_CR17","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1111\/biom.13602","volume":"79","author":"M Masotti","year":"2023","unstructured":"Masotti M, Zhang L, Leng E, Metzger GJ, Koopmeiners JS (2023) A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI. Biometrics 79(2):604\u2013615","journal-title":"Biometrics"},{"key":"18762_CR18","doi-asserted-by":"crossref","unstructured":"Mazzetti S, De Luca M, Bracco C, Vignati A, Giannini V, Stasi M, Russo F, Armando E, Agliozzo S, Regge D. A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI. InMedical Imaging 2011: Computer-Aided Diagnosis 2011 Mar 9 (Vol. 7963, p 79633Q). Int Soc Opt Photon","DOI":"10.1117\/12.877549"},{"issue":"1","key":"18762_CR19","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1002\/jmri.25562","volume":"46","author":"SB Ginsburg","year":"2017","unstructured":"Ginsburg SB, Algohary A, Pahwa S, Gulani V, Ponsky L, Aronen HJ, Bostr\u00f6m PJ, B\u00f6hm M, Haynes AM, Brenner P, Delprado W (2017) Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study. J Magn Reson Imaging 46(1):184\u2013193","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"18762_CR20","doi-asserted-by":"publisher","first-page":"041307","DOI":"10.1117\/1.JMI.4.4.041307","volume":"4","author":"T Clark","year":"2017","unstructured":"Clark T, Zhang J, Baig S, Wong A, Haider MA, Khalvati F (2017) Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks. J Med Imaging 4(4):041307","journal-title":"J Med Imaging"},{"key":"18762_CR21","doi-asserted-by":"crossref","unstructured":"Seetharaman A, Bhattacharya I, Chen LC, Kunder CA, Shao W, Soerensen SJ, Wang JB, Teslovich NC, Fan RE, Ghanouni P, Brooks JD. Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging. Medical Physics. 2021 Mar 24","DOI":"10.1002\/mp.14855"},{"issue":"3","key":"18762_CR22","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1023\/A:1014080923068","volume":"46","author":"N Paragios","year":"2002","unstructured":"Paragios N, Deriche R (2002) Geodesic active regions and level set methods for supervised texture segmentation. Int J Comput Vision 46(3):223\u2013247","journal-title":"Int J Comput Vision"},{"issue":"2","key":"18762_CR23","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"TF Chan","year":"2001","unstructured":"Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266\u2013277","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"18762_CR24","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.patrec.2012.12.022","volume":"34","author":"L Wang","year":"2013","unstructured":"Wang L, Wu H, Pan C (2013) Region-based image segmentation with local signed difference energy. Pattern Recogn Lett 34(6):637\u2013645","journal-title":"Pattern Recogn Lett"},{"issue":"46","key":"18762_CR25","doi-asserted-by":"publisher","first-page":"E6265","DOI":"10.1073\/pnas.1505935112","volume":"112","author":"D Fehr","year":"2015","unstructured":"Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, Sala E, Hricak H, Deasy JO (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci 112(46):E6265\u2013E6273","journal-title":"Proc Natl Acad Sci"},{"issue":"4","key":"18762_CR26","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1097\/MOU.0b013e32835481c2","volume":"22","author":"B Turkbey","year":"2012","unstructured":"Turkbey B, Choyke PL (2012) Multiparametric MRI and prostate cancer diagnosis and risk stratification. Curr Opin Urol 22(4):310","journal-title":"Curr Opin Urol"},{"issue":"1","key":"18762_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40644-016-0068-2","volume":"16","author":"P Steiger","year":"2016","unstructured":"Steiger P, Thoeny HC (2016) Prostate MRI based on PI-RADS version 2: how we review and report. Cancer Imaging 16(1):1\u20139","journal-title":"Cancer Imaging"},{"issue":"3","key":"18762_CR28","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1002\/jmri.25983","volume":"48","author":"A Algohary","year":"2018","unstructured":"Algohary A, Viswanath S, Shiradkar R, Ghose S, Pahwa S, Moses D, Jambor I, Shnier R, B\u00f6hm M, Haynes AM, Brenner P (2018) Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. J Magn Reson Imaging 48(3):818\u2013828","journal-title":"J Magn Reson Imaging"},{"issue":"12","key":"18762_CR29","doi-asserted-by":"publisher","first-page":"3833","DOI":"10.1088\/0031-9155\/57\/12\/3833","volume":"57","author":"E Niaf","year":"2012","unstructured":"Niaf E, Rouvi\u00e8re O, M\u00e8ge-Lechevallier F, Bratan F, Lartizien C (2012) Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys Med Biol 57(12):3833","journal-title":"Phys Med Biol"},{"key":"18762_CR30","doi-asserted-by":"crossref","unstructured":"Litjens GJ, Elliott R, Shih N, Feldman M, Barentsz JO, Hulsbergen-van de Kaa CA, Kovacs I, Huisman HJ, Madabhushi A. Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI. InMedical Imaging 2014: Computer-Aided Diagnosis 2014 Mar 18 (Vol. 9035, p 903512). Int Soc Opt Photon","DOI":"10.1117\/12.2043751"},{"issue":"1","key":"18762_CR31","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1002\/jmri.23618","volume":"36","author":"SE Viswanath","year":"2012","unstructured":"Viswanath SE, Bloch NB, Chappelow JC, Toth R, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A (2012) Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2-weighted MR imagery. J Magn Reson Imaging 36(1):213\u2013224","journal-title":"J Magn Reson Imaging"},{"issue":"5","key":"18762_CR32","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s10278-018-0160-1","volume":"32","author":"R Alkadi","year":"2019","unstructured":"Alkadi R, Taher F, El-Baz A, Werghi N (2019) A deep learning-based approach for the detection and localization of prostate cancer in T2 magnetic resonance images. J Digit Imaging 32(5):793\u2013807","journal-title":"J Digit Imaging"},{"issue":"9","key":"18762_CR33","doi-asserted-by":"publisher","first-page":"714","DOI":"10.3390\/diagnostics10090714","volume":"10","author":"MR Sunoqrot","year":"2020","unstructured":"Sunoqrot MR, Seln\u00e6s KM, Sandsmark E, Nketiah GA, Zavala-Romero O, Stoyanova R, Bathen TF, Elschot M (2020) A quality control system for automated prostate segmentation on T2-weighted MRI. Diagnostics 10(9):714","journal-title":"Diagnostics"},{"issue":"16","key":"18762_CR34","doi-asserted-by":"publisher","first-page":"6497","DOI":"10.1088\/1361-6560\/aa7731","volume":"62","author":"MH Le","year":"2017","unstructured":"Le MH, Chen J, Wang L, Wang Z, Liu W, Cheng KT, Yang X (2017) Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks. Phys Med Biol 62(16):6497","journal-title":"Phys Med Biol"},{"issue":"5","key":"18762_CR35","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.bspc.2011.09.003","volume":"7","author":"S Parfait","year":"2012","unstructured":"Parfait S, Walker PM, Cr\u00e9hange G, Tizon X, Miteran J (2012) Classification of prostate magnetic resonance spectra using Support Vector Machine. Biomed Signal Process Control 7(5):499\u2013508","journal-title":"Biomed Signal Process Control"},{"key":"18762_CR36","doi-asserted-by":"crossref","unstructured":"Viswanath S, Bloch BN, Chappelow J, Patel P, Rofsky N, Lenkinski R, Genega E, Madabhushi A. Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI. InMedical Imaging 2011: Computer-Aided Diagnosis 2011 Mar 4 (Vol. 7963, p 79630U). Int Soc Opt Photon","DOI":"10.1117\/12.878312"},{"key":"18762_CR37","doi-asserted-by":"crossref","unstructured":"Litjens GJ, Barentsz JO, Karssemeijer N, Huisman HJ. Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach. InMedical Imaging 2012: Computer-Aided Diagnosis 2012 Feb 23 (Vol. 8315, p 83150G). Int Soc Opt Photon","DOI":"10.1117\/12.911061"},{"issue":"6","key":"18762_CR38","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1088\/0031-9155\/57\/6\/1527","volume":"57","author":"PC Vos","year":"2012","unstructured":"Vos PC, Barentsz JO, Karssemeijer N, Huisman HJ (2012) Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Phys Med Biol 57(6):1527","journal-title":"Phys Med Biol"},{"key":"18762_CR39","doi-asserted-by":"crossref","unstructured":"Liu P, Wang S, Turkbey B, Grant K, Pinto P, Choyke P, Wood BJ, Summers RM. A prostate cancer computer-aided diagnosis system using multimodal magnetic resonance imaging and targeted biopsy labels. InMedical Imaging 2013: Computer-Aided Diagnosis 2013 Feb 26 (Vol. 8670, p 86701G). Int Soc Opt Photon","DOI":"10.1117\/12.2007927"},{"issue":"3","key":"18762_CR40","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1148\/radiol.13121454","volume":"267","author":"Y Peng","year":"2013","unstructured":"Peng Y, Jiang Y, Yang C, Brown JB, Antic T, Sethi I, Schmid-Tannwald C, Giger ML, Eggener SE, Oto A (2013) Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score\u2014a computer-aided diagnosis development study. Radiology 267(3):787\u2013796","journal-title":"Radiology"},{"key":"18762_CR41","doi-asserted-by":"crossref","unstructured":"Lemaitre G, Massich J, Mart\u00ed R, Freixenet J, Vilanova JC, Walker PM, Sidib\u00e9 D, M\u00e9riaudeau F. A boosting approach for prostate cancer detection using multi-parametric MRI. InTwelfth International Conference on Quality Control by Artificial Vision 2015 2015 Apr 30 (Vol. 9534, p. 95340A). Int Soc Opt Photon","DOI":"10.1117\/12.2182772"},{"issue":"4","key":"18762_CR42","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s10257-014-0239-2","volume":"13","author":"D Anderson","year":"2015","unstructured":"Anderson D, Golden B, Wasil E, Zhang H (2015) Predicting prostate cancer risk using magnetic resonance imaging data. IseB 13(4):599\u2013608","journal-title":"IseB"},{"issue":"6","key":"18762_CR43","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1109\/TBME.2015.2485779","volume":"63","author":"A Cameron","year":"2016","unstructured":"Cameron A, Khalvati F, Haider MA, Wong A (2016) MAPS: a quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng 63(6):1145\u20131156","journal-title":"IEEE Trans Biomed Eng"},{"key":"18762_CR44","doi-asserted-by":"crossref","unstructured":"Khalvati F, Zhang J, Wong A, Haider MA. Bag of Bags: Nested Multi Instance Classification for Prostate Cancer Detection. InMachine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on 2016 Dec 18 (pp. 146\u2013151). IEEE.","DOI":"10.1109\/ICMLA.2016.0032"},{"issue":"6","key":"18762_CR45","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1002\/cncr.29874","volume":"122","author":"CP Filson","year":"2016","unstructured":"Filson CP, Natarajan S, Margolis DJ, Huang J, Lieu P, Dorey FJ, Reiter RE, Marks LS (2016) Prostate cancer detection with magnetic resonance-ultrasound fusion biopsy: The role of systematic and targeted biopsies. Cancer 122(6):884\u2013892","journal-title":"Cancer"},{"key":"18762_CR46","unstructured":"Lemaitre G. Computer-aided diagnosis for prostate cancer using multi-parametric magnetic resonance imaging (Doctoral dissertation, Ph. D. dissertation, Universitat de Girona and Universit\u00e9 de Bourgogne)"},{"key":"18762_CR47","doi-asserted-by":"crossref","unstructured":"Caselles V, Kimmel R, Sapiro G. Geodesic active contours. InProceedings of IEEE international conference on computer vision 1995 Jun 20 (pp 694\u2013699). IEEE","DOI":"10.1109\/ICCV.1995.466871"},{"issue":"5","key":"18762_CR48","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.2214\/AJR.10.6062","volume":"197","author":"YS Sung","year":"2011","unstructured":"Sung YS, Kwon HJ, Park BW, Cho G, Lee CK, Cho KS, Kim JK (2011) Prostate cancer detection on dynamic contrast-enhanced MRI: computer-aided diagnosis versus single perfusion parameter maps. Am J Roentgenol 197(5):1122\u20131129","journal-title":"Am J Roentgenol"},{"issue":"4","key":"18762_CR49","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1002\/nbm.1777","volume":"25","author":"P Tiwari","year":"2012","unstructured":"Tiwari P, Viswanath S, Kurhanewicz J, Sridhar A, Madabhushi A (2012) Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection. NMR Biomed 25(4):607\u2013619","journal-title":"NMR Biomed"},{"key":"18762_CR50","doi-asserted-by":"crossref","unstructured":"Peng Y, Jiang Y, Antic T, Giger ML, Eggener S, Oto A. A study of T 2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer. InMedical Imaging 2013: Computer-Aided Diagnosis 2013 Feb 26 (Vol. 8670, p 86701H). Int Soc Opt Photon","DOI":"10.1117\/12.2007979"},{"issue":"9","key":"18762_CR51","doi-asserted-by":"publisher","first-page":"2390","DOI":"10.1118\/1.1593633","volume":"30","author":"I Chan","year":"2003","unstructured":"Chan I, Wells W, Mulkern RV, Haker S, Zhang J, Zou KH, Maier SE, Tempany C (2003) Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. Med Phys 30(9):2390\u20132398","journal-title":"Med Phys"},{"key":"18762_CR52","doi-asserted-by":"crossref","unstructured":"Giannini V, Vignati A, Mazzetti S, De Luca M, Bracco C, Stasi M, Russo F, Armando E, Regge D. A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images. InMedical Imaging 2013: Computer-Aided Diagnosis 2013 Feb 28 (Vol. 8670, p 86703E). Int Soc Opt Photon","DOI":"10.1117\/12.2006336"},{"issue":"6","key":"18762_CR53","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1002\/jmri.24487","volume":"40","author":"L Matulewicz","year":"2014","unstructured":"Matulewicz L, Jansen JF, Bokacheva L, Vargas HA, Akin O, Fine SW, Shukla-Dave A, Eastham JA, Hricak H, Koutcher JA, Zakian KL (2014) Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging. J Magn Reson Imaging 40(6):1414\u20131421","journal-title":"J Magn Reson Imaging"},{"issue":"2","key":"18762_CR54","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.media.2012.10.004","volume":"17","author":"P Tiwari","year":"2013","unstructured":"Tiwari P, Kurhanewicz J, Madabhushi A (2013) Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI\/MRS. Med Image Anal 17(2):219\u2013235","journal-title":"Med Image Anal"},{"key":"18762_CR55","doi-asserted-by":"crossref","unstructured":"Duda D, Kretowski M, Mathieu R, de Crevoisier R, Bezy-Wendling J. Multi-image texture analysis in classification of prostatic tissues from MRI. Preliminary results. InInformation Technologies in Biomedicine, Volume 3 2014 (pp 139\u2013150). Springer, Cham","DOI":"10.1007\/978-3-319-06593-9_13"},{"key":"18762_CR56","doi-asserted-by":"crossref","unstructured":"Cameron A, Modhafar A, Khalvati F, Lui D, Shafiee MJ, Wong A, Haider M. Multiparametric MRI prostate cancer analysis via a hybrid morphological-textural model. InEngineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE 2014 Aug 26 (pp 3357\u20133360). IEEE","DOI":"10.1109\/EMBC.2014.6944342"},{"key":"18762_CR57","doi-asserted-by":"crossref","unstructured":"Firjani A, Khalifa F, Elnakib A, Gimel\u2019farb G, El-Ghar MA, Elmaghraby A, El-Baz A. A novel image-based approach for early detection of prostate cancer using DCE-MRI. InComputational intelligence in biomedical imaging 2014 (pp 55\u201382). Springer, New York, NY","DOI":"10.1007\/978-1-4614-7245-2_3"},{"issue":"1","key":"18762_CR58","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s12880-015-0069-9","volume":"15","author":"F Khalvati","year":"2015","unstructured":"Khalvati F, Wong A, Haider MA (2015) Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging 15(1):27","journal-title":"BMC Med Imaging"},{"key":"18762_CR59","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/ACCESS.2015.2502220","volume":"3","author":"AG Chung","year":"2015","unstructured":"Chung AG, Khalvati F, Shafiee MJ, Haider MA, Wong A (2015) Prostate cancer detection via a quantitative radiomics-driven conditional random field framework. IEEE Access 3:2531\u20132541","journal-title":"IEEE Access"},{"key":"18762_CR60","doi-asserted-by":"crossref","unstructured":"Parra NA, Pollack A, Chinea FM, Abramowitz MC, Marples B, Munera F, Castillo R, Kryvenko ON, Punnen S, Stoyanova R (2017) Automatic Detection and Quantitative Dce-Mri scoring of Prostate cancer aggressiveness. Front Oncol 7","DOI":"10.3389\/fonc.2017.00259"},{"issue":"42","key":"18762_CR61","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.media.2017.08.006","volume":"1","author":"X Yang","year":"2017","unstructured":"Yang X, Liu C, Wang Z, Yang J, Le Min H, Wang L, Cheng KT (2017) Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI. Med Image Anal 1(42):212\u2013227","journal-title":"Med Image Anal"},{"issue":"1","key":"18762_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-018-0258-4","volume":"18","author":"F Khalvati","year":"2018","unstructured":"Khalvati F, Zhang J, Chung AG, Shafiee MJ, Wong A, Haider MA (2018) MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection. BMC Med Imaging 18(1):1\u20134","journal-title":"BMC Med Imaging"},{"key":"18762_CR63","doi-asserted-by":"crossref","unstructured":"Liu Z, Jiang W, Lee KH, Lo YL, Ng YL, Dou Q, Vardhanabhuti V, Kwok KW. A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network. InWorkshop on Artificial Intelligence in Radiation Therapy 2019 Oct 17 (pp 43-51). Springer, Cham","DOI":"10.1007\/978-3-030-32486-5_6"},{"key":"18762_CR64","doi-asserted-by":"crossref","unstructured":"Rundo L, Han C, Zhang J, Hataya R, Nagano Y, Militello C, Ferretti C, Nobile MS, Tangherloni A, Gilardi MC, Vitabile S. CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study. InNeural Approaches to Dynamics of Signal Exchanges 2020 (pp 269\u2013280). Springer, Singapore","DOI":"10.1007\/978-981-13-8950-4_25"},{"key":"18762_CR65","first-page":"552","volume":"2020","author":"J Sanyal","year":"2020","unstructured":"Sanyal J, Banerjee I, Hahn L, Rubin D (2020) An automated two-step pipeline for aggressive prostate lesion detection from multi-parametric MR sequence. AMIA Summit Transl Sci Proc 2020:552","journal-title":"AMIA Summit Transl Sci Proc"},{"key":"18762_CR66","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6487-2","author":"G Garg","year":"2018","unstructured":"Garg G, Juneja M (2018) A survey of denoising techniques for multi-parametric prostate MRI. Multimedia Tools Appl. https:\/\/doi.org\/10.1007\/s11042-018-6487-2","journal-title":"Multimedia Tools Appl"},{"issue":"2","key":"18762_CR67","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s11390-013-1331-7","volume":"28","author":"K Treerattanapitak","year":"2013","unstructured":"Treerattanapitak K, Jaruskulchai C (2013) Possibilistic exponential fuzzy clustering. J Comput Sci Technol 28(2):311\u2013321","journal-title":"J Comput Sci Technol"},{"key":"18762_CR68","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.compbiomed.2015.02.009","volume":"60","author":"G Lemaitre","year":"2015","unstructured":"Lemaitre G, Marti R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F (2015) Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A Review. Computer in Biology and Medicine 60:8\u201331","journal-title":"Computer in Biology and Medicine"},{"key":"18762_CR69","unstructured":"http:\/\/i2cvb.github.io\/. Accessed 24 Apr 2022"},{"key":"18762_CR70","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.bspc.2016.07.015","volume":"31","author":"R Trigui","year":"2017","unstructured":"Trigui R, Mit\u00e9ran J, Walker PM, Sellami L, Hamida AB (2017) Automatic classification and localization of prostate cancer using multi-parametric MRI\/MRS. Biomed Signal Process Control 31:189\u2013198","journal-title":"Biomed Signal Process Control"},{"key":"18762_CR71","doi-asserted-by":"crossref","unstructured":"Garg G, Juneja M (2018) A survey on computer-aided detection techniques of prostate cancer. In\u00a0Progress in Advanced Computing and Intelligent Engineering\u00a0(pp 115\u2013125). Springer, Singapore","DOI":"10.1007\/978-981-10-6875-1_12"},{"issue":"1","key":"18762_CR72","doi-asserted-by":"publisher","first-page":"19","DOI":"10.2174\/1573405613666170504145842","volume":"14","author":"G Garg","year":"2018","unstructured":"Garg G, Juneja M (2018) A survey of prostate segmentation techniques in different imaging modalities. Curr Med Imaging Rev 14(1):19\u201346","journal-title":"Curr Med Imaging Rev"},{"key":"18762_CR73","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/0-387-33006-2_4","volume-title":"System Modeling and Optimization","author":"T B\u00eersan","year":"2006","unstructured":"B\u00eersan T, Tiba D (2006) One hundred years since the introduction of the set distance by Dimitrie Pompeiu. In: Ceragioli F, Dontchev A, Futura H, Marti K, Pandolfi L (eds) System Modeling and Optimization, vol 199. Kluwer Academic Publishers, Boston, pp 35\u201339"},{"issue":"6943","key":"18762_CR74","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1136\/bmj.308.6943.1552","volume":"308","author":"DG Altman","year":"1994","unstructured":"Altman DG, Bland JM (1994) Diagnostic tests. 1: Sensitivity and specificity. BMJ 308(6943):1552","journal-title":"BMJ"},{"key":"18762_CR75","unstructured":"Murgu\u00eda M and Jos\u00e9 LV (2003) Estimating the effect of the similarity coefficient and the cluster algorithm on biogeographic classifications. In Annales Botanici Fennici, pp 415\u2013421. Finnish Zoological and Botanical Publishing Board"},{"key":"18762_CR76","doi-asserted-by":"crossref","unstructured":"Metz CE (1978) Basic principles of ROC analysis. In Seminars in nuclear medicine. 8(4)283\u2013298. WB Saunders","DOI":"10.1016\/S0001-2998(78)80014-2"},{"issue":"1","key":"18762_CR77","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1175\/1520-0434(1996)011<0003:TFAASE>2.0.CO;2","volume":"11","author":"AH Murphy","year":"1996","unstructured":"Murphy AH (1996) The Finley affair: A signal event in the history of forecast verification. Weather Forecast 11(1):3\u201320","journal-title":"Weather Forecast"},{"key":"18762_CR78","doi-asserted-by":"crossref","unstructured":"Dalmau, Oscar, and Mariano Rivera. Beta-measure for probabilistic segmentation. In Mexican International Conference on Artificial Intelligence, pp 312\u2013324. Springer, Berlin, Heidelberg, 2010","DOI":"10.1007\/978-3-642-16761-4_28"},{"issue":"9","key":"18762_CR79","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1016\/S0031-3203(97)00159-3","volume":"31","author":"X Shen","year":"1998","unstructured":"Shen X, Spann M, Nacken P (1998) Segmentation of 2D and 3D images through a hierarchical clustering based on region modelling. Pattern Recogn 31(9):1295\u20131309","journal-title":"Pattern Recogn"},{"key":"18762_CR80","doi-asserted-by":"crossref","unstructured":"Burney Aqil SM and Humera T (2014) K-means cluster analysis for image segmentation. Int J Comput App 96(4)","DOI":"10.5120\/16779-6360"},{"key":"18762_CR81","doi-asserted-by":"crossref","unstructured":"Adhikari SK, Jamuna KS, Dipak KB and Mita N (2015) Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images. App Soft Comput 34(2015):758\u2013769","DOI":"10.1016\/j.asoc.2015.05.038"},{"key":"18762_CR82","first-page":"1","volume":"1","author":"L Zhang","year":"2022","unstructured":"Zhang L, Gui Z, Wang J, Zhang P, Qin Z, Liu Y (2022) Spatial information-based intuitionistic fuzzy kernel clustering algorithm for cerebral hemorrhage image segmentation. SIViP 1:1\u20139","journal-title":"SIViP"},{"issue":"1","key":"18762_CR83","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):29","journal-title":"BMC Med Imaging"},{"key":"18762_CR84","doi-asserted-by":"publisher","first-page":"106375","DOI":"10.1016\/j.cmpb.2021.106375","volume":"210","author":"D Hoar","year":"2021","unstructured":"Hoar D, Lee PQ, Guida A, Patterson S, Bowen CV, Merrimen J, Wang C, Rendon R, Beyea SD, Clarke SE (2021) Combined transfer learning and test-time augmentation improves convolutional neural network-based semantic segmentation of prostate cancer from multi-parametric MR images. Comput Methods Programs Biomed 210:106375","journal-title":"Comput Methods Programs Biomed"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18762-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18762-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18762-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T05:13:01Z","timestamp":1731561181000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18762-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,12]]},"references-count":84,"journal-issue":{"issue":"35","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["18762"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18762-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,12]]},"assertion":[{"value":"22 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no biomedical financial interests or potential conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}