Standard Color Images Research Articles

Vein pattern visualisation for biometric identification with cGAN on a New Zealand dataset

Forensic identification using vein patterns in standard colour images presents significant challenges due to their low visibility. Recent efforts have employed various computational techniques, including artificial neural networks and optical vein disclosure, to enhance vein pattern detection. However, these methods still face limitations in reliability when compared to Near-Infrared (NIR) reference images. One of the biggest challenges of the studies is the limited number of available datasets that have synchronised colour and NIR images from body limbs. This paper introduces a new dataset comprising 602 pairs of synchronised NIR and RGB forearm images from a diverse population, ethically approved and collected in Auckland, New Zealand. Using this dataset, we also propose a conditional Generative Adversarial Networks (cGANs) model to translate RGB images into their NIR equivalents. Our evaluations focus on matching accuracy, vein length measurements, and contrast quality, demonstrating that the translated vein patterns closely resemble their NIR counterparts. This advancement offers promising implications for forensic identification techniques.

Use of Digital Methods to Optimize Visualization during Surgical Gonioscopy.

Purpose: The aim of this study was to evaluate the efficacy of digital visualization for enhancing the visualization of iridocorneal structures during surgical gonioscopy. Methods: This was a prospective, single-center study on a series of 26 cases of trabecular stent implantation performed by the same surgeon. Images were recorded during surgical gonioscopy, and before stent implantation, with standard colors and with the optimization of various settings, principally color saturation and temperature and the use of the cyan color filter. Subjective analyses were performed by two glaucoma surgeons, and objective contrast measurements were made on iridocorneal structure images. Results: The surgeons evaluating the images considered the optimized digital settings to produce enhanced tissue visibility for both trabecular meshwork pigmentation and Schlemm's canal in more than 65% of cases. The mean difference in the standard deviation of the pixel intensity values was 37.87 (±4.61) for the optimized filter images and 32.37 (±3.51) for the standard-color images (p < 0.001). The use of a cyan filter provided a good level of contrast for the visualization of trabecular meshwork pigmentation. Increasing the color temperature highlighted the red appearance of Schlemm's canal. Conclusions: We report here the utility of optimized digital settings including the cyan filter and a warmer color for enhancing the visualization of iridocorneal structures during surgical gonioscopy. These settings could be used in surgical practice to enhance the visualization of the trabecular meshwork and Schlemm's canal during minimally invasive glaucoma surgery.

Image encryption algorithm based on the density and 6D logistic map

One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are generated depending on the chaotic logistic with the image density to encrypt the gray and color images, and the second stage is the decryption, which is the opposite of the encryption process to obtain the original image. The proposed method has been tested on two standard gray and color images publicly available. The test results indicate to the highest value of peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), number of pixel change rate (NPCR) are 7.7268, 50.2011 and 100, respectively. While the encryption and decryption speed up to 0.6319 and 0.5305 second respectively.

Use of Black-and-White Digital Filters to Optimize Visualization in Cataract Surgery.

Purpose: To evaluate the effect of a black-and-white (BW) filter on the optimization of visualization at each stage of cataract surgery. Methods: Prospective, single-center, single-surgeon, consecutive case series of 40 patients undergoing cataract surgery with BW filter. Surgical images and videos were recorded with and without the BW filter at each stage of cataract surgery. Contrast measurements of surgical images and subjective analysis of video sequences were performed. Results: The surgeons assessed the BW filter to optimize the tissue visibility of capsulorhexis contours, hydrodissection fluid wave perception, the contrast of instruments through a nucleus during phaco-chop, and subincisional cortex contrast through the corneal edema. Despite the higher contrasts’ value obtained with BW filter images during nucleus removal, posterior capsular polishing and viscous removal, the surgeons subjectively reported no significant advantage of using a BW filter. Standard color images were found to be better for localizing the limbal area during incision and for nucleus sculpture to assess groove depth. Conclusions: In conclusion, we describe here the potential indications for BW filter use at particular stages in cataract surgery. A BW filter could be used, with caution, in cases of poor visualization.

A New Adaptive Filter to Remove Impulsive Noise in Color Images

In this paper, a new adaptive vector directional distance filter (AVDDF) is proposed. The AVDDF filter is developed to remove the ‘salt and papper’ impulsive noise in color images. This algorithm consists in first step to detect pixels that are likely to have been contaminated with noise by using a threshold value. In second step, after recognizing the corrupted pixels, the smallest angular‐magnitude distance is used to replace the noisy pixels. The proposed filter is tested with several standard color images which are contaminated with various levels of ‘salt and pepper’ impulsive noise (3%, 5%, 10%, 20% and 30%). The performance of the proposed filter is measured with peak signal‐to‐noise ratio (PSNR) and normalized color difference (NCD). The simulation results show that the proposed filter permits to ameliorate the performance of the classic vector directional distance filter (VDDF). Indeed, it provides an improvement by an average of 5% and 26% in the PSNR and NCD relative to the VDDF filter, respectively, with a small increase in the execution time by 6%. Besides, the AVDDF filter allows also an enhancement by an average of 2% and 14% in the PSNR and NCD relative to adaptive vector median filter (AVMF), respectively. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Optical biopsy mapping on endoscopic image mosaics with a marker-free probe

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Early diagnosis significantly increases the chances of survival; therefore, improved assisted exploration and screening techniques are necessary. Previously, we made use of an augmented multi-spectral endoscope by inserting an optical probe into the instrumentation channel. However, the limited field of view and the lack of markings left by optical biopsies on the tissue complicate the navigation and revisit of the suspect areas probed in-vivo. In this contribution two innovative tools are introduced to significantly increase the traceability and monitoring of patients in clinical practice: (i) video mosaicing to build a more comprehensive and panoramic view of large gastric areas; (ii) optical biopsy targeting and registration with the endoscopic images. The proposed optical flow-based mosaicing technique selects images that minimize texture discontinuities and is robust despite the lack of texture and illumination variations. The optical biopsy targeting is based on automatic tracking of a free-marker probe in the endoscopic view using deep learning to dynamically estimate its pose during exploration. The accuracy of pose estimation is sufficient to ensure a precise overlapping of the standard white-light color image and the hyperspectral probe image, assuming that the small target area of the organ is almost flat. This allows the mapping of all spatio-temporally tracked biopsy sites onto the panoramic mosaic. Experimental validations are carried out from videos acquired on patients in hospital. The proposed technique is purely software-based and therefore easily integrable into clinical practice. It is also generic and compatible to any imaging modality that connects to a fiberscope.

Hue‐shifting for accurate and precise quantification of biochemical substances using diagnostic test strips

AbstractSome test strips have high feasibility for the quick and approximate quantification of the biochemical substances in samples. Recently, test strips became more precise and accurate by involving the quantitative observation of coloration and regression modelling. Their accuracy and precision can be significantly improved by hue‐shifting the colours of the original sample and those of the standards. The standard colour images included in the instructions of test strips for the detection of four urinary and two salivary biochemical substances were used. Images of glucose‐loaded test strips for urinary glucose detection were also prepared. Using the red‐green‐blue (RGB) colour image, the author prepared 11 hue‐shifted colour images by changing the hue of the original RGB colour image at intervals of 30°. From each of the 12 colour images, 10 greyscale images indicating values of the intensity of RGB, cyan, magenta, yellow, key black, L*, a* and b* were prepared. In regression modelling, the additional 110 greyscale images improved the values of the coefficient of determination and normalised mean absolute error in the quantification of protein, specific gravity, urobilinogen and glucose in urine, as well as of alcohol and nitric oxide in saliva. Further analyses showed that the hue‐shifting technique yielded new greyscale images that were more significantly related to the values of the biochemical variables in question than any previous greyscale images. This improvement was realised by the most significant linear measures for the description of concentrations of biochemical substances, and synergetic effects between significant greyscale images.

Spatial Context Energy Curve-Based Multilevel 3-D Otsu Algorithm for Image Segmentation

While yielding satisfactory segmentation results for images with low SNR and poor contrast, one-dimensional (1-D) and two-dimensional (2-D) Otsu’s thresholding methods have the downside of high computational complexity. So far, three-dimensional (3-D) Otsu method has been based on histogram, which has only probability distribution of pixels as an object of interest. Histogram-based segmentation methods do not consider the contextual information which is significant to enrich the quality of segmented image. In this paper, a context-based 3-D Otsu algorithm has been proposed that considers the pixel intensity values as well as spatial information along with same properties of histogram. The proposed method is evaluated comprehensively with respect to quality and a detailed analysis is presented to compare the results of histogram-based 1-D, 2-D, and 3-D Otsu and energy-based 1-D, 2-D, and 3-D Otsu method, respectively. Experimental outcomes demonstrate the superiority of energy-based 3-D Otsu algorithm compared to histogram-based methods in terms of improved performance metrics, including mean error (ME), mean square error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), and entropy. Experiments on standard daily life color images have been carried out to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.

Research on blur discrimination thresholds of three-Channel for color image

On the basis of modern theory of color vision, the blur discrimination threshold of color image with three channels (luminance, red-green and the yellow-blue channel) in the opponent color space (DKL space) is studied through visual experiments in this paper. The visual evaluation experiment is designed by the psychophysical method based on Quest algorithm. Finding the blur discrimination threshold of color image according to the DKL space is of great significance for helping us understand color perception of image. It also has a wide range of applications in areas such as color image compression during color image transmission. Two (flowers and artificial object) typical images in the McGill standard color image database were chosen as the experimental images. After converting the color space of the image from RGB to DKL space, Gaussian low-pass filtering is used to establish the fuzzy image database. Quest algorithm which is a psychological physical method is used to design the visual evaluation experiment. Eleven volunteers with normal color vision were invited to participate in the visual evaluation experiment for each of the three channels of the two images. Our main conclusions are: (1) Human eyes are more sensitive to luminance information than color information. (2) The larger the contrast of the image and the smaller the uniformity of the image, the more sensitive the human eye is to changes in image blur.

DWT based color image watermarking using maximum entropy

Digital watermarking techniques can be used to solve the authenticity and copyright protection issue of images. In this work, the authors have proposed an adaptive color image watermarking scheme based on DWT by combining alpha blending and entropy concept. Entropy is one of the important features of images and can be used for watermark insertion. There are two domains in which watermarking can be carried out i.e. spatial and transform domain. Here, watermark embedding is carried out in the of Y component of YCbCr color space. This paper also lays down the proper justification for the selection of the Y component to embed the watermark. The performance of the proposed scheme is tested over seven different standard color images. The average PSNR and SSIM of the proposed scheme are 51.6145 dB and 0.9992 respectively. Whereas, NCC of the proposed scheme under no attack condition is 1. Further, the performance of the proposed scheme is compared with other state-of-the-art techniques.

Improved data hiding method for securing color images

Recently, data hiding techniques have become very popular in several vital applications, especially in telemedicine. The reason for this is their ability to give good results such as high embedding capacity while preserving visual image quality as much as possible after extracting the hidden secret message. In earlier studies, many researchers have achieved the goal of reversible data hiding (RDH) algorithm. All these methods have achieved excellent results on standard and natural images. However, in the case of medical images, especially color medical images, we face the problem of how to preserve the visual quality of image contents while achieving the goals of RDH in avoiding the loss of patient data or the distortion of the diagnosing image. In this paper, we proposed a secure data hiding method using a hyper chaotic map and left-most embedding strategy. The proposed methods are hybrid, where it is applied in the DCT frequency domain and encrypted domain together as presented here. This gives a higher embedding rate and higher visual image quality than existing methods without any loss or distortion of both hidden secrets data and reconstructed image. The novelty of this paper is to embed the desired secret data in each quantized block of DCT using (8-bit LMSB) strategy for embedding process. We tested our algorithm on both color medical images and standard color images of different sizes and different formats. We evaluated the performance of our algorithm on the basis of the quality metrics MSE, PSNR, BER, SSIM, Correlation, Symbol Error Rate, additional quality evaluation metrics, execution time, and different types of geometric and signal attacks. All of these parameters are demonstrated and represented in this proposed work in detail.

A high speed roller dung beetles clustering algorithm and its architecture for real-time image segmentation

Several practical applications like disaster detection, remote surveillance, object recognition using remote sensing satellite images, object monitoring and tracking using radar images etc. essentially require real-time image segmentation. In these applications computational complexity of the algorithm play a vital role along with accuracy. In this work image segmentation is dealt as a clustering problem and a bio-inspired algorithm based on the behavior of ‘Roller Dung Beetles (RDB)’ is proposed to determine effective solutions. The beauty of this proposed RDB Clustering architecture is its lower computational complexity O(N). The software implementation of the proposed algorithm is carried out in MATLAB environment and a hardware architecture is developed in Verilog HDL using Modelsim, Xilinx ISE for FPGA environment. The architecture has a comparison free sorting module, two data storage modules and a parallel threshold comparator unit, all of which use fewer mathematical operations. The performance of the proposed architecture is validated on many synthetic and standard benchmark color images. Further application of the proposed architecture is carried out for real-time segmentation of 8 NASA LANDSAT / ESA satellite images. Performance comparison has been carried out with other existing architectures based on Artificial Immune System (AIS), Genetic, K-means clustering, CNN etc. Simulation results reveal that the proposed RDBC architecture is 42% faster than the K-Means implementation with a clock frequency of 230.52MHz with an increased PSNR of 6.825% and SSIM of 15.55%. Statistical analysis and silhouette index also confirms the superiority of new clustering architecture over existing implementations.Compared to CNN the RDBC architecture is economical both in terms of lower chip-area and power consumption.

Computational Filters for Dental and Oral Lesion Visualization in Spectral Images

Clinically interesting low-contrast dental and oral features can be challenging to detect. In visual observation and clinical photographs, identification of low-contrast features can be hard or even impossible. Imaging methods, e.g., X-ray and magnetic resonance imaging, provide more information but often require use of ionizing radiation, expensive equipment, and specialized personnel to operate the devices. A cost-effective, non-ionizing, contrast-enhancing imaging method that can be used at any dental clinic is in great demand. Here we show a dental and oral feature visibility-enhancement based on a portable spectral camera and computational filters derived from principal component analysis. By applying computational filters on oral and dental spectral images, selected features of clinical interest can be highlighted against their surroundings. Due to the lack of information available in standard color images, this visibility-enhancement technique can only be realized using spectral images. Oral and dental spectral imaging does not use ionizing radiation, and modern spectral cameras are small, portable, and can be used without specialized training. In this paper, spectral image-based visibility-enhancement is demonstrated for the following cases: gingival recession, calculus, gingivitis, root caries, secondary caries, Fordyce’s granules, leukoplakia, and pigmentous lesions. The results gained with spectral images and computational filters from principal component analysis are compared against regular color images and grayscale images computed with band-pass filters from our earlier work. The results are promising as the visibility and contrast of the features of interests are enhanced in all the studied cases. This study provides a starting point for future research and demonstrates the applicability of spectral imaging-based methods for practical use at dental clinics.

This issue of ACTA, printed and electronic

This issue of ACTA, printed and electronic

Crop researchers harness artificial intelligence to breed crops for the changing climate

Until recently, the field of plant breeding looked a lot like it did in centuries past. A breeder might examine, for example, which tomato plants were most resistant to drought and then cross the most promising plants to produce the most drought-resistant offspring. This process would be repeated, plant generation after generation, until, over the course of roughly seven years, the breeder arrived at what seemed the optimal variety. Researchers at ETH Zurich use standard color images and thermal images collected by drone to determine how plots of wheat with different genotypes vary in grain ripeness. Image credit: Norbert Kirchgessner (ETH Zurich, Zurich, Switzerland). Now, with the global population expected to swell to nearly 10 billion by 2050 (1) and climate change shifting growing conditions (2), crop breeder and geneticist Steven Tanksley doesn’t think plant breeders have that kind of time. “We have to double the productivity per acre of our major crops if we’re going to stay on par with the world’s needs,” says Tanksley, a professor emeritus at Cornell University in Ithaca, NY. To speed up the process, Tanksley and others are turning to artificial intelligence (AI). Using computer science techniques, breeders can rapidly assess which plants grow the fastest in a particular climate, which genes help plants thrive there, and which plants, when crossed, produce an optimum combination of genes for a given location, opting for traits that boost yield and stave off the effects of a changing climate. Large seed companies in particular have been using components of AI for more than a decade. With computing power rapidly advancing, the techniques are now poised to accelerate breeding on a broader scale. AI is not, however, a panacea. Crop breeders still grapple with tradeoffs such as higher yield versus marketable appearance. And even the most sophisticated AI …

A fused contextual color image thresholding using cuttlefish algorithm

In this paper, we have proposed a fusion-based context-sensitive Masi energy curve model for multi-level thresholding exploiting cuttlefish algorithm (CFA). The proposed algorithm is simple and very efficient for the task of color image segmentation. Although Masi entropy exploits the additive/non-extensive information with the aid of a concordant entropic parameter, the performance is observed to be poor in the case of color image segmentation. Improved results can be obtained by using the concept of energy curve with Masi entropy at the cost of increased computational cost while selecting the suitable thresholds. To overcome the aforementioned drawbