{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:25:09Z","timestamp":1740144309706,"version":"3.37.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"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":["Int J CARS"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s11548-021-02449-3","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T12:03:54Z","timestamp":1626264234000},"page":"1469-1480","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reducing reconstruction error of classified textural patches by integration of random forests and coupled dictionary nonlinear regressors: with applications to super-resolution of abdominal CT images"],"prefix":"10.1007","volume":"16","author":[{"given":"Mahdieh","family":"Akbari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0177-3227","authenticated-orcid":false,"given":"Amir Hossein","family":"Foruzan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"2449_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2017.11.012","author":"S Wei","year":"2018","unstructured":"Wei S, Zhou X, Wu W, Pu Q, Wang Q, Yang X (2018) Medical image super-resolution by using multi-dictionary and random forest. Sustain Cities Soc. https:\/\/doi.org\/10.1016\/j.scs.2017.11.012","journal-title":"Sustain Cities Soc"},{"key":"2449_CR2","doi-asserted-by":"publisher","first-page":"25897","DOI":"10.1109\/ACCESS.2019.2900125","volume":"7","author":"Y Li","year":"2019","unstructured":"Li Y, Song B, Guo J, Du X, Guizani M (2019) Super-resolution of brain MRI images using overcomplete dictionaries and nonlocal similarity. IEEE Access 7:25897\u201325907","journal-title":"IEEE Access"},{"key":"2449_CR3","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1080\/24699322.2018.1560092","volume":"24","author":"F Zhang","year":"2019","unstructured":"Zhang F, Wu Y, Xiao Z, Geng L, Wu J, Wen J, Wang W, Liu P (2019) Super resolution reconstruction for medical image based on adaptive multi-dictionary learning and structural self-similarity. Comput Assist Surg 24:81\u201388","journal-title":"Comput Assist Surg"},{"key":"2449_CR4","first-page":"43005","volume":"27","author":"H Li","year":"2018","unstructured":"Li H, Lam K-M, Li D (2018) Joint maximum purity forest with application to image super-resolution. J Electron Imaging 27:43005","journal-title":"J Electron Imaging"},{"key":"2449_CR5","first-page":"39","volume":"35","author":"A Grabner","year":"2017","unstructured":"Grabner A, Poier G, Opitz M, Schulter S, Roth PM (2017) Loss-specific training of random forests for super-resolution. Comput Complex 35:39","journal-title":"Comput Complex"},{"key":"2449_CR6","doi-asserted-by":"crossref","unstructured":"Huang JJ, Liu T, Luigi P, Dragotti T (2017) Stathaki, SRHRF+: Self-example enhanced single image super-resolution using hierarchical random forests, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Work., pp. 71\u201379.","DOI":"10.1109\/CVPRW.2017.144"},{"key":"2449_CR7","doi-asserted-by":"publisher","first-page":"207","DOI":"10.3390\/s19010207","volume":"19","author":"P Gu","year":"2019","unstructured":"Gu P, Jiang C, Ji M, Zhang Q, Ge Y, Liang D, Liu X, Yang Y, Zheng H, Hu Z (2019) Low-dose computed tomography image super-resolution reconstruction via random forests. Sensors 19:207","journal-title":"Sensors"},{"key":"2449_CR8","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.nima.2019.02.042","volume":"927","author":"Z Hu","year":"2019","unstructured":"Hu Z, Wang Y, Zhang X, Zhang M, Yang Y, Liu X, Zheng H, Liang D (2019) Super-resolution of PET image based on dictionary learning and random forests. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 927:320\u2013329","journal-title":"Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip"},{"key":"2449_CR9","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.image.2018.12.001","volume":"72","author":"H Li","year":"2019","unstructured":"Li H, Lam K-M, Wang M (2019) Image super-resolution via feature-augmented random forest. Signal Process Image Commun 72:25\u201334","journal-title":"Signal Process Image Commun"},{"key":"2449_CR10","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1007\/s10766-017-0551-9","volume":"46","author":"X Yang","year":"2018","unstructured":"Yang X, Wu W, Yan B, Wang H, Zhou K, Liu K (2018) Infrared image super-resolution with parallel random Forest. Int J Parallel Program 46:838\u2013858","journal-title":"Int J Parallel