Color gamut transform pairs

Smith, Alvy Ray

doi:10.1145/965139.807361

Cited by 712 publications

(211 citation statements)

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“…29 Moreover, functional staining protected against possible irregularities in blood flow, which might create poorly irrorated areas despite adequate vascularization. 15 As for image segmentation, it was carried out by thresholding images represented in an HSB color space 30 followed by removal of unwanted particles. Although we found this approach satisfactory, it might be possible that other experimental models require a more refined strategy to correctly identify vascular structures.…”

Section: Discussionmentioning

confidence: 99%

A computational approach to compare microvessel distributions in tumors following antiangiogenic treatments

Righi

Giacomini

Lavazza

et al. 2009

Laboratory Investigation

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Experimental approaches currently used to quantify the activity of antiangiogenic treatments in cancer therapy do not generally address the importance of spatial distribution of microvessels in target tissues. We report a new computerized method to assess tumor vascularization by quantifying the distribution of functional microvessels as revealed by in vivo staining with sulfosuccinimidyl-6-(biotinamido) hexanoate. Our approach was based on pixel dilation of digital images of blood vessels and addressed the space-filling property of the vessel layouts. This was practically achieved computing the number of dilation cycles (Halo index) needed to permeate a pre-defined amount of each image. Our approach was validated on human tumor xenografts in nonobese diabetic/severe combined immunodeficient mice treated with the antiangiogenic drug sorafenib. For each experimental model, area normalization allowed the unbiased comparison of several hundreds of images showing different amounts of vascular tissue. In two different tumor types, comparison of Halo values showed statistically significant differences between control and sorafenib-treated samples. Conversely, this effect was not observed in samples from an additional xenograft known to resist the antiangiogenic treatment. By separating the analysis of vessel area from the quantification of vessel distributions, our approach can potentially contribute to a better evaluation of the antiangiogenic or vascular-disrupting activity of new drugs or treatments.

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Section: Discussionmentioning

confidence: 99%

A computational approach to compare microvessel distributions in tumors following antiangiogenic treatments

Righi

Giacomini

Lavazza

et al. 2009

Laboratory Investigation

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“…To create the basic conversion table, first, the HSV (huesaturation-value) values of the recommended colors are calculated using their RGB values by formulas (1)- (3) [22]. According to these formulas, because colors are represented by values of hue (H), saturation (S), and brightness (V), the saturation values of the colors can be obtained.…”

Section: A Basic Conversion Tablementioning

confidence: 99%

A Method for Converting Colors for Color-impaired People, Considering Saturation and Contrast Ratio

Shirogane

Iwase²,

Iwata

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-Because color-impaired people have difficulty in perceiving specific colors, colors used in documents must be converted for them. The colors of documents include the intentions of their designers. That is, designers use different colors for parts of the document that they want to be attractive or distinguishable in some way. In existing conversion methods, however, colors are not converted based on a consideration of the designers' intentions. Hence, we propose a method for converting document colors for color-impaired people in a manner that takes the designers' intentions into account. Here, we define the designers' intentions as "attractiveness" and "distinguishability." In our method, the original colors are classified into three levels, low, middle, and high, based on their saturation. In each saturation level, some colors are defined as available for conversion. Next, the original colors are converted to colors in the corresponding saturation levels. According to the RGB (red-green-blue) values of the original colors, the target colors for conversion can be specified. Then, the contrast ratios of the pair of foreground and background colors are calculated. If the contrast ratios do not satisfy the specific thresholds, the colors are additionally converted to other colors in the corresponding saturation levels. Finally, when the contrast ratios satisfy the specific thresholds, the conversion is complete.

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“…These approaches recognize the benefit of utilizing color correlation to improve algorithm performance. We use the color saturation channel from the hue, saturation, value (HSV) color space hexcone model [43] which is functionally related to color ratios and differences.…”

Section: Channel Cross-correlation Weightmentioning

confidence: 99%

“…,c j,MN T . The color saturation values are computed from these local mean images using the hexcone model [43]. The weight in Equation (8) and the saturation channel are sample-based operations, so these steps can be implemented in parallel by sample.…”

Section: Channel Cross-correlation Weightmentioning

confidence: 99%

Robust super-resolution by fusion of interpolated frames for color and grayscale images

Karch

Hardie

2015

Front. Phys.

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Multi-frame super-resolution (SR) processing seeks to overcome undersampling issues that can lead to undesirable aliasing artifacts in imaging systems. A key factor in effective multi-frame SR is accurate subpixel inter-frame registration. Accurate registration is more difficult when frame-to-frame motion does not contain simple global translation and includes locally moving scene objects. SR processing is further complicated when the camera captures full color by using a Bayer color filter array (CFA). Various aspects of these SR challenges have been previously investigated. Fast SR algorithms tend to have difficulty accommodating complex motion and CFA sensors. Furthermore, methods that can tolerate these complexities tend to be iterative in nature and may not be amenable to real-time processing. In this paper, we present a new fast approach for performing SR in the presence of these challenging imaging conditions. We refer to the new approach as Fusion of Interpolated Frames (FIF) SR. The FIF SR method decouples the demosaicing, interpolation, and restoration steps to simplify the algorithm. Frames are first individually demosaiced and interpolated to the desired resolution. Next, FIF uses a novel weighted sum of the interpolated frames to fuse them into an improved resolution estimate. Finally, restoration is applied to improve any degrading camera effects. The proposed FIF approach has a lower computational complexity than many iterative methods, making it a candidate for real-time implementation. We provide a detailed description of the FIF SR method and show experimental results using synthetic and real datasets in both constrained and complex imaging scenarios. Experiments include airborne grayscale imagery and Bayer CFA image sets with affine background motion plus local motion.

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Color gamut transform pairs

Cited by 712 publications

References 0 publications

A computational approach to compare microvessel distributions in tumors following antiangiogenic treatments

A computational approach to compare microvessel distributions in tumors following antiangiogenic treatments

A Method for Converting Colors for Color-impaired People, Considering Saturation and Contrast Ratio

Robust super-resolution by fusion of interpolated frames for color and grayscale images

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