Color Image Analysis Research Articles

Using Color, Texture and Object-Based Image Analysis of Multi-Temporal Camera Data to Monitor Soil Aggregate Breakdown.

Remote sensing has shown its potential to assess soil properties and is a fast and non-destructive method for monitoring soil surface changes. In this paper, we monitor soil aggregate breakdown under natural conditions. From November 2014 to February 2015, images and weather data were collected on a daily basis from five soils susceptible to detachment (Silty Loam with various organic matter content, Loam and Sandy Loam). Three techniques that vary in image processing complexity and user interaction were tested for the ability of monitoring aggregate breakdown. Considering that the soil surface roughness causes shadow cast, the blue/red band ratio is utilized to observe the soil aggregate changes. Dealing with images with high spatial resolution, image texture entropy, which reflects the process of soil aggregate breakdown, is used. In addition, the Huang thresholding technique, which allows estimation of the image area occupied by soil aggregate, is performed. Our results show that all three techniques indicate soil aggregate breakdown over time. The shadow ratio shows a gradual change over time with no details related to weather conditions. Both the entropy and the Huang thresholding technique show variations of soil aggregate breakdown responding to weather conditions. Using data obtained with a regular camera, we found that freezing–thawing cycles are the cause of soil aggregate breakdown.

THE JUSTIFICATION FOR THE USE OF THE THERAPEUTIC FACTORS OF THE INTERNAL ENVIRONMENT OF MODERN DEVICES MADE OF NATURAL SYLVINITE IN DENTISTRY

Background: One of the methods of comprehensive therapeutic effects on the human body is sylvinite therapy which has not been used in the treatment of inflammatory periodontal diseases at young age. Aims: Hygienic and clinical evaluation of the possible application of therapeutic factors of sylvinite devices in dentistry.Materials and methods: We conducted hygienic study of the basic physical factors of the internal environment of modern sylvinite structures (radiation background, aeroionization and aerosol environment, climate). The percentage of sylvine and halite in the sylvinite minerals was determined using color image analysis of the salt screen. The microbiological studies of the impact of the mineral sylvinite on the growth parameters of bacterial culture were performed. We examined 79 patients aged 20−25 years diagnosed with chronic generalized catarrhal gingivitis, 36 persons underwent treatment course in the salt structure. The evaluation of the dental status and the periodontal status was conducted using hygienic and periodontal indices. The duration of study was 4 years.Results: Аll participants who entered the study completed it. In the experimental sylvinite room formed food-grade, biodegradable internal environment (background radiation ― 0,18±0,0027 µsv/h; LOA ― 802,33±62,69 ion/cm3; RO ― 509,33±37,17 ion/cm3; stable microclimate). We detected evidence of an inhibitory effect of the combination on Staphylococcus aureus. The use of a comprehensive dental program has improved the condition of periodontal tissues and careeradvantage tooth enamel (PMA and SBI fell of 80.8% and 75.5%; the reduction of the CPU ― 67.7%; fuel and energy resources increased by 37%). Conclusions: The inclusion of sylvinite therapy in the complex treatment of patients with chronic generalized catarrhal gingivitis had a marked positive impact on the state of periodontal tissues and local immunity of the oral cavity.

Retrospective single center study of the efficacy of large spot 532 nm laser for the treatment of facial capillary malformations in 44 patients with the use of three-dimensional image analysis.

We wanted to asses the efficacy of large spot 532 nm laser for the treatment of facial capillary malformations with the use of three-dimensional (3D) image analysis. Retrospective single center study on previously non-treated patients with facial capillary malformations (CM) was performed. A total of 44 consecutive Caucasian patients aged 5-66 were included. Patients had 3D photography performed before and after and had at least one single session of treatment with 532 nm neodymium-doped yttrium aluminum garnet (Nd:YAG) laser with contact cooling, fluencies ranging from 8 to 11.5 J/cm2 , pulse duration ranging from 5 to 9 milliseconds and spot size ranging from 5 to 10 mm. Objective analysis of percentage improvement based on 3D digital assessment of combined color and area improvement (global clearance effect [GCE]) were performed. Median maximal improvement achieved during the treatment (GCEmax ) was 70.4%. Mean number of laser procedures required to achieve this improvement was 7.1 (ranging from 2 to 14)). Improvement of minimum 25% (GCE 25) was achieved by all patients, of minimum 50% (GCE 50) by 77.3%, of minimum 75% (GCE 75) by 38.6%, and of minimum 90% (GCE 90) by 13.64. Large spot 532 nm laser is highly effective in the treatment of facial CM. 3D color and area image analysis provides an objective method to compare different methods of facial CM treatment in future studies. Lasers Surg. Med. 49:743-749, 2017. © 2017 Wiley Periodicals, Inc.

The content of fat acids of general lipids and heavy metals in pollen from dande-lion under different technogenic impact on environment

The level of environmental impact was determined by the content of heavy metals (iron, zinc, copper, chromium, nickel, lead, arsenic and cadmium) in the pollen from dandelion (TaraxacumofficinaleWigg.). Samples of pollen from dandelion for laboratory tests were taken in apiaries located in areas with different intensity of traffic and industry. In particular, the training apiary of Lviv National University of Veterinary Medicine and Biotechnologies named after S.Z. Gzhytsky (the area with very heavy traffic and operation of industrial enterprises) and private beekeeping farms in Vynnyky town and Chyzhykiv village in Pustomiti district Lviv region (areas with less intensity of traffic and industry). Private beekeeping farms in Vynnyky town and Chyzhykiv village are located at a distance of 2–3 and 5–6 km from the training apiary of Lviv National University of Veterinary Medicine and Biotechnologies named after S.Z. Gzhytsky. Moreover, both the training apiary of Lviv National University of Veterinary Medicine and Biotechnologies named after S.Z. Gzhytsky and private beekeeping farms in Vynnyky and Chyzhykiv of Pustomiti district Lviv Oblast are located along the highway Lviv–Ternopil. In each of the mentioned above areas samples of the pollen of dandelion. And in each apiary the samples of the pollen of dandelion and newly built honeycombs were taken from three hives. To clarify the origin of species of the dandelion pollen the identification study was performed using the computer programs «LUCIA» (Laboratory Colour Image Analysis) and «Pollen Data Bank». In the selected samples of the bee pollen heavy metals were found and measured. In the selected samples of newly built honeycombs the concentration of heavy metals, anionic and nonetherified fatty acids and fatty acids of total lipids was measured. The content of heavy metals in the studied biological material was measured on atomic absorption spectrophotometer C–115 PC. The concentration of anionic and nonetherified fatty acids and fatty acids of total lipids in the studied biological material were measured by the gas–liquid chromatography. The resulting digital material was studied and analyzed by variation statistics using the Student's criterion. The arithmetic mean value and arithmetic errors were calculated. Changes were considered probable at p <0.05. For calculations a special computer program Origin 6.0, Excel (Microsoft, USA) was used. The pollen of the dandelion which grows in the area with medium and low technogenic load in comparison with the pollen of the dandelion which grows in the area with high technogenic load the amount of iron, zinc, copper, chromium, nickel, lead and cadmium decreases. The pollen of the dandelion which grows in the area with medium and low technogenic load in comparison with the pollen of the dandelion which grows in the area with high technogenic load the amount of fatty acids of common lipids increases mainly due to monounsaturated fatty acids of n–7 and n–9 families and polyunsaturated fatty acids of n–3 and n–6 families. Therefore their energetic, attractive, functional–metabolic and biologic value for the bee organism increases. The most considerable change in the amount of heavy metals and fatty acids of common lipids is observed in the pollen of the dandelion which grows in the area of low technogenic load.

위성 영상의 효과적인 분석을 위한 밝기와 크로스 엔트로피 기반의 그림자 검출

그림자는 자연 영상에서 관찰되는 물리적인 현상이지만 위성 영상 분석에 부정적인 영향을 미치는 요소로 컴퓨터 비전의 전처리 과정에서 그림자 검출 과정은 매우 중요하다. 본 논문에서는 싱글 영상 기반의 위성 영상에서 효과적인 영상 분석을 위해 그림자를 검출하는 방법으로 크로스 엔트로피와 밝기 영상을 이용해 그림자를 검출하는 방법을 제안하였다. 칼라 영상을 그레이 레벨 영상으로 변환한 후 크로스 엔트로피를 기반으로 최적의 임계값을 추정하여 첫 번째 그림자 후보 영역으로 판별하였고, 칼라 영상의 밝기 영상을 이용해 최종 그림자 영역을 검출하였다. 제안하는 방법의 타당성을 위해 위성 영상들을 대상으로 실험하였고, 실험 결과 제안하는 그림자를 검출 방법이 효과적으로 수행됨을 확인하였다. Shadows are common phenomena observed in natural scenes and often bring a major problem that is affected negatively in colour image analysis. It is important to detect the shadow areas and should be considered in the pre-processing of computer vision. In this paper, the method of shadow detection is proposed using cross entropy and intensity image, and is performed in single image based on the satellite images. After converting the color image to a gray level image, the shadow candidate region has been estimated the optimal threshold value by cross entropy, and then the final shadow region has been detected using intensity image. For the validity of the proposed method, the satellite images is used to experiment. Some experiments are conducted so as to verify the proposed method, and as a result, shadow detection is well performed.

Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications.

Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant’s response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions.

Ore Petrography Using Optical Image Analysis: Application to Zaruma-Portovelo Deposit (Ecuador)

Optical image analysis (OIA) supporting microscopic observation can be applied to improve ore mineral characterization of ore deposits, providing accurate and representative numerical support to petrographic studies, on the polished section scale. In this paper, we present an experimental application of an automated mineral quantification process on polished sections from Zaruma-Portovelo intermediate sulfidation epithermal deposit (Ecuador) using multispectral and color images. Minerals under study were gold, sphalerite, chalcopyrite, galena, pyrite, pyrrhotite, bornite, hematite, chalcocite, pentlandite, covellite, tetrahedrite and native bismuth. The aim of the study was to quantify the ore minerals visible in polished section through OIA and, mainly, to show a detailed description of the methodology implemented. Automated ore identification and determination of geometric parameters predictive of geometallurgical behavior, such as grade, grain size or liberation, have been successfully performed. The results show that automated identification and quantification of ore mineral images are possible through multispectral and color image analysis. Therefore, the optical image analysis method could be a consistent automated mineralogical alternative to carry on detailed ore petrography.

Combined use of monopolar radiofrequency and transdermal drug delivery in the treatment of melasma.

Melasma is a common acquired pigmentary disorder that has a considerable psychological impact on the patient. The recurrent and refractory nature of this condition makes it difficult for treatment. We aim to evaluate the efficacy and safety of a combined system that simultaneously uses monopolar radiofrequency (RF) and transdermal drug delivery of phytocomplex containing 1% kojic acid in the treatment of melasma. Fifty patients affected by melasma underwent 6 sessions of treatment at 1-week intervals. The outcome was evaluated before treatment (T0) and 1 month (T1) and 6 months (T2) after treatment using the Melasma Area and Severity Index score, a Mexameter, and Visioface devices for digital and ultraviolet computerized image analysis of skin color. The image analysis showed that hyperpigmentation was significantly reduced at T1 and T2 compared with baseline. Melasma Area and Severity Index score, the average melanin score, and the average erythema values showed a significant reduction. No side effects were observed or reported. This study describes the first report of improvement in melasma through the combined use of monopolar RF with transdermal delivery of depigmenting agents. This could be a safe, tolerable, and effective alternative tool for the treatment of melasma.

Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves.

The ability of plants to sequester carbon is highly variable over the course of the year and reflects seasonal variation in photosynthetic efficiency. This seasonal variation is most prominent during autumn, when leaves of deciduous tree species such as sugar maple (Acer saccharum Marsh.) undergo senescence, which is associated with downregulation of photosynthesis and a change of leaf color. The remote sensing of leaf color by spectral reflectance measurements and digital repeat images is increasingly used to improve models of growing season length and seasonal variation in carbon sequestration. Vegetation indices derived from spectral reflectance measurements and digital repeat images might not adequately reflect photosynthetic efficiency of red-senescing tree species during autumn due to the changes in foliar pigment content associated with autumn phenology. In this study, we aimed to assess how effectively several widely used vegetation indices capture autumn phenology and reflect the changes in physiology and photosynthetic pigments during autumn. Chlorophyll fluorescence and pigment content of green, yellow, orange and red leaves were measured to represent leaf senescence during autumn and used as a reference to validate and compare vegetation indices derived from leaf-level spectral reflectance measurements and color analysis of digital images. Vegetation indices varied in their suitability to track the decrease of photosynthetic efficiency and chlorophyll content despite increasing anthocyanin content. Commonly used spectral reflectance indices such as the normalized difference vegetation index and photochemical reflectance index showed major constraints arising from a limited representation of gradual decreases in chlorophyll content and an influence of high foliar anthocyanin levels. The excess green index and green-red vegetation index were more suitable to assess the process of senescence. Similarly, digital image analysis revealed that vegetation indices such as Hue and normalized difference index are superior compared with the often-used green chromatic coordinate. We conclude that indices based on red and green color information generally represent autumn phenology most efficiently.

Choice of distance metrics for RGB color image analysis

Choice of distance metrics for RGB color image analysis

Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed

An adaptive-network-based fuzzy inference system based on color image analysis was used to estimate coffee bean moisture content during roasting in a spouted bed. The neuro-fuzzy model described the grain moisture changes as a function of brightness (L*), browning index (BI) and the distance to a defined standard (ΔE). An image-capture device was designed to monitor color variations in the L*a*b* space for high temperatures samples taken from the reactor. The proposed model was composed of three Gaussian-type fuzzy sets based on the scatter partition method. The neuro-fuzzy model was trained with the Back-propagation algorithm using experimental measurements at three air temperature levels (400, 450 and 500°C). The performance of the neuro-fuzzy model resulted better compared to conventional methods obtaining a coefficient of determination > 0.98, a root mean square error <0.002 and a modified Schwarz–Rissanen information criterion <0. The simplicity of the model and its robustness against changes in the input variables make it suitable for monitoring on-line the roasting process.

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ABSTRACT In this Article, it is proposed a computer vision system, which can detect whether a coffee fruit is suitable for production or not. In order to achieve this detection two algorithms were developed, one to classify the coffee fruit in the ripe or unripe state; and the other to detect the presence of the ‘coffee berry borer’. The first one uses a Bayesian Classifier to identify the colour of the fruit, and the second algorithm searches for the holes made by the coffee berry borer on the surface of the product. Moreover, a mechanical system was designed for the transportation and separation of the coffee fruits. In the first stage, coffees are transported as pictures of them are taken. At the end of this stage, the separation mechanism alters the path of the fruit based on the result of the classifier. The system proposed obtained an effectiveness of 87%. KEYWORDS: Artificial neural networks, Binarization, Feature Extraction, Image classification, Image color analysis, Image segmentation, Machine vision, Selection process.

Automated Assessment and Mapping of Grape Quality through Image-based Color Analysis

Abstract: The harvest operation for table-grapes and fresh market horticultural fruits is a large and expensive logistical challenge with the choice of harvest dates and locations playing a crucial role in determining the quality of the yield and in determining the efficiency and productivity gain of the entire operation. The choice of harvest dates and locations, particularly in red varieties, is planned based upon the development of the color of the grape clusters. The traditional process to evaluate the amount of ripe, fully-colored fruit is visual assessment, which is subjective and prone to errors. The number of locations where a grower will evaluate the fruit development is statistically insufficient given the size of commercial vineyards and the variability in the color development. Therefore, an automated approach for evaluating color development is desirable. In this paper, we use a vision-based system to collect images of the fruit zone in a vineyard. We then use color image analysis to grade and predict the color development of grape clusters in the vineyard. Using our approach we are able to generate spatial maps of the vineyard showing the current and predicted distribution of color development. Our imaging measurement system achieves R 2 correlation values of 0.42-0.56 against human measurements. We our able to predict the color development to within 5% average absolute error of the imaging measurements. The prediction of spatial maps is important from the perspective of selective harvesting as it allows the precise targeting of productive zones during the harvest operation. To the best of our knowledge generation of these spatial maps that represent the current and predicted state of the color development of an entire vineyard block, before harvest and in high resolution, is a first of its kind.

Renal Graft Fibrosis and Inflammation Quantification by an Automated Fourier-Transform Infrared Imaging Technique.

Renal interstitial fibrosis and interstitial active inflammation are the main histologic features of renal allograft biopsy specimens. Fibrosis is currently assessed by semiquantitative subjective analysis, and color image analysis has been developed to improve the reliability and repeatability of this evaluation. However, these techniques fail to distinguish fibrosis from constitutive collagen or active inflammation. We developed an automatic, reproducible Fourier-transform infrared (FTIR) imaging-based technique for simultaneous quantification of fibrosis and inflammation in renal allograft biopsy specimens. We generated and validated a classification model using 49 renal biopsy specimens and subsequently tested the robustness of this classification algorithm on 166 renal grafts. Finally, we explored the clinical relevance of fibrosis quantification using FTIR imaging by comparing results with renal function at 3 months after transplantation (M3) and the variation of renal function between M3 and M12. We showed excellent robustness for fibrosis and inflammation classification, with >90% of renal biopsy specimens adequately classified by FTIR imaging. Finally, fibrosis quantification by FTIR imaging correlated with renal function at M3, and the variation in fibrosis between M3 and M12 correlated well with the variation in renal function over the same period. This study shows that FTIR-based analysis of renal graft biopsy specimens is a reproducible and reliable label-free technique for quantifying fibrosis and active inflammation. This technique seems to be more relevant than digital image analysis and promising for both research studies and routine clinical practice.

Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time

This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product.

Novel uncertainty principles associated with 2D quaternion Fourier transforms

ABSTRACTThe quaternion Fourier transform has been widely employed in the colour image processing. The use of quaternions allow the analysis of colour images as vector fields. In this paper, the right-sided quaternion Fourier transform and its properties are reviewed. Using the polar form of quaternions, two novel uncertainty principles associated with covariance are established. They prescribe the lower bounds with covariances on the products of the effective widths of quaternionic signals in the space and frequency domains. The results generalize the Heisenberg's uncertainty principle to the 2D quaternionic space.

Computer Aided Diagnosis System for Early Lung Cancer Detection

Lung cancer continues to rank as the leading cause of cancer deaths worldwide. One of the most promising techniques for early detection of cancerous cells relies on sputum cell analysis. This was the motivation behind the design and the development of a new computer aided diagnosis (CAD) system for early detection of lung cancer based on the analysis of sputum color images. The proposed CAD system encompasses four main processing steps. First is the preprocessing step which utilizes a Bayesian classification method using histogram analysis. Then, in the second step, mean shift segmentation is applied to segment the nuclei from the cytoplasm. The third step is the feature analysis. In this step, geometric and chromatic features are extracted from the nucleus region. These features are used in the diagnostic process of the sputum images. Finally, the diagnosis is completed using an artificial neural network and support vector machine (SVM) for classifying the cells into benign or malignant. The performance of the system was analyzed based on different criteria such as sensitivity, specificity and accuracy. The evaluation was carried out using Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate the efficiency of the SVM classifier over other classifiers, with 97% sensitivity and accuracy as well as a significant reduction in the number of false positive and false negative rates.

Elliptical Monogenic Wavelets for the Analysis and Processing of Color Images

This paper studies and gives new algorithms for image processing based on monogenic wavelets. Existing greyscale monogenic filterbanks are reviewed and we reveal a lack of discussion about the synthesis part. The monogenic synthesis is therefore defined from the idea of wavelet modulation, and an innovative filterbank is constructed by using the Radon transform. The color extension is then investigated. First, the elliptical Fourier atom model is proposed to generalize the analytic signal representation for vector-valued signals. Then, a color Riesz-transform is defined so as to construct color elliptical monogenic wavelets. Our Radon-based monogenic filterbank can be easily extended to color according to this definition. The proposed wavelet representation provides efficient analysis of local features in terms of shape and color, thanks to the concepts of amplitude, phase, orientation, and ellipse parameters. The synthesis from local features is deeply studied. We conclude the paper by defining the color local frequency, proposing an estimation algorithm.

Leaf classification using multiple feature analysis based on semi-supervised clustering

Multiple features such as the margin, the shape and the texture of plant leaves are of great importance for classification of plant species, as they are often regarded as the unique features to identify plants. In this paper, we study the performance of a recently proposed semi-supervised fuzzy clustering algorithm with feature discrimination for leaf classification, based on features generated by principal component analysis of color images. The method outlines a basic framework for judging the weights of different features by adopting multiple feature matrixes obtained from the initial images as input data and the clustering results of the proposed clustering algorithm as output data to distinguish dissimilarities between various leaves. Real leaf images are employed to evaluate its performance and the experiment demonstrates that these results suggest that the margin feature, the shape feature and combination feature especially the margin feature and combination feature may be the best choice for leaf classification.

Process optimization based on carrot powder color characteristics

The carotenoid content indicates the processing quality of carrot powder. The carotenoid content of carrot powder was analyzed using digital image processing technology and color analysis technology, and an estimation model between the carotenoid content and color characteristics was established. The carrot was dried using different drying methods. The verification test result shows that model fitting is prominent and the test error is small and promptly improves the reference of testing for the carotenoid content of carrot powder. The processing parameters of the carrot powder with the highest carotenoid content are as follows: the carrot was frozen and thawed using the freeze drying method; the powder particle size is 80–120 mesh.