Multiresolution approach on Medical Image Fusion by Modified Local Energy Article Swipe
Rekha R Nair
,
Tina Babu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2173233/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2173233/v1
Human and machine perception data that are not redundant are very important in the medical field for diagnosis and treatment. The proposed work fuses medical images to extract valuable necessary information from dissimilar images to a single image in the Wavelet domain using a novel Modified Local Energy(MLE) fusion rule termed ModifiedLocal Energy Image Fusion(MLEIF). The image edges are essential information points that are used to display a clear visual image structure. Modified Local energy helps to provide edge characteristics more clearly in the fusion outcome than a single pixel-based fusion rule. The denoising property of the local energy is the additional advantage of the proposed fusion. The similarity of the fused image with the source images is improved by B-Spline registration in the pre-processing stage. Finally, the fused image is created with all the corresponding coefficients by transforming the inverse wavelet. SVM-based contouring of the lesion part helps observes to identify the lesion part from the fused image. Carried out experiments on real-time and standard data sets with expert radiologist subjective evaluation and quantitative analysis by well-known non-reference performance measures.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2173233/v1
- https://www.researchsquare.com/article/rs-2173233/latest.pdf
- OA Status
- green
- Cited By
- 3
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307877624
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Raw OpenAlex JSON
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https://openalex.org/W4307877624Canonical identifier for this work in OpenAlex
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https://doi.org/10.21203/rs.3.rs-2173233/v1Digital Object Identifier
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Multiresolution approach on Medical Image Fusion by Modified Local EnergyWork title
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preprintOpenAlex work type
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-31Full publication date if available
- Authors
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Rekha R Nair, Tina BabuList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-2173233/v1Publisher landing page
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https://www.researchsquare.com/article/rs-2173233/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-2173233/latest.pdfDirect OA link when available
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Artificial intelligence, Image fusion, Computer science, Computer vision, Pattern recognition (psychology), Fusion rules, Wavelet, Energy (signal processing), Pixel, Image (mathematics), Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.proposed | 22, 106 |
| abstract_inverted_index.standard | 166 |
| abstract_inverted_index.valuable | 29 |
| abstract_inverted_index.wavelet. | 142 |
| abstract_inverted_index.SVM-based | 143 |
| abstract_inverted_index.advantage | 103 |
| abstract_inverted_index.denoising | 94 |
| abstract_inverted_index.diagnosis | 18 |
| abstract_inverted_index.essential | 60 |
| abstract_inverted_index.important | 12 |
| abstract_inverted_index.measures. | 181 |
| abstract_inverted_index.necessary | 30 |
| abstract_inverted_index.real-time | 164 |
| abstract_inverted_index.redundant | 9 |
| abstract_inverted_index.additional | 102 |
| abstract_inverted_index.contouring | 144 |
| abstract_inverted_index.dissimilar | 33 |
| abstract_inverted_index.evaluation | 173 |
| abstract_inverted_index.perception | 4 |
| abstract_inverted_index.similarity | 109 |
| abstract_inverted_index.structure. | 72 |
| abstract_inverted_index.subjective | 172 |
| abstract_inverted_index.treatment. | 20 |
| abstract_inverted_index.well-known | 178 |
| abstract_inverted_index.Energy(MLE) | 48 |
| abstract_inverted_index.experiments | 162 |
| abstract_inverted_index.information | 31, 61 |
| abstract_inverted_index.performance | 180 |
| abstract_inverted_index.pixel-based | 90 |
| abstract_inverted_index.radiologist | 171 |
| abstract_inverted_index.coefficients | 137 |
| abstract_inverted_index.quantitative | 175 |
| abstract_inverted_index.registration | 122 |
| abstract_inverted_index.transforming | 139 |
| abstract_inverted_index.ModifiedLocal | 52 |
| abstract_inverted_index.corresponding | 136 |
| abstract_inverted_index.non-reference | 179 |
| abstract_inverted_index.Fusion(MLEIF). | 55 |
| abstract_inverted_index.pre-processing | 125 |
| abstract_inverted_index.characteristics | 80 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5076530957 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I4400573291, https://openalex.org/I8977528 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.63604051 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |