{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T07:17:06Z","timestamp":1760080626386},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s11042-022-12529-4","type":"journal-article","created":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T08:02:21Z","timestamp":1646467341000},"page":"17095-17110","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["De-noising low dose CT images of the ovarian region using modified discrete wavelet transform"],"prefix":"10.1007","volume":"81","author":[{"given":"H. Heartlin","family":"Maria","sequence":"first","affiliation":[]},{"given":"A. Maria","family":"Jossy","sequence":"additional","affiliation":[]},{"given":"G.","family":"Malarvizhi","sequence":"additional","affiliation":[]},{"given":"A.","family":"Jenitta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,5]]},"reference":[{"key":"12529_CR1","doi-asserted-by":"publisher","unstructured":"Abramovich F, Sapatinas T (1999) Bayesian approach to wavelet decomposition and shrinkage. Lecture Notes in Statistics 141. https:\/\/doi.org\/10.1007\/978-1-4612-0567-8_3","DOI":"10.1007\/978-1-4612-0567-8_3"},{"key":"12529_CR2","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s11266-006-4191-3","volume":"42","author":"T Acharya","year":"2006","unstructured":"Acharya T, Chakrabarti C (2006) A survey on lifting-based discrete wavelet transform architectures. VLSI Signal Process 42:321\u2013339. https:\/\/doi.org\/10.1007\/s11266-006-4191-3","journal-title":"VLSI Signal Process"},{"key":"12529_CR3","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1007\/978-3-319-25468-5_14","volume-title":"Digital Signal Processing and Spectral Analysis for Scientists. Signals and Communication Technology","author":"SM Alessio","year":"2016","unstructured":"Alessio SM (2016) Discrete Wavelet Transform. In: Digital Signal Processing and Spectral Analysis for Scientists. Signals and Communication Technology, pp 645\u2013714. https:\/\/doi.org\/10.1007\/978-3-319-25468-5_14"},{"key":"12529_CR4","doi-asserted-by":"publisher","unstructured":"Anumala V, Pullakura RK (2017) Correction of ocular artifacts from EEG by DWT with an improved thresholding. Computer Communication, Networking and Internet Security 157\u2013167.\u00a0https:\/\/doi.org\/10.1007\/978-981-10-3226-4_15","DOI":"10.1007\/978-981-10-3226-4_15"},{"key":"12529_CR5","doi-asserted-by":"publisher","unstructured":"Anutam R (2014) Performance analysis of image denoising with wavelet thresholding methods for different levels of decomposition. The International Journal of Multimedia & Its Applications 6:35\u201346. https:\/\/doi.org\/10.5121\/ijma.2014.6303","DOI":"10.5121\/ijma.2014.6303"},{"key":"12529_CR6","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s10916-018-1069-4","volume":"42","author":"TE Aravindan","year":"2018","unstructured":"Aravindan TE, Seshasayanan R (2018) Denoising brain images with the aid of discrete wavelet transform and monarch butterfly optimization with different noises. J Med Syst 42:207. https:\/\/doi.org\/10.1007\/s10916-018-1069-4","journal-title":"J Med Syst"},{"key":"12529_CR7","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1007\/s11517-019-02014-w","volume":"57","author":"A Bal","year":"2019","unstructured":"Bal A, Banerjee M, Sharma P, Maitra M (2019) An efficient wavelet and curvelet-based PET image denoising technique. Med Biol Eng Comput 57:2567\u20132598. https:\/\/doi.org\/10.1007\/s11517-019-02014-w","journal-title":"Med Biol Eng Comput"},{"key":"12529_CR8","doi-asserted-by":"publisher","DOI":"10.7937\/TCIA.2019.9stoinf1","volume-title":"Cancer: An Exploratory Analysis","author":"L Beer","year":"2019","unstructured":"Beer L, Sahin H, Blazic I, Vargas HA, Veeraraghavan H, Kirby J, Fevrier-Sullivan B, Freymann J, Jaffe C, Conrads T, Maxwell G, Darcy K, Huang E, Sala E (2019) Data from integration of CT-based qualitative and Radiomic features with proteomic variables in patients with high-grade serous ovarian. In: Cancer: An Exploratory Analysis. https:\/\/doi.org\/10.7937\/TCIA.2019.9stoinf1"},{"key":"12529_CR9","doi-asserted-by":"publisher","unstructured":"Biswas M, Om H (2016) A new adaptive image denoising method. Journal of The Institution of Engineers (India): Series B 97:1\u201310. https:\/\/doi.org\/10.1007\/s40031-014-0167-z","DOI":"10.1007\/s40031-014-0167-z"},{"key":"12529_CR10","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1109\/TIM.2012.2224277","volume":"62","author":"X Chen","year":"2013","unstructured":"Chen X, Li X, Wang S, Yang Z, Chen B, He Z (2013) Composite damage detection based on redundant second-generation wavelet transform and fractal dimension tomography algorithm of lamb wave. IEEE Trans Instrum Meas 62:1354\u20131363. https:\/\/doi.org\/10.1109\/TIM.2012.2224277","journal-title":"IEEE Trans Instrum Meas"},{"key":"12529_CR11","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s11771-019-3987-9","volume":"26","author":"B-Q Chen","year":"2019","unstructured":"Chen B-Q, Cui J-g, Xu Q, Shu T, Liu H-l (2019) Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image. J Cent South Univ 26:120\u2013131. https:\/\/doi.org\/10.1007\/s11771-019-3987-9","journal-title":"J Cent South Univ"},{"key":"12529_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15887-3_24","volume-title":"Handbook of Multimedia Information Security: Techniques and Applications","author":"M Diwakar","year":"2019","unstructured":"Diwakar M, Kumar P (2019) Wavelet packet based CT image denoising using bilateral method and Bayes shrinkage rule. In: Singh A, Mohan A (eds) Handbook of Multimedia Information Security: Techniques and Applications. https:\/\/doi.org\/10.1007\/978-3-030-15887-3_24"},{"key":"12529_CR13","doi-asserted-by":"publisher","unstructured":"Fan L, Zhang F, Fan H, Zhang C (2019) Brief review of image denoising techniques. Visual Computing for Industry, Biomedicine, and Art. https:\/\/doi.org\/10.1186\/s42492-019-0016-7","DOI":"10.1186\/s42492-019-0016-7"},{"key":"12529_CR14","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1109\/MEC.2011.6025656","volume-title":"International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)","author":"Z Fengjun","year":"2011","unstructured":"Fengjun Z, Xie C et al (2011) Stationary wavelet denoising based on wavelet coefficients obeying prior distribution in subbands. In: International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), pp 1090\u20131093. https:\/\/doi.org\/10.1109\/MEC.2011.6025656"},{"key":"12529_CR15","doi-asserted-by":"publisher","first-page":"22735","DOI":"10.1007\/s11042-017-5500-5","volume":"77","author":"R Kaur","year":"2018","unstructured":"Kaur R, Juneja M, Mandal AK (2018) A comprehensive review of denoising techniques for abdominal CT images. Multimed Tools Appl 77:22735\u201322770. https:\/\/doi.org\/10.1007\/s11042-017-5500-5","journal-title":"Multimed Tools Appl"},{"key":"12529_CR16","doi-asserted-by":"crossref","unstructured":"Kumar U, Acharya SK (2020) Particle swarm optimized texture based histogram equalization (PSOTHE) for MRI brain image enhancement. Optik 224","DOI":"10.1016\/j.ijleo.2020.165760"},{"key":"12529_CR17","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s13534-015-0182-2","volume":"5","author":"S Lahmiri","year":"2015","unstructured":"Lahmiri S, Boukadoum M (2015) A weighted bio-signal denoising approach using empirical mode decomposition. Biomed-Eng Lett 5:131\u2013139. https:\/\/doi.org\/10.1007\/s13534-015-0182-2","journal-title":"Biomed-Eng Lett"},{"key":"12529_CR18","doi-asserted-by":"publisher","first-page":"166883","DOI":"10.1016\/j.ijleo.2021.166883","volume":"241","author":"H Maria","year":"2021","unstructured":"Maria H, Jossy M, Mazharvizhi J (2021) Analysis of lifting scheme based double density dual tree complex wavelet transform for de-noising medical images. Optik. 241:166883. https:\/\/doi.org\/10.1016\/j.ijleo.2021.166883","journal-title":"Optik."},{"key":"12529_CR19","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.tranon.2015.01.001","volume":"8","author":"MF McNitt-Gray","year":"2015","unstructured":"McNitt-Gray MF, Kim GH, Zhao B et al (2015) Determining the variability of lesion size measurements from CT patient data sets acquired under \u201cno change\u201d conditions. Transl Oncol 8:55\u201364. https:\/\/doi.org\/10.1016\/j.tranon.2015.01.001","journal-title":"Transl Oncol"},{"key":"12529_CR20","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s41571-020-00432-6","volume":"18","author":"M Oudkerk","year":"2020","unstructured":"Oudkerk M, Liu SY, Heuvelmans MA et al (2020) Lung cancer LDCT screening and mortality reduction \u2014 evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 18:135\u2013151. https:\/\/doi.org\/10.1038\/s41571-020-00432-6","journal-title":"Nat Rev Clin Oncol"},{"key":"12529_CR21","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s00521-017-2995-7","volume":"29","author":"G Ozmen","year":"2018","unstructured":"Ozmen G, Ozsen S (2018) A new denoising method for fMRI based on weighted three-dimensional wavelet transform. Neural Comput & Applic 