基于图像几何变换映射的色盲矫正方法

为了提高色盲患者分辨色彩的能力,提出了基于图像几何变换映射的色盲矫正方法。首先根据图像中颜色面两侧的颜色比例对颜色空间各个平面进行相应的几何变换,进而划分不同的颜色映射区域,通过颜色变换,生成色盲患者较易分辨颜色的图像。实验表明,该方法可以改善色盲患者对原本难以区分的颜色的分辨能力,同时计算速度快,有望满足实时处理的需要,性能上优于已有的方法。     

色觉异常的数学模型

色盲是一种遗传性疾病,由于色觉机理尚未研究清楚,一向不能矫治。为了解除色盲患者的苦脑,增加他们在升学、就业和职业选择中的权利,我们通过色觉过程的拓扑学研究,建立了色觉异常的突变模型;论证了色盲矫治机理;研制了色盲电脑诊断矫正检测仪。为色盲眼镜的批量生产和患者的配镜提供准确的矫正曲线。     

浅谈先天性色觉障碍

先天性色觉障碍通常称为色盲，它不能分辩自然光谱中的各种颜色或某种颜色。而对颜色的辨别能力差的则称色弱，它与色盲的界限一般不易严格区分，只不过轻重程度不同罢了。色盲又分为全色盲和部分色盲（红色盲、绿色盲、蓝黄色盲等）。色弱包括全色弱和部分色弱（红色弱、绿色弱、蓝黄色弱等）。1全色菌属于完全性视锥细胞功能障碍，与夜盲（视杆细胞功能障碍）恰好相反，患者尤喜暗、畏光，表现为昼盲。七彩世界在其眼中是一片灰暗，如同现黑白电视一般，仅有明暗之分，而无颜色差别。而且所见红色发暗、蓝色光亮，此外还有视力差、弱视、…     

问题解答

问:为什么色盲基因只用一个隐性基因b表示? 答:色盲有许多类型。课本上讲的实际上是红绿色盲。据研究,人的色觉是由于三种不同的视蛋白和维生素A复合形成红、绿、蓝三种色素,因而能分别吸收红、绿、蓝光而引起视觉。如果缺乏某种色素,不能分辨某种颜色,称为部分色盲,如红色盲、绿色盲等。如色素全部缺乏,则完全不能分辨颜色,称全色盲。最常见的红绿色盲是一种x连锁隐性遗传病,患者不能     

水胁迫下多角度幼龄檀香图像颜色变化分析及含水率反演

干旱与水淹胁迫是植物遭受的主要非生物胁迫,对植物的生理活动造成严重影响.本研究基于单反相机获取幼龄檀香的纵向和冠层叶片图像,使用分割算法提取叶片和颜色特征,然后讨论两种胁迫条件下多角度檀香叶片颜色变化及含水率反演.结果表明:干旱组在胁迫前期(前6d)叶片亮度降低,绿色分量增加,之后叶片亮度增加,绿色分量降低;水淹组叶片在整个胁迫周期亮度持续降低,黄色分量增加;对照组则与干旱组的变化趋势类似,但拐点出现的时间较晚.当叶片含水率在50%~70%时,随着含水率的增加,彩色图像的红(R)、绿(G)、蓝(B)通道值均会减小;但当叶片含水率小于40%时,会出现R通道值大于G通道值的现象.在使用极限学习机反演含水率时,校正后的颜色分量对拟合优度及预测精度均有所提高.纵向图像更适合用来反演叶片的含水量,决定系数和平均绝对百分比误差分别为0.8352和2.3%;而冠层图像对叶片等效水厚度的表达更准确,上述指标分别为0.7924和9.3%.     

科研快讯

导入人类感光色素基因的小鼠从此告别色盲时代[《Science》2007年3月23日报道:现在科学家们通过转基因手段,赋予小鼠灵长类所特有的第三种感光色素,拥有这种感光色素的小鼠将不再是色盲。]我们知道,在哺乳动物中,只有灵长类能够看到五颜六色,而其它绝大多数哺乳动物都是色盲。可是现在科学家们通过转基因手段,赋予小鼠灵长类所特有的第三种感光色素,拥有这种感光色素的小鼠将不再是色盲。3月23日的《科学》杂志报道了该研究成果。灵长类之所以能够区分出各种各样的颜色,是因为它们的眼睛含有三种感光色素蛋白,其中每一种蛋白分别对某一段波长的光特别敏感。     

基于可见光遥感的苎麻种质资源冠层性状研究

无人机近空遥感技术可快速实时掌握农田信息,在农作物田间监测中发挥日益重要的作用。本研究使用无人机可见光遥感平台,获取苎麻冠层航拍图像,通过图像处理获得苎麻种质资源冠层图像特征值,结合各苎麻种质资源生长性状,研究26份苎麻种质资源冠层图像性状差异。结果表明,使用HSV色彩空阈值分割可有效将苎麻与土壤杂草分割;26份苎麻资源6个表型性状变异系数分布在11.00%~40.00%之间,多样性指数分布在1.08~1.58之间;26份苎麻资源15个冠层颜色、纹理性状变异系数分布在0.28%~48.09%之间,多样性指数分布在1.25~1.54之间,表明试验苎麻种质资源具有丰富变异和广泛多样性。15个冠层颜色纹理性状主成分分析得到2个主成分,累计贡献率达到95.10%,可有效反映各性状的主要信息。     

基于MIP图像拼接系统的大型古生物化石数字化应用研究北大核心CSCD

在大型古生物化石数字化过程中,为了充分展示化石的细节信息,往往需要拍摄大量的图像。为了实现大型古生物化石数字化数据的完整性,需要对这些大量的图像进行精密的图像拼接处理。基于这种应用需求的前提下,本文在自主研发的Mosaic of Image Program(MIP)图像拼接系统的基础上,对高精度的相机检校、畸变检校及改正和拼接缝的保真处理等方面进行研究,形成系统的古生物化石彩色合成影像数字化流程。在宜州化石馆的实际处理中,完成了杨氏锦州龙、蜥脚类恐龙、孔子鸟等大型古生物化石的数字化,几何失真小于0.36mm(畸变矫正精度优于1像元,拼接精度优于2像元,像片分辨率0.12mm)。同时采用基于SIFT的自动辐射归一化处理算法对拼接影像进行辐射均衡处理,矫正拼接影像辐射亮度的不均衡。     

基于颜色名和OpponentSIFT特征的鳞翅目昆虫图像识别

【目的】本研究旨在探索使用先进的计算机视觉技术实现对昆虫图像的自动分类方法。【方法】通过预处理对采集的昆虫标本图像去除背景,获得昆虫图像的前景蒙板,并由蒙板确定的轮廓计算出前景图像的最小包围盒,剪切出由最小包围盒确定的前景有效区域,然后对剪切得到的图像进行特征提取。首先提取颜色名特征,把原来的RGB（Red-Green-Blue）图像的像素值映射到11种颜色名空间,其值表示RGB值属于该颜色名的概率,每个颜色名平面划分成3×3像素大小的网格,用每格的概率均值作为网格中心点的描述子,最后用空阈金字塔直方图统计的方式形成颜色名视觉词袋特征;其次提取OpponentSIFT(Opponent Scale Invariant Feature Transform)特征,首先把RGB图像变换到对立色空间,对该空间每通道提取SIFT特征,最后用空域池化和直方图统计方法形成OpponentSIFT视觉词袋。将两种词袋特征串接后得到该昆虫图像的特征向量。使用昆虫图像样本训练集提取到的特征向量训练SVM（Support Vector Machine）分类器,使用这些训练得到的分类器即可实现对鳞翅目昆虫的分类识别。【结果】该方法在包含10种576个样本的昆虫图像数据库中进行了测试,取得了100%的识别正确率。【结论】试验结果证明基于颜色名和OpponentSIFT特征可以有效实现对鳞翅目昆虫图像的识别。     

杧果花瓣与花药色彩的数字化描述

对40个杧果品种(系)的花瓣和花药颜色进行数字化描述,即采用Photoshop CS3软件对试材图像进行处理,分别提取花瓣和花药颜色典型色域的CMYK模式参数,通过C、M、Y、K的百分比值确定其颜色。结果表明,用C、M、Y、K的百分比值能较准确科学地表现杧果花瓣和花药的色彩,可以反映品种(系)间差异。通过UPGMA聚类分析,将花瓣和花药颜色分别分成7个类群和4个类群。本研究为种质色彩描述与新品种选育指标提供新的数字化方法。     

The Causes and Consequences of Color Vision

The ability to see colors is not universal in the animal kingdom. Those animals that can detect differences in the wavelengths of the electromagnetic spectrum glean valuable sensory information about their environment. They use color vision to forage, avoid predators, and find high-quality mates. In the past, the colors that humans could see clouded scientists’ study of animals’ color perception. Leaving that bias behind has led to new insights about how and why the color vision of animals evolved. This paper provides a brief introduction to color vision, the genetics of color vision in humans, what colors other animals see, and how scientists study color vision. We examine the consequences of having color vision, including speciation, loss of olfactory capabilities, and sexual selection.     

Color analysis method for estimating the oxygen saturation of hemoglobin using an image-input and processing system

A color analysis method which enables both qualitative and quantitative analyses of an object's color was developed. The method uses a color image-input and processing system composed of a 3-tube video camera and a digital image analyzer, which quantizes a color image into values of red, green, and blue brightness, then processes these values. We introduced a spectrophotometric principle by the Beer-Lambert law, and were able to establish a color model to analyze an object's color. In the coordinate space based on our color model, the hue of the object's color is represented by the direction from the origin, and the density by the distance from the origin. This new method was used to analyze the colors of hemoglobin solutions at various oxygen saturations and concentrations. The results agreed with the known conditions, indicating the validity of the model and its usefulness for quantitative as well as qualitative analyses of color.     

Color discrimination in wild boars

Two experiments were carried out in order to clarify the color perception of Japanese wild boarsSus scrofa leucomystax. Two females were trained using an operant conditioning technique to press a switch under a positive stimulus color card in order to receive food as a reward. In Exp. 1, they were tested for discrimination between 3 colors (red, green and blue) and gray. The luminosity of all colors was the same. The wild boars succeeded in discrimination tests between blue and gray, but failed to discriminate red from gray. They also did not discriminate green from gray so clearly as blue from gray. In Exp. 2, the same wild boars were tested to discriminate between 8 kinds of color, which were created by gradating green yellow into red purple except for the 3 colors used in Exp. 1, and gray. They could clearly discriminate blue, purple blue and a part of purple from gray. In these experiments, wild boars were capable of recognizing bluish colors. However, for colors approaching green or yellow, they failed the test by degrees.     

CMEIAS Color Segmentation: An Improved Computing Technology to Process Color Images for Quantitative Microbial Ecology Studies at Single-Cell Resolution

Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system’s uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at . This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most, thereby strengthening quantitative microscopy-based approaches to advance microbial ecology in situ at individual single-cell resolution.     

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

Background

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

Methods and Results

In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin—3,3’-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

Validation

To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

Context

Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.     

Spectral Test Instrument for Color Vision Measurement

Common displays such as CRT or LCD screens have hmlted capabilities in displaying most color spectra correctly. The main disadvantage of these devices is that they work with three primaries and the colors displayed are the mixture of these three colours. Consequently these devices can be confusing in testing human color identification, because the spectral distribution of the colors displayed is the combined spectrum of the three primaries. We have developed a new instrument for spectrally correct color vision measurement. This instrument uses light emitting diodes (LEDs) and is capable of producing all spectra of perceivable colors, thus with appropriate test methods this instrument can be a reliable and useful tool in testing human color vision and in verifying color vision correction.     

Floral Color Polymorphism and Reproductive Success in Annatto (Bixa orellana L.)

Floral color polymorphism of annatto (Bixa orellana L.) offers a wide range of colors that are maintained in the population by either pollinators or non pollinator agents of selection. In the present study, maintenance of different floral colors was analyzed in relation to reproductive success of Bixa orellana. The different floral petal colors (white, amaranth rose, petunia purple or cobalt violet) were determined from selected plants with reflectance spectrophotometry. Phenotypic measures of other floral traits, female reproductive success, seed set, seed output and seed weight also revealed variation between different floral morphs. Records on seed set varied significantly for different floral color morphs. Maximum fruit maturation (58 %) was observed in amaranth rose and least fruit maturation (25 %) in the white morph. Seed set data indicates pollinators’ preference for more intensely colored flowers. This preference may be due to ability of the pollinators to distinguish the morphs through differentially reduced sensitivity at the green wavelengths. In flowers which received fewer insect visits, polymorphism might be maintained by self fertilization. The color morphs showed differences in Random Amplified Polymorphic DNA (RAPD) profile indicating a genetic basis for floral color variation and consequent differences in seed set. Out of 88 bands generated with nine operon primers, 70 were polymorphic. The present study provides valuable information on the influence of petal color on maternal fitness in B. orellana.     

Color standardization and optimization in Whole Slide Imaging

Introduction

Standardization and validation of the color displayed by digital slides is an important aspect of digital pathology implementation. While the most common reason for color variation is the variance in the protocols and practices in the histology lab, the color displayed can also be affected by variation in capture parameters (for example, illumination and filters), image processing and display factors in the digital systems themselves.

Method

We have been developing techniques for color validation and optimization along two paths. The first was based on two standard slides that are scanned and displayed by the imaging system in question. In this approach, one slide is embedded with nine filters with colors selected especially for H&;E stained slides (looking like tiny Macbeth color chart); the specific color of the nine filters were determined in our previous study and modified for whole slide imaging (WSI). The other slide is an H&;E stained mouse embryo. Both of these slides were scanned and the displayed images were compared to a standard. The second approach was based on our previous multispectral imaging research.

Discussion

As a first step, the two slide method (above) was used to identify inaccurate display of color and its cause, and to understand the importance of accurate color in digital pathology. We have also improved the multispectral-based algorithm for more consistent results in stain standardization. In near future, the results of the two slide and multispectral techniques can be combined and will be widely available.We have been conducting a series of researches and developing projects to improve image quality to establish Image Quality Standardization. This paper discusses one of most important aspects of image quality – color.