Matches in SemOpenAlex for { <https://semopenalex.org/work/W4294286368> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W4294286368 endingPage "82" @default.
- W4294286368 startingPage "65" @default.
- W4294286368 abstract "The chapter signifies the benefits of using AI methods for medical image fusion of different modalities. The modality can be CT, MR-T1, MR-T2, PET, etc. depending on the suspected malignant region. The aim of fusion is to collaborate each modality's best information into a single image called as fused image. Depicting the decisive information on the resultant image (fused image) will help the oncologist in demarcation of the tumor volume. The image fusion process can be done upfront, i.e., carried on the whole image directly and at once. The other way is to decompose the images first using the decomposition methods like DWT, CWT, ST, HIS, CBF, etc. After the decomposition, fusion operation is carried out on each decomposed part. In the end, the decomposed fused parts form a single image using reconstruction. This chapter addresses the multimodality medical image fusion using Artificial Intelligence techniques like Fuzzy Logic and Adaptive Neuro-Fuzzy Inference System (ANFIS). For the evaluation of fusion results, researchers have used metrics: Conventional metrics like API, AG, ENTROPY, MI, etc. and objective metrics like QAB/F, LAB/F, and NAB/F. In this study, only conventional metrics are calculated. The study reveals that the AI techniques not only give better results, but their learning capabilities will likely make the future work self-driven." @default.
- W4294286368 created "2022-09-02" @default.
- W4294286368 creator A5038594490 @default.
- W4294286368 creator A5080332896 @default.
- W4294286368 date "2022-01-01" @default.
- W4294286368 modified "2023-10-18" @default.
- W4294286368 title "Role of AI techniques in enhancing multi-modality medical image fusion results" @default.
- W4294286368 cites W1973880112 @default.
- W4294286368 cites W1974314515 @default.
- W4294286368 cites W1980382026 @default.
- W4294286368 cites W2017896827 @default.
- W4294286368 cites W2029549845 @default.
- W4294286368 cites W2048849384 @default.
- W4294286368 cites W2059111949 @default.
- W4294286368 cites W2107500826 @default.
- W4294286368 cites W2155662634 @default.
- W4294286368 cites W2157857559 @default.
- W4294286368 cites W2173635503 @default.
- W4294286368 cites W2289413700 @default.
- W4294286368 cites W2411377185 @default.
- W4294286368 cites W2461851607 @default.
- W4294286368 cites W2474462684 @default.
- W4294286368 cites W2601815223 @default.
- W4294286368 cites W2782259231 @default.
- W4294286368 cites W2788798270 @default.
- W4294286368 cites W2794692403 @default.
- W4294286368 cites W2891356005 @default.
- W4294286368 cites W2891857663 @default.
- W4294286368 cites W2893335971 @default.
- W4294286368 cites W2914139557 @default.
- W4294286368 cites W2919129786 @default.
- W4294286368 cites W2958150439 @default.
- W4294286368 cites W2996165363 @default.
- W4294286368 cites W3020146971 @default.
- W4294286368 cites W811864384 @default.
- W4294286368 doi "https://doi.org/10.1016/b978-0-323-99864-2.00003-2" @default.
- W4294286368 hasPublicationYear "2022" @default.
- W4294286368 type Work @default.
- W4294286368 citedByCount "0" @default.
- W4294286368 crossrefType "book-chapter" @default.
- W4294286368 hasAuthorship W4294286368A5038594490 @default.
- W4294286368 hasAuthorship W4294286368A5080332896 @default.
- W4294286368 hasConcept C115961682 @default.
- W4294286368 hasConcept C124681953 @default.
- W4294286368 hasConcept C138885662 @default.
- W4294286368 hasConcept C144024400 @default.
- W4294286368 hasConcept C153180895 @default.
- W4294286368 hasConcept C154945302 @default.
- W4294286368 hasConcept C158525013 @default.
- W4294286368 hasConcept C18903297 @default.
- W4294286368 hasConcept C2779903281 @default.
- W4294286368 hasConcept C2780226545 @default.
- W4294286368 hasConcept C31972630 @default.
- W4294286368 hasConcept C36289849 @default.
- W4294286368 hasConcept C41008148 @default.
- W4294286368 hasConcept C41895202 @default.
- W4294286368 hasConcept C58166 @default.
- W4294286368 hasConcept C69744172 @default.
- W4294286368 hasConcept C86803240 @default.
- W4294286368 hasConceptScore W4294286368C115961682 @default.
- W4294286368 hasConceptScore W4294286368C124681953 @default.
- W4294286368 hasConceptScore W4294286368C138885662 @default.
- W4294286368 hasConceptScore W4294286368C144024400 @default.
- W4294286368 hasConceptScore W4294286368C153180895 @default.
- W4294286368 hasConceptScore W4294286368C154945302 @default.
- W4294286368 hasConceptScore W4294286368C158525013 @default.
- W4294286368 hasConceptScore W4294286368C18903297 @default.
- W4294286368 hasConceptScore W4294286368C2779903281 @default.
- W4294286368 hasConceptScore W4294286368C2780226545 @default.
- W4294286368 hasConceptScore W4294286368C31972630 @default.
- W4294286368 hasConceptScore W4294286368C36289849 @default.
- W4294286368 hasConceptScore W4294286368C41008148 @default.
- W4294286368 hasConceptScore W4294286368C41895202 @default.
- W4294286368 hasConceptScore W4294286368C58166 @default.
- W4294286368 hasConceptScore W4294286368C69744172 @default.
- W4294286368 hasConceptScore W4294286368C86803240 @default.
- W4294286368 hasLocation W42942863681 @default.
- W4294286368 hasOpenAccess W4294286368 @default.
- W4294286368 hasPrimaryLocation W42942863681 @default.
- W4294286368 hasRelatedWork W1988829224 @default.
- W4294286368 hasRelatedWork W2057200091 @default.
- W4294286368 hasRelatedWork W2341735422 @default.
- W4294286368 hasRelatedWork W2359631359 @default.
- W4294286368 hasRelatedWork W2419576664 @default.
- W4294286368 hasRelatedWork W2767265881 @default.
- W4294286368 hasRelatedWork W3006630499 @default.
- W4294286368 hasRelatedWork W3007420330 @default.
- W4294286368 hasRelatedWork W3132484442 @default.
- W4294286368 hasRelatedWork W4312613727 @default.
- W4294286368 isParatext "false" @default.
- W4294286368 isRetracted "false" @default.
- W4294286368 workType "book-chapter" @default.