{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:34:40Z","timestamp":1731648880602,"version":"3.28.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"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\/s11042-024-19207-7","type":"journal-article","created":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T03:34:46Z","timestamp":1713497686000},"page":"84381-84400","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["No reference retinal image quality assessment using support vector machine classifier in wavelet domain"],"prefix":"10.1007","volume":"83","author":[{"given":"Sima","family":"Sahu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nishita","family":"Priyadarshini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"19207_CR1","doi-asserted-by":"publisher","first-page":"107238","DOI":"10.1016\/j.cmpb.2022.107238","volume":"228","author":"T Guo","year":"2023","unstructured":"Guo T, Liang Z, Gu Y, Liu K, Xu X, Yang J, Yu Q (2023) Learning for retinal image quality assessment with label regularization. Comput Methods Programs Biomed 228:107238","journal-title":"Comput Methods Programs Biomed"},{"key":"19207_CR2","doi-asserted-by":"crossref","unstructured":"Vashist P, Senjam SS, Gupta V, Gupta N, Shamanna BR, Wadhwani M, ..., Bharadwaj A (2022) Blindness and visual impairment and their causes in India: results of a nationally representative survey. PLoS One 17(7):e0271736","DOI":"10.1371\/journal.pone.0271736"},{"issue":"2","key":"19207_CR3","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TIP.2005.859378","volume":"15","author":"HR Sheikh","year":"2006","unstructured":"Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430\u2013444","journal-title":"IEEE Trans Image Process"},{"key":"19207_CR4","doi-asserted-by":"publisher","first-page":"4089","DOI":"10.1007\/s11042-017-5221-9","volume":"78","author":"S Sahu","year":"2019","unstructured":"Sahu S, Singh HV, Kumar B, Singh AK (2019) De-noising of ultrasound image using Bayesian approached heavy-tailed Cauchy distribution. Multimed Tools Appl 78:4089\u20134106","journal-title":"Multimed Tools Appl"},{"key":"19207_CR5","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.compbiomed.2017.09.012","volume":"90","author":"L Abdel-Hamid","year":"2017","unstructured":"Abdel-Hamid L, El-Rafei A, Michelson G (2017) No-reference quality index for color retinal images. Comput Biol Med 90:68\u201375","journal-title":"Comput Biol Med"},{"issue":"6","key":"19207_CR6","doi-asserted-by":"publisher","first-page":"160","DOI":"10.3390\/jimaging8060160","volume":"8","author":"I St\u0119pie\u0144","year":"2022","unstructured":"St\u0119pie\u0144 I, Oszust M (2022) A brief survey on no-reference image quality assessment methods for magnetic resonance images. J Imaging 8(6):160","journal-title":"J Imaging"},{"key":"19207_CR7","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.mri.2017.07.016","volume":"43","author":"LS Chow","year":"2017","unstructured":"Chow LS, Rajagopal H (2017) Modified-BRISQUE as no reference image quality assessment for structural MR images. Magn Reson Imaging 43:74\u201387","journal-title":"Magn Reson Imaging"},{"key":"19207_CR8","doi-asserted-by":"publisher","unstructured":"Ou FZ, Wang YG, Zhu G (2019) A novel blind image quality assessment method based on refined natural scene statistics. In: 2019 IEEE international conference on image processing (ICIP). Taipei, Taiwan, pp 1004\u20131008. https:\/\/doi.org\/10.1109\/ICIP.2019.8803047","DOI":"10.1109\/ICIP.2019.8803047"},{"issue":"6","key":"19207_CR9","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.media.2006.09.006","volume":"10","author":"M Niemeijer","year":"2006","unstructured":"Niemeijer M, Abramoff MD, van Ginneken B (2006) Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med Image Anal 10(6):888\u2013898","journal-title":"Med Image Anal"},{"key":"19207_CR10","doi-asserted-by":"crossref","unstructured":"MacGillivray TJ, Cameron JR, Zhang Q, El-Medany A, Mulholland C, Sheng Z, ..., UK Biobank Eye and Vision Consortium (2015) Suitability of UK Biobank retinal images for automatic analysis of morphometric properties of the vasculature.\u00a0PLoS One 10(5):e0127914","DOI":"10.1371\/journal.pone.0127914"},{"issue":"22","key":"19207_CR11","doi-asserted-by":"publisher","first-page":"34005","DOI":"10.1007\/s11042-023-14805-3","volume":"82","author":"Z Xu","year":"2023","unstructured":"Xu Z, Zou B, Liu Q (2023) A deep retinal image quality assessment network with salient structure priors. Multimed Tools Appl 82(22):34005\u201334028. https:\/\/doi.org\/10.1007\/s11042-023-14805-3","journal-title":"Multimed Tools Appl"},{"key":"19207_CR12","doi-asserted-by":"publisher","first-page":"57810","DOI":"10.1109\/ACCESS.2020.2982588","volume":"8","author":"A Raj","year":"2020","unstructured":"Raj A, Shah NA, Tiwari AK, Martini MG (2020) Multivariate regression-based convolutional neural network model for fundus image quality assessment. IEEE Access 8:57810\u201357821","journal-title":"IEEE Access"},{"key":"19207_CR13","doi-asserted-by":"publisher","unstructured":"Fu H et al (2019) Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces. In: Shen D et al (eds) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11764. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-32239-7_6","DOI":"10.1007\/978-3-030-32239-7_6"},{"issue":"4","key":"19207_CR14","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1109\/TMI.2015.2506902","volume":"35","author":"S Wang","year":"2015","unstructured":"Wang S, Jin K, Lu H, Cheng C, Ye J, Qian D (2015) Human visual system-based fundus image quality assessment of portable fundus camera photographs. IEEE Trans Med Imaging 35(4):1046\u20131055","journal-title":"IEEE Trans Med Imaging"},{"key":"19207_CR15","doi-asserted-by":"publisher","unstructured":"Yulianti T, Septama HD, Himayani R, Nugroho HA, Setiawan NA (2022) No reference image quality assessment of retinal image for diabetic retinopathy detection based on feature extraction. AIP conference proceedings, vol 2563. p 080009. https:\/\/doi.org\/10.1063\/5.0103286","DOI":"10.1063\/5.0103286"},{"key":"19207_CR16","doi-asserted-by":"publisher","unstructured":"Nugroho HA, Yulianti T, Setiawan NA, Dharmawan DA (2014) Contrast measurement for no-reference retinal image quality assessment. 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE), vol 2014. Yogyakarta, Indonesia, pp 1\u20134. https:\/\/doi.org\/10.1109\/ICITEED.2014.7007902","DOI":"10.1109\/ICITEED.2014.7007902"},{"key":"19207_CR17","doi-asserted-by":"publisher","unstructured":"K\u00f6hler T, Budai A, Kraus MF, Odstr\u010dilik J, Michelson G, Hornegger J (2013) Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. Proceedings of the 26th IEEE international symposium on computer-based medical systems, vol 2013. Porto, Portugal, pp 95\u2013100. https:\/\/doi.org\/10.1109\/CBMS.2013.6627771","DOI":"10.1109\/CBMS.2013.6627771"},{"issue":"9","key":"19207_CR18","doi-asserted-by":"publisher","first-page":"096007","DOI":"10.1117\/1.JBO.21.9.096007","volume":"21","author":"L Abdel-Hamid","year":"2016","unstructured":"Abdel-Hamid L, El-Rafei A, El-Ramly S, Michelson G, Hornegger J (2016) Retinal image quality assessment based on image clarity and content. J Biomed Opt 21(9):096007\u2013096007","journal-title":"J Biomed Opt"},{"key":"19207_CR19","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.compbiomed.2018.10.004","volume":"103","author":"GT Zago","year":"2018","unstructured":"Zago GT, Andreao RV, Dorizzi B, Salles EOT (2018) Retinal image quality assessment using deep learning. Comput Biol Med 103:64\u201370","journal-title":"Comput Biol Med"},{"issue":"3","key":"19207_CR20","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1016\/j.asej.2021.02.010","volume":"12","author":"L Abdel-Hamid","year":"2021","unstructured":"Abdel-Hamid L (2021) Retinal image quality assessment using transfer learning: spatial images vs. wavelet detail subbands. Ain Shams Eng J 12(3):2799\u20132807","journal-title":"Ain Shams Eng J"},{"key":"19207_CR21","doi-asserted-by":"crossref","unstructured":"Hamid LA, El-Rafei A, El-Ramly S, Michelson G, Hornegger J (2015) No-reference wavelet based retinal image quality assessment. In: Computational vision and medical image processing V: proceedings of the 5th eccomas thematic conference on computational vision and medical image processing (VipIMAGE), Spain. pp 123","DOI":"10.1201\/b19241-22"},{"key":"19207_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-023-16575-4","volume":"83","author":"S Sahu","year":"2023","unstructured":"Sahu S, Singh AK (2023) Genetic algorithm based multi-resolution approach for de-speckling OCT image. Multimed Tools Appl 83:1\u201322. https:\/\/doi.org\/10.1007\/s11042-023-16575-4","journal-title":"Multimed Tools Appl"},{"key":"19207_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101875","volume":"59","author":"CK Jha","year":"2020","unstructured":"Jha CK, Kolekar MH (2020) Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier. Biomed Signal Process Control 59:101875","journal-title":"Biomed Signal Process Control"},{"key":"19207_CR24","unstructured":"Retina image bank: a project from the American society of Retina specialists.\u00a0http:\/\/imagebank.asrs.org\/about. Accessed 30 June 2023"},{"key":"19207_CR25","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1136\/bjo-2022-321963","volume":"108","author":"M K\u00f6nig","year":"2024","unstructured":"K\u00f6nig M, Seeb\u00f6ck P, Gerendas BS, Mylonas G, Winklhofer R, Dimakopoulou I, Schmidt-Erfurth UM (2024) Quality assessment of colour fundus and fluorescein angiography images using deep learning. Br J Ophthalmol 108:98\u2013104","journal-title":"Br J Ophthalmol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19207-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19207-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19207-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T13:20:25Z","timestamp":1731590425000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19207-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,19]]},"references-count":25,"journal-issue":{"issue":"36","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["19207"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19207-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,4,19]]},"assertion":[{"value":"19 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 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":"The authors of this manuscript declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}