Color Image Analysis Research Articles

Estimation of leaf chlorophyll content of rice using image color analysis

Leaf color has been commonly used as an index for crop stress status diagnosis. We have developed a low-cost and nondestructive method that is easy to use to evaluate the chlorophyll content of rice (Oryza sativa L.) using leaf image color analysis. The relationships between the imaging data and leaf pigment content were investigated. There was a highly significant negative relationship between the red (R) and green (G) values in the RGB color space analyzed from leaf image and chlorophyll a (Chl a) content, chlorophyll b (Chl b) content, Chl a + b content, and carotenoid (CAR) content. The G data had a higher correlation coefficient with Chl a, Chl b, Chl a + b, and CAR than the R data. However, no significant relationship was found between the blue (B) value and Chl a, Chl b, Chl a + b, and CAR. Linear and logarithmic correlation functions were used to model the relationship between imaging data and leaf pigment data. Using another set of collected data, significant correlations were observed between the predicted Chl a, Chl b, Chl a + b, CAR-based G data and the measured values. The determination coefficient of the R predicted model of simulated chlorophyll pigment content and observed data was smaller than that of the G predicted model. Comparably, Chl a + b and Chl a could be better predicted than Chl b and CAR from rice leaf image analysis. Combined with our previous study on barley and wheat, this study demonstrated that the chlorophyll content of plants could be nondestructivly evaluated using the leaf image color analysis method.

Evaluation of Corneal Vessels

Corneal new vessels can result in significant loss of vision as well as reduce graft survival. Recently, newer treatment modalities have been introduced. However, attempts to meet the challenge of evaluating corneal vessels by quantification are evolving.In vivoevaluations range from computer-aided analysis of colour images to determination of area, diameter and tortuousity of blood vessels on indocyanine green angiography images. Attempts at developing a fully automated process of evaluation and quantification are hampered by the anatomical variations of limbus, random nature of blood vessels and multiple levels of involvement of the corneal stroma. Currently, semi-automated methods require manually fixing the limbus prior to quantification of vessels. Using indocyanine green angiography, the full extent of corneal vessels even in the presence of scarring can be determined. With the evolution of new techniques, it is hoped that a robust corneal vessel quantifying tool will be described in the near future that will aid the effectiveness of treatment strategies.

Color Image Analysis of Cosmetic Web-Site for Color Marketing

This study aims to derive the sensitive image analysing the color tones based on the web-sites of cosmetic brand and to know how the web-site color tones and the color tone image as visual factor for information transfer do influence the effective communication. 10 cosmetic brand web-sites are selected based on the pre survey, and the main page color tones of cosmetic brand web-site are analyzed by Color Catch and Munsell Conversion program, whereas the composition of color tones and color image such as the main trend colors, secondary colors, accent colors and etc are analyzed by a graphic professional program 'Adobe Photoshop'. 5 color tone groups are classified as Black&White, Green&White, Blue&White, Purple&White and Red&White according to the color distribution chart, and the emotional language is derived applying them to the pre research and IRI image scale. The color association is empirically analyzed by a survey method. The study results finally show Black&White is proper for expression of high quality and modern image, Green&White for pure and clean image, Blue&White for cool and transparent image, Purple&White for mysterious and elegant image respectively. This study results are necessary for plan of color delivering the strong transferring power of visual information the time when to make the effective web-sites, and it hopefully will be utilized as the basic data for the color marketing to actively express the brand identity and to satisfy the consumers' sensitivity.

Contrast Enhancement of Sports Images Using Two Comparative Approaches

In this paper we have proposed two comparative approaches for the contrast enhancement of dark sports images. The contrast of any image is a very important characteristic which decides the quality of image. Low contrast images occur often due to poor or non uniform lighting conditions and sometimes due to the non linearity or small dynamic range of the imaging system. Enhancing the contrast of sports images is of importance since it is difficult to analyze the performance of the team or player with a poor quality image. Though several methods are proposed for gray scale images, enhancing the contrast of color images is a complicated process. In this paper we have proposed two comparative approaches for the contrast enhancement of color images and have compared their performance against the standard histogram equalization method. First method is contrast enhancement of color images using fuzzy rule based method and the second method is using modified sigmoid function. Color images cannot be processed directly hence a suitable color model is chosen for processing and the proposed methods are implemented. For both the approaches the color images are split into RGB planes and the proposed operation is performed on each plane and finally the planes are concatenated to obtain the enhanced image. Performance of the proposed methods is measured using a factor known as Measure of Contrast and the comparison is represented graphically. Experimental results prove that of the two methods proposed, contrast enhancement using modified sigmoid function provides the highest measure of contrast and can be effectively used for further analysis of sports color images.

Kidney Graft Quality after Donation from Uncontrolled Deceased Donors after Cardiac Arrest

Kidney grafts from uncontrolled deceased donors after cardiac arrest (uDDCA) have recently been used in France to counteract organ shortage. The quality of these kidneys remains debatable. The aim of our study was to compare the outcomes and the quality of uDDCA kidneys with that of kidneys from optimal donors such as simultaneous kidney and pancreas (SPK) donors and extended-criteria donors (ECD). 27 kidney grafts from uDDCA (mean donor age, 41) were compared with 24 kidney grafts from SPK donors (mean donor age, 26), and 30 kidney grafts from ECD (mean donor age, 66). All three patient groups were non-immunized and received the same induction and maintenance immuossupressive therapy. The quality of the grafts was assessed by renal function and histology. GFR was estimated by MDRD formula (eGFR) at M1 (n=80), M3 (n=80), M6 (n=79), M12 (n=74), M24 (n=70) and M36 (n=51) and measured by inuline clearance (mGFR) at M12 (n=66) and M36 (n=46). Interstitial fibrosis (IF) and vascular lesions were analyzed in systematic kidney biopsies at M3 (n=54) and M12 (n=50) with the Banff 2007 classification. IF was quantitatively measured by colour image analysis. Kidney graft quality from SPK group was always superior than the two others groups. In the short term, DGF in the uDDCA group was significantly higher than in the ECD group (table 1). In the uDDCA group renal function was initially poorer but improved during the first year. However on the long term, renal function and interstitial fibrosis was not different in uDDCA vs ECD group (table 2). Conclusion: Our study suggests that the quality of kidneys from uDDCA donors is similar to that of ECD and that these kidneys should be attributed to the same recipient population.[Table 1: Early results][Table 2: kidney graft function and histology]

Kidney Graft Quality after Donation from Uncontrolled Deceased Donors after Cardiac Arrest

Kidney grafts from uncontrolled deceased donors after cardiac arrest (uDDCA) have recently been used in France to counteract organ shortage. The quality of these kidneys remains debatable. The aim of our study was to compare the outcomes and the quality of uDDCA kidneys with that of kidneys from optimal donors such as simultaneous kidney and pancreas (SPK) donors and extended-criteria donors (ECD). 27 kidney grafts from uDDCA (mean donor age, 41) were compared with 24 kidney grafts from SPK donors (mean donor age, 26), and 30 kidney grafts from ECD (mean donor age, 66). All three patient groups were non-immunized and received the same induction and maintenance immuossupressive therapy. The quality of the grafts was assessed by renal function and histology. GFR was estimated by MDRD formula (eGFR) at M1 (n=80), M3 (n=80), M6 (n=79), M12 (n=74), M24 (n=70) and M36 (n=51) and measured by inuline clearance (mGFR) at M12 (n=66) and M36 (n=46). Interstitial fibrosis (IF) and vascular lesions were analyzed in systematic kidney biopsies at M3 (n=54) and M12 (n=50) with the Banff 2007 classification. IF was quantitatively measured by colour image analysis. Kidney graft quality from SPK group was always superior than the two others groups. In the short term, DGF in the uDDCA group was significantly higher than in the ECD group (table 1). In the uDDCA group renal function was initially poorer but improved during the first year. However on the long term, renal function and interstitial fibrosis was not different in uDDCA vs ECD group (table 2). Conclusion: Our study suggests that the quality of kidneys from uDDCA donors is similar to that of ECD and that these kidneys should be attributed to the same recipient population.[Table 1: Early results][Table 2: kidney graft function and histology]

Color Difference Evaluation Based on Image Color Appearance Model

The iCAM (image color appearance model) as the most advanced modern color appearance model is constantly being put into use. In color images industry, the evaluation of the color difference is significant. This article will focus on color difference formula of image color appearance model and analysis its reasonable color difference calculation method, which reflects the advantages of image color appearance model.

Assessing print quality by machine in offset colour printing

Information processing steps in printing industry are highly automated, except the last one—print quality assessment, which usually is a manual, tedious, and subjective procedure. This article presents a random forests-based technique for automatic print quality assessment based on objective values of several print quality attributes. Values of the attributes are obtained from soft sensors through data mining and colour image analysis. Experimental investigations have shown good correspondence between print quality evaluations obtained by the technique proposed and the average observer.

A novel adaptive color to grayscale conversion algorithm for digital images

A color image sent to the monochrome device must undergo the color-to-grayscale conversion process. Although the conventional color-to-grayscale conversion algorithms are widely used, their performance is questionable in certain circumstances and poor quality resultant grayscale images will be produced. This paper tackled some drawbacks of the conventional methods by introducing a novel approach namely the Adaptive Color to Grayscale (ACGS) conversion algorithm. In the proposed ACGS method, the pixels distribution analysis of input color images was performed to calculate the weight contribution of red, green, and blue components during the conversion process. The extensive experimental results demonstrated the superior qualitative and quantitative performance of the proposed ACGS method over the conventional algorithms and its feasibility in real-time video processing applications. These promising results suggest that the proposed ACGS method is suitable to be employed in the monochrome devices for pre- and post- processing of digital images. Key words: Color-to-grayscale conversion, adaptive, color image, grayscale image, image pre-processing.

Color Image Analysis of the Public Nursery Room

Color Image Analysis of the Public Nursery Room

Automatic machine embroidery image color analysis system, part III: Integration of machine embroidery image color analysis system

This series of study proposed the automatic machine embroidery image color analysis system as an extension of the previous proposed machine embroidery color separation system and repetitive pattern search system. This paper integrated the machine embroidery image color analysis system to achieve system automation. The system is divided into three stages: (1) acquire the embroidery fabric image, filter the acquired image noise, and smooth the embroidery fabric texture before using the transform to reduce image information amount and improve subsequent calculation speed; (2) use the genetic algorithm for the repetitive pattern search, and restore the identified repetitive pattern images to the original size by image pyramids, then use frequency domain template matching to determine the locations of the repetitive patterns to verify repetitive pattern accuracy; (3) apply the Gustafson–Kessel cluster algorithm and cluster validity partition index SC to obtain the machine embroidery image’s number of colors and corresponding areas, then employ half-toning technology to determine color types for chromatography. The integrated system can accurately identify the repetitive patterns for chromatography to realize the automatic drafting of embroidery fabric. It can be further integrated with automatic manufacturing equipment for machine embroidery automation.

Thermoluminescence color image analysis of sediments from Lake Khuvsgul, Mongolia, and its potential to investigate paleoenvironmental change

Thermoluminescence color images (TLCIs) of lacustrine sediments from Lake Khuvsgul (Hovsgol), Mongolia, were taken with a digital camera, and the RGB information of each color pixel was converted into numerical information to plot a CIE chromaticity diagram. The number of pixels within one of five color zones was counted for statistical color analyses. A total of 163 samples were collected for the core depth of 2.10–59.49 m. Twelve images were investigated for each sample. The number of colored pixels (analogous to the emission intensity) varied along the core depth. The amount of HCl-soluble-material, i.e., calcium carbonates, shows similar fluctuations, and the colored pixel points on the CIE chromaticity diagram fell in the same region with those for calcium carbonates from the catchment area. These results indicate that the major source of luminescence is calcium carbonate. When TLCIs are investigated after grain size separation, a small number of pixels are found in the blue-purple region, separate from the main cluster of pixels in the CIE diagrams for only fine particles. Because aeolian dust is expected to be in fine-grained fractions, these distinctive luminescence emitters are possibly of aeolian origin. TLCI has a potential to extract information for aeolian dust in lake sediments.

Automatic machine embroidery image color analysis system, part II: Application of the genetic algorithm in search of a repetitive pattern image

This series of studies proposed an automatic machine embroidery image color analysis system. Based on the machine embroidery image color separation system proposed in part I of this series of papers (Kuo CFJ, Jian BJ and Wu HC. Automatic machine embroidery image color analysis system, part I: using Gustafson-Kessel clustering algorithm in embroidery fabric color separation. Textil Res J 2011, submitted for publication), this paper presents an automatic repetitive pattern image recognition algorithm for machine embroidery. Firstly, the image pre-processing is implemented to eliminate image acquisition noise and smooth the texture of machine embroidery. After the completion of image pre-processing, the genetic algorithm (GA) is applied to obtain the color image of repetitive patterns. The novel fitness function proposed in this paper can increase the calculation of the color entropy value to improve the measurement block accuracy of color similarity in previous literature. When applied to the GA, the system can accurately identify and cut out images of repetitive patterns. In addition, since the proposed fitness function has been normalized, it can accurately calculate the optimal target function, indicating that the proposed algorithm can automatically and accurately identify and cut out the images of repetitive patterns in machine embroidery.

The Benefit of Color Information in Digital Image Analysis for the Estimation of Legume Contribution in Legume–Grass Mixtures

ABSTRACTEfficient detection of spatial legume dry matter (DM) contribution in legume–grass mixtures is of great importance for targeted management of legume‐based swards. Legume coverage may be an appropriate indicator for the contribution of legumes, because it can be assessed by on‐the‐go systems with digital image analysis (DIA). To examine the perspectives of DIA a pot experiment, under controlled conditions, was conducted across a wide range of legume species (white clover [Trifolium repens L.], red clover [Trifolium pratense L.], and alfalfa [Medicago sativa L.]), legume proportion (0–800 g kg−1), and growth stage (start of tillering to start of heading). In this study, an advanced procedure for the determination of legume DM contribution by DIA is suggested. The DIA procedure comprises the analysis of color images and applies an advanced function to predict legume DM contribution from legume coverage by considering total sward biomass. This resulted in an accurate prediction of legume contribution (grams per kilogram) with R2 of 0.90, 0.94, and 0.93 for red clover, white clover, and alfalfa, respectively. For validation, swards of a field experiment were used. It showed that legume detection is possible, but for practical field application some further adjustments are necessary.

자연모래와 부순모래를 사용한 콘크리트의 고온 하에서 특성연구

이 연구의 목적은 자연모래를 사용한 콘크리트(NSC)와 부순모래를 사용한 콘크리트(CSC)의 고온 하에서의 물리, 역학적인 특성을 조사하는 것이다. 시험체를 23℃에서 800℃ 범위에 노출시켜 육안검사와 중량손실을 측정하였으며, 압축강도 시험과 할렬 인장강도 시험을 수행하였다. 그 결과 중량손실률은 노출 온도가 증가할수록 감소하였으며, NSC와 CSC의 감소율은 비슷하였다. 압축강도, 할렬 인장강도와 탄성계수 또한 노출온도가 증가할수록 감소하였다. NSC의 잔존 압축강도는 200℃와 400℃에서 CSC보다 급격히 감소했으며, NSC의 잔존 할렬 인장강도 또한 200℃에서 CSC 보다 급격하게 감소했다. 하지만 CSC와 NSC 공히 800℃에 노출되었을 때는 비슷한 잔존강도를 나타내었다.

Automatic machine embroidery image color analysis system. Part I: Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation

This series of studies aims to propose the automatic machine embroidery image color analysis system to solve the problem of lack of manpower for machine embroidery fabric drafting and to shorten drafting time. The studies included three parts: (1) machine embroidery image color separation, (2) search of repeated pattern images, (3) machine embroidery color analysis system integration. This study aimed to find the optimal clustering algorithm and cluster validity indices for the automatic color separation process of machine embroidery fabric drafting in order to shorten drafting time. To improve image quality for computer analysis, this study used the color hybrid median filter to filter noise and the color bilateral filter to smoothen fabric and embroidery texture for subsequent color separation. By extracting the color a* component and b* component of the machine embroidery image in CIE L*a*b* color system, this study used the Gustafson-Kessel clustering algorithm for color separation. The Gustafson-Kessel clustering algorithm in machine embroidery image color separation can improve color separation accuracy, and its result is compared with that of the clustering algorithms commonly used in the color separation of color images. This study implemented the chromatography of the color separation results, and used the cluster validity indices to prove that the application of Gustafson-Kessel clustering algorithm in the machine embroidery image color separation system has better results than K-means, K-medoid, fuzzy C-means (FCM), and self-organizing map (SOM) clustering algorithm. The results meet the classifications as expected by human eyes.

Expression of GSK-3β in renal allograft tissue and its significance in pathogenesis of chronic allograft dysfunction

ObjectiveTo explore the expression of Glycogen synthase kinase 3 beta (GSK-3β) in renal allograft tissue and its significance in the pathogenesis of chronic allograft dysfunction.MethodsRenal allograft biopsy was performed in all of the renal allograft recipients with proteinuria or increased serum creatinine level who came into our hospital from January 2007 to December 2009. Among them 28 cases was diagnosed as chronic allograft dysfunction based on pahtological observation, including 21 males with a mean age of 45 ± 10 years old and 7 females with a mean age of 42 ± 9 years old. The time from kidney transplantation to biopsy were 1-9 (3.5) years. Their serum creatinine level were 206 ± 122 umol/L. Immunohistochemical assay and computer-assisted genuine color image analysis system (imagepro-plus 6.0) were used to detect the expression of GSK-3β in the renal allografts of 28 cases of recipients with chronic allograft dysfunction. Mean area and mean integrated optical density of GSK-3β expression were calculated. The relationship between expression level of GSK-3β and either the grade of inflammatory cell infiltration or interstitial fibrosis/tubular atrophy in renal allograft was analyzed. Five specimens of healthy renal tissue were used as controls.ResultsThe expression level of the GSK-3β was significantly increased in the renal allograft tissue of recipients with chronic allograft dysfunction, compared to normal renal tissues, and GSK-3β expression became stronger along with the increasing of the grade of either inflammatory cell infiltration or interstitial fibrosis/tubular atrophy in renal allograft tissue.ConclusionThere might be a positive correlation between either inflammatory cell infiltration or interstitial fibrosis/tubular atrophy and high GSK-3β expression in renal allograft tissue.Virtual slidesThe virtual slide(s) for this article can be found here:http://www.diagnosticpathology.diagnomx.eu/vs/9924478946162998.

Apartment BI and outer wall color analysis through the analysis of color image - Focusing on the “Korea Color Awards” winning -

Apartment BI and outer wall color analysis through the analysis of color image - Focusing on the “Korea Color Awards” winning -

COLOR IMAGE ANALYSIS FOR DETERMINING HARVEST TIME OF BEAN AND PEA PODS

The main objectives of this research are to study color image analysis for determining the optimal harvest time of green beans (Phaseolus vulgaris L. var Paulista) and green peas (Pisum Sativum L. var Sugar Lays) depending on maturity, and creating the color standards at different ages. Obtained results are summarized as follows: 1) The percentages of color components increased for read (R), green (G) and blue (B) from 29.0% to 60.4%, from 44.3% to 76.5%, and from 15.3% to 49.0% for pod ages from 10 to 31 days, respectively. Meanwhile, the values for sweet pea pods increased from 26.67% to 51.76%, from 41.18% to 61.57%, and from 15.29% to 29.80% for pod ages from 6 to 33 days, respectively. 2) The relationships between red color (R), green color (G) and blue color (B) as functions of the pod age (A) resulted as follows: R = 39.490 + 3.909A, G = 78.203 + 3.755 A, and B = 3.826 A - 1.063 (green bean pods) R = 52.767 + 2.423A, G = 96.462 + 1.817 A, and B = 34.027 + 1.080 A (sweet peas pods) 3) According to this study, the criteria as a standard for quality depending on the color to determine the optimal harvest time appeared about 22 and18 days for green bean and pea pods (from appearing the pod from its flower) were 34.12% (R), 43.04%(G) and 22.83% (B) for bean, while they were 33.45% (R), 45.99% (G) and 20.56%(B)for peas.

Improved Image Analysis of DETECHIP® Allows for Increased Specificity in Drug Discrimination

DETECHIP® is a novel molecular sensing array being developed for the detection and identification of a variety of compounds including controlled substances. This easy to use technology has the ability to produce a unique identifying binary code for each substance tested. Original analysis methodology relied on human vision to classify color and fluorescence changes within the array. New digital color image analysis techniques using red-green-blue (RGB) color values provided a higher degree of specificity and greater consistency. This image analysis technique was able to detect more subtle changes in color and was therefore able to properly discriminate between substances that previously produced identical codes. This technique was also expanded to analyze changes in RGB color values individually, increasing the length of the code to 48 digits and therefore potentially providing a further increase in specificity. To show the applicability of this new method, a blind study was performed, correctly identifying two unknown analytes.