Fuzzy image fusion application in detecting coronary layers in IVUS pictures

Ashoori, Ahmad; Moshiri, Behzad; Setarehdan, Seyed Kamaledin

doi:10.1109/isccsp.2008.4537185

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Disciplines that have benefited from image fusion techniques include remote sensing [7], computer vision [8], satellite imagery [7], night vision [8, 9], weather forecasting [8], forensic science [10], and medical imaging [11, 12]. Multiple sensors are used to acquire two or more images of the same scene, where different, more specific information is contained in each of the images.…”

Section: Introductionmentioning

confidence: 99%

Improving Secondary Ion Mass Spectrometry Image Quality with Image Fusion

Tarolli

Jackson

Winograd

2014

J. Am. Soc. Mass Spectrom.

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The spatial resolution of chemical images acquired with cluster secondary ion mass spectrometry (SIMS) is limited not only by the size of the probe utilized to create the images, but also by detection sensitivity. As the probe size is reduced to below 1 µm, for example, a low signal in each pixel limits lateral resolution due to counting statistics considerations. Although it can be useful to implement numerical methods to mitigate this problem, here we investigate the use of image fusion to combine information from scanning electron microscope (SEM) data with chemically resolved SIMS images. The advantage of this approach is that the higher intensity and, hence, spatial resolution of the electron images can help to improve the quality of the SIMS images without sacrificing chemical specificity. Using a pan-sharpening algorithm, the method is illustrated using synthetic data, experimental data acquired from a metallic grid sample, and experimental data acquired from a lawn of algae cells. The results show that up to an order of magnitude increase in spatial resolution is possible to achieve. A cross-correlation metric is utilized for evaluating the reliability of the procedure.

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Section: Introductionmentioning

confidence: 99%

Improving Secondary Ion Mass Spectrometry Image Quality with Image Fusion

Tarolli

Jackson

Winograd

2014

J. Am. Soc. Mass Spectrom.

View full text Add to dashboard Cite

show abstract

“…The main categories are determined by the level at which the fusion is actually executed Zhang (2010). The methodologies are designed on the basis of the following mathematical fields: statistical methods (e.g., using aggregation operators, such as the MinMax method Blum (2005)), estimation theory Loza et al (2010), fuzzy methods (see Singh et al (2004); Ranjan et al (2005); Ashoori et al (2008)), optimization methods (e.g., neural networks, genetic algorithms Mumtaz & Majid (2008)) and multiscale decomposition methods, which incorporate various transforms, e.g., discrete wavelet transforms (for a classification of these methods see Piella (2003); a classification of wavelet-based image fusion methods can be found in Amolins et al (2007), and for applications for blurred and unregistered images, refer toŠroubek & Flusser (2005); Šroubek & Zítová (2006)). The choice of a fusion methodology is basically influenced by parameters relating to the type of degradation operators d i , the occurrence of noise and the type of outputs of the preprocessing analysis.…”

Section: Introductionmentioning

confidence: 99%