ArticleJournal of medical physics
Multi-Modality Medical Image Fusion Using Cross-Bilateral Filter and Neuro-Fuzzy Approach.
Article in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
- Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Context: The proposed technique uses the edge-preserving capabilities of cross-bilateral filter (CBF) and artificial intelligence technique adaptive neuro-fuzzy inference system (ANFIS) to fuse multi-modality medical images. Aims: The aim is to present the unlike information onto a single image as each modality of medical image contains the unalike domain of information. Settings and Design: First, the multi-modality medical images are decomposed using CBF by tuning its parameters: radiometric and geometric sigma producing CBF component and detail component. This detail is fed to ANFIS for fusion. On the other hand, the sub-bands obtained from DWT are fused using average rule. Reconstruction method gives final image. Subjects and Methods: ANFIS is used to train the Sugeno systems using neuro-adaptive learning. The fuzzy inference system in the ANFIS is used to define fuzzy rules for fusion. On the other hand, bior2.2 is used to decompose the source images. Statistical Analysis Used: The performance is verified on the Harvard database with five cases, and the results are equated with conventional metrics, objective metrics as well as visual inspection. The statistics of the metrics values is visualized in the form of column chart. Results: In Case 1, better results are obtained for all conventional metrics except for average gradient (AG) and spatial frequency (SF). It also achieved preferred objective metric values. In Case 2, all metrics except AG, mutual information, fusion symmetry, and SF are better values among all methods. In Cases 3, 4, and 5, all the metrics have achieved desired values. Conclusions: Experiments conclude that conventional, objective, visual evaluation shows best results for Cases 1, 3, 4, and 5.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.