Program"},{"key":"2449_CR11","doi-asserted-by":"crossref","unstructured":"Zhi-SongL, Siu WC (2018) Cascaded random forests for fast image super-resolution, in: 2018 25th IEEE Int. Conf. Image Process, pp. 2531\u20132535","DOI":"10.1109\/ICIP.2018.8451349"},{"key":"2449_CR12","doi-asserted-by":"publisher","first-page":"543","DOI":"10.3390\/app9030543","volume":"9","author":"Z Lu","year":"2019","unstructured":"Lu Z, Wu C, Yu X (2019) Learning weighted forest and similar structure for image super resolution. Appl Sci 9:543","journal-title":"Appl Sci"},{"key":"2449_CR13","first-page":"1","volume":"8","author":"C Jiang","year":"2018","unstructured":"Jiang C, Zhang Q, Fan R, Hu Z (2018) Super-resolution ct image reconstruction based on dictionary learning and sparse representation. Sci Rep 8:1\u201310","journal-title":"Sci Rep"},{"key":"2449_CR14","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","volume":"18","author":"J-G Lee","year":"2017","unstructured":"Lee J-G, Jun S, Cho Y-W, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18:570\u2013584","journal-title":"Korean J Radiol"},{"key":"2449_CR15","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19:2861\u20132873","journal-title":"IEEE Trans Image Process"},{"key":"2449_CR16","unstructured":"Lin D, Tang X (2005) Coupled space learning of image style transformation, in: Tenth IEEE Int. Conf. Comput. Vis. Vol. 1, pp. 1699\u20131706"},{"key":"2449_CR17","unstructured":"Wang S, Zhang L, Liang Y, Pan Q (2012) Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis, in: 2012 IEEE Conf. Comput. Vis. Pattern Recognit, pp. 2216\u20132223"},{"key":"2449_CR18","doi-asserted-by":"crossref","unstructured":"Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition, in: 2010 IEEE Int Conf Image Process, pp. 1601\u20131604","DOI":"10.1109\/ICIP.2010.5652363"},{"key":"2449_CR19","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.patrec.2019.01.001","volume":"130","author":"J Wang","year":"2020","unstructured":"Wang J, Li J, Han X-H, Lin L, Hu H, Xu Y, Chen Q, Iwamoto Y, Chen Y-W (2020) Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions. Pattern Recognit Lett 130:207\u2013215","journal-title":"Pattern Recognit Lett"},{"key":"2449_CR20","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s11548-017-1671-9","volume":"13","author":"Y Xu","year":"2018","unstructured":"Xu Y, Lin L, Hu H, Wang D, Zhu W, Wang J, Han X-H, Chen Y-W (2018) Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images. Int J Comput Assist Radiol Surg 13:151\u2013164","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2449_CR21","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/1250538","author":"C Li","year":"2016","unstructured":"Li C, Deng K, Sun J, Wang H (2016) Compressed sensing, pseudodictionary-based, superresolution reconstruction. J Sensors. https:\/\/doi.org\/10.1155\/2016\/1250538","journal-title":"J Sensors"},{"key":"2449_CR22","doi-asserted-by":"crossref","unstructured":"Wang Y, Teng Q, He X, Feng J, Zhang T (2018) Ct-image super resolution using 3d convolutional neural network, ArXiv Prepr. ArXiv1806.09074","DOI":"10.1016\/j.cageo.2019.104314"},{"key":"2449_CR23","doi-asserted-by":"publisher","first-page":"102819","DOI":"10.1016\/j.jvcir.2020.102819","volume":"70","author":"G Amaranageswarao","year":"2020","unstructured":"Amaranageswarao G, Deivalakshmi S, Ko SB (2020) Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network. J Vis Commun Image Represent 70:102819","journal-title":"J Vis Commun Image Represent"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02449-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02449-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02449-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T12:23:18Z","timestamp":1628598198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02449-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,14]]},"references-count":23,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["2449"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02449-3","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"type":"print","value":"1861-6410"},{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2021,7,14]]},"assertion":[{"value":"7 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declares that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All human and animal studies have been approved and performed in accordance with ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}}]}}