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Ni, “Multimodality medical imagefusion based on multiscale geometric analysis of contourlettransform,” Neurocomputing, vol. 72, no. 1-3, pp. 203–211, 2008" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "Based on Sparse Representation and Feature Extraction in Clinical Image Fusion" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jstj.2018.v3.i1.183", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Based on Sparse Representation and Feature Extraction in Clinical Image Fusion", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jstj.2018.v3.i1.183" } ], "agents": [ { "role": "author", "name": { "given": "", "family": "P.RAVI KUMAR" }, "sequence": "first" }, { "role": "author", "name": { "given": "", "family": "K.RAMBABU" }, "sequence": "additional" }, { "role": "author", "name": { "given": "LAM", "family": "VENKATESH" }, "sequence": "additional" }, { "role": "author", "name": { "given": "BOLLIPALLI", "family": "PRADEEP" }, "sequence": "additional" }, { "role": "author", "name": { "given": "TONY", "family": "RHODES KOTCHERA" }, "sequence": "additional" }, { "role": "author", "name": { "given": "", "family": "B.V.R.SAI KRISHN" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2018-01-16", "date_type": "PublicationDate", "online": "2018-01-16" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "03", "issue": "01", "pages": { "first": "68", "last": "74" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/183", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/183/162", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/183/3434", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/183/162", "purpose": "similarity-checking" } ], "abstract": { "value": "There are several advantages to using minimal depiction in a narrative multi-scale geometric assessment contraption over normal image depiction methods. Regardless, the typical poor representation fails to consider the unique structure and thetime-multifaceted architecture. To address all of these difficulties at the same time, a novel blend segment for multimodal clinical images relying on poor depiction and decision direct is now being considered. In order to save more essentiality and edge information, three decision maps are organized, including the structure information map (SM) and the essentialness information map (EM). For example, SM has the local structure that is derived from a Gaussian Laplacian (LOG) and it also contains the essentiality and imperativeness movement characteristic that is defined by the mean square deviation (EM). In order to speed up computations, decision control is introduced to the depiction-based technique. More structure and imperativeness information maybe extracted from source images using this proposed method, which further enhances the notion of the combined results. 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Basu, “Cross-scale coefficient selectionfor volumetric medical image fusion,” IEEE Transactionson Biomedical Engineering, vol. 60, no. 4, pp. 1069–1079, 2013" }, { "key": "ref8", "doi": "10.1016/j.inffus.2007.04.003", "unstructured": "Z. Wang and Y. Ma, “Medical image fusion using m-PCNN,”Information Fusion, vol. 9, no. 2, pp. 176–185, 2008" }, { "key": "ref9", "doi": "10.1016/j.eswa.2012.09.011", "unstructured": "G. Bhatnagar, Q. M. Jonathan Wu, and Z. Liu, “Human visualsystem inspiredmulti-modalmedical image fusion framework,”Expert Systems with Applications, vol. 40, no. 5, pp. 1708–1720,2013" }, { "key": "ref10", "doi": "10.1016/j.neucom.2008.02.025", "unstructured": "L. Yang, B. L. Guo, and W. Ni, “Multimodality medical imagefusion based on multiscale geometric analysis of contourlettransform,” Neurocomputing, vol. 72, no. 1-3, pp. 203–211, 2008" }, { "key": "ref11", "doi": "10.1016/j.inffus.2015.03.003", "unstructured": "M. Kim, D. K. Han, and H. 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Liu, and Z. F. Wang, “Medical image fusion by combiningnonsubsampled contourlet transform and sparse representation,”in Pattern Recognition, vol. 484 of Communicationsin Computer and Information Science, pp. 372–381, Springer,Berlin, Germany, 2014" }, { "key": "ref16", "doi": "10.1016/j.inffus.2012.01.008", "unstructured": "H. Yin, S. Li, and L. Fang, “Simultaneous image fusionand super-resolution using sparse representation,” InformationFusion, vol. 14, no. 3, pp. 229–240, 2013" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-08", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/183", "sha256_before": null, "sha256_after": "354844cd11dcef817c330489b66801e60161bc6041adb91a0ee38a6417744774" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:57", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jstj.2018.v3.i1.183", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Based on Sparse Representation and Feature Extraction in Clinical Image Fusion", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jstj.2018.v3.i1.183" } ], "agents": [ { "role": "author", "name": { "given": "", "family": "P.RAVI KUMAR" }, "sequence": "first" }, { "role": "author", "name": { "given": "", "family": "K.RAMBABU" }, "sequence": "additional" }, { "role": "author", "name": { "given": "LAM", "family": "VENKATESH" }, "sequence": "additional" }, { "role": "author", "name": { "given": "BOLLIPALLI", "family": "PRADEEP" }, "sequence": "additional" }, { "role": "author", "name": { "given": "TONY", "family": "RHODES KOTCHERA" }, "sequence": "additional" }, { "role": "author", "name": { "given": "", "family": "B.V.R.SAI KRISHN" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2018-01-16", "date_type": "PublicationDate", "online": "2018-01-16" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "03", "issue": "01", "pages": { "first": "68", "last": "74" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/183", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/183/162", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/183/3434", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/183/162", "purpose": "similarity-checking" } ], "abstract": { "value": "There are several advantages to using minimal depiction in a narrative multi-scale geometric assessment contraption over normal image depiction methods. Regardless, the typical poor representation fails to consider the unique structure and thetime-multifaceted architecture. To address all of these difficulties at the same time, a novel blend segment for multimodal clinical images relying on poor depiction and decision direct is now being considered. In order to save more essentiality and edge information, three decision maps are organized, including the structure information map (SM) and the essentialness information map (EM). For example, SM has the local structure that is derived from a Gaussian Laplacian (LOG) and it also contains the essentiality and imperativeness movement characteristic that is defined by the mean square deviation (EM). In order to speed up computations, decision control is introduced to the depiction-based technique. More structure and imperativeness information maybe extracted from source images using this proposed method, which further enhances the notion of the combined results. According to the results of 36 studies including CT/MRI, MR-T1/MR-T2, and CT/PET images, the technique subject to SR and SEM outmanoeuvres five of the most advanced approaches.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2018-01-16", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.4236/cs.2016.78139", "unstructured": "P. S. Gomati and B. Kalaavathi, “Multimodal medical imagefusion in non-subsampled contourlet transform domain,” Circuits& Systems, vol. 7, no. 8, pp. 1598–1610, 2016" }, { "key": "ref2", "doi": "10.1007/s00330-010-1718-6", "unstructured": "M. D. C. Vald´es Hern´andez, K. J. Ferguson, F.M. Chappell, andJ. M. Wardlaw, “New multispectral MRI data fusion techniquefor white matter lesion segmentation: method and comparisonwith thresholding in FLAIR images,” European Radiology, vol.20, no. 7, pp. 1684–1691, 2010" }, { "key": "ref3", "doi": "10.1016/j.eswa.2014.05.043", "unstructured": "Z. Liu, H. Yin, Y. 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