2008 IEEE National Aerospace and Electronics Conference 2008
DOI: 10.1109/naecon.2008.4806563
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Mutual Information Metric Evaluation for PET/MRI Image Fusion

Abstract: Image fusion developments has paved way for new approaches like image overlay, image sharpening, and image cueing through pixel, feature, or region/shape combinations. The applicability of these new techniques differs on the image content, contextual information, and generalized metrics of image fusion gain. An image fusion gain can be assessed relative to information gain or entropy reduction. In this paper, we are interested in exploring the performance metric evaluation of the fused images. The metric evalu… Show more

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Cited by 28 publications
(10 citation statements)
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“…This discussion is important because MIS and NMI have been commonly recommended for inter-modality neuroimaging registrations [41][42][43]. In comparative studies [44,45], as many as 16 retrospective registration methods from 12 research groups worldwide were evaluated with respect to the MIS and NMI performance in rigid multimodal brain images registration.…”
Section: Discussionmentioning
confidence: 99%
“…This discussion is important because MIS and NMI have been commonly recommended for inter-modality neuroimaging registrations [41][42][43]. In comparative studies [44,45], as many as 16 retrospective registration methods from 12 research groups worldwide were evaluated with respect to the MIS and NMI performance in rigid multimodal brain images registration.…”
Section: Discussionmentioning
confidence: 99%
“…In astronomy and in remote sensing, multisensory fusion is used to achieve high spatial resolutions by combining images from two sensors, one of which has high spatial resolution and the other one high spectral resolution. Also in medical imaging like simultaneous evaluation of CT, MRI and PET images [4]. Use of mutlisensor fusion of visible and infrared images have appeared in military, security and surveillance areas.…”
Section: Introductionmentioning
confidence: 99%
“…The technique has many applications in a variety of domains including geospatial information systems (GIS), wireless sensor networks, bioinformatics, and others. There have been many proposed metrics by which one can evaluate the quality of a fused signal [19], especially fused images [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]. However, it is not well known which metric provides the best performance evaluation for a given fusion algorithm since each algorithm has specific applications, advantages, and shortcomings.…”
Section: Introductionmentioning
confidence: 99%