29:263\u2013276. https:\/\/doi.org\/10.1007\/s00521-017-2995-7","journal-title":"Neural Comput & Applic"},{"key":"12529_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2934178","volume-title":"Unpaired image denoising using a generative adversarial network in X-ray CT","author":"HS Park","year":"2019","unstructured":"Park HS, Baek J, You SK et al (2019) Unpaired image denoising using a generative adversarial network in X-ray CT. IEEE Access"},{"key":"12529_CR23","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/ICEOE.2011.6013318","volume-title":"Proceedings of 2011 International Conference on Electronics and Optoelectronics","author":"Y Qiang","year":"2011","unstructured":"Qiang Y (2011) Image denoising based on Haar wavelet transform. In: Proceedings of 2011 International Conference on Electronics and Optoelectronics, vol 3, pp 129\u2013132. https:\/\/doi.org\/10.1109\/ICEOE.2011.6013318"},{"key":"12529_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/EUSIPCO.2016.7760508","volume-title":"24th European Signal Processing Conference","author":"N u Rehman","year":"2016","unstructured":"Rehman N u, Naveed K, Ehsan S et al (2016) Multi-scale image denoising based on goodness of fit (GOF) tests. In: 24th European Signal Processing Conference. https:\/\/doi.org\/10.1109\/EUSIPCO.2016.7760508"},{"key":"12529_CR25","doi-asserted-by":"publisher","unstructured":"Remenyi N, Vidakovic B (2013) Bayesian wavelet shrinkage strategies: a review. Multiscale Signal Anal Model. https:\/\/doi.org\/10.1007\/978-1-4614-4145-8_14","DOI":"10.1007\/978-1-4614-4145-8_14"},{"key":"12529_CR26","doi-asserted-by":"publisher","unstructured":"Safari A, Kong Y (2013) The application of lifting DWT in digital image processing. Adv Mech Electron Eng 178. https:\/\/doi.org\/10.1007\/978-3-642-31528-2_71","DOI":"10.1007\/978-3-642-31528-2_71"},{"key":"12529_CR27","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s40009-014-0238-3","volume":"37","author":"S Satheeskumaran","year":"2014","unstructured":"Satheeskumaran S, Sabrigiriraj M (2014) A new LMS based noise removal and DWT based R-peak detection in ECG signal for biotelemetry applications. Natl Acad Sci Lett 37:341\u2013349. https:\/\/doi.org\/10.1007\/s40009-014-0238-3","journal-title":"Natl Acad Sci Lett"},{"key":"12529_CR28","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s13246-018-0685-0","volume":"41","author":"P Singh","year":"2018","unstructured":"Singh P, Pradhan G (2018) Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering. Australas Phys Eng Sci Med 41:891\u2013904. https:\/\/doi.org\/10.1007\/s13246-018-0685-0","journal-title":"Australas Phys Eng Sci Med"},{"key":"12529_CR29","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/ACT.2009.97","volume-title":"International conference on advances in computing, control, and telecommunication technologies","author":"P Vikhe","year":"2009","unstructured":"Vikhe P, Hamde ST (2009) Wavelet transform based abnormality analysis of heart sound. In: International conference on advances in computing, control, and telecommunication technologies, pp 367\u2013371. https:\/\/doi.org\/10.1109\/ACT.2009.97"},{"key":"12529_CR30","doi-asserted-by":"publisher","first-page":"3998","DOI":"10.1016\/j.proeng.2011.08.749","volume":"15","author":"F Xiao","year":"2011","unstructured":"Xiao F, Zhang Y (2011) A comparative study on thresholding methods in wavelet-based image Denoising. Proc Eng 15:3998\u20134003. https:\/\/doi.org\/10.1016\/j.proeng.2011.08.749","journal-title":"Proc Eng"},{"key":"12529_CR31","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s42979-021-00478-y","volume":"2","author":"F Yasmeen","year":"2021","unstructured":"Yasmeen F, Uddin MS (2021) An efficient watermarking approach based on LL and HH edges of DWT\u2013SVD. SN Comput Sci 2:82. https:\/\/doi.org\/10.1007\/s42979-021-00478-y","journal-title":"SN Comput Sci"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12529-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12529-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12529-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T10:31:48Z","timestamp":1651660308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12529-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,5]]},"references-count":31,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["12529"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12529-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,5]]},"assertion":[{"value":"18 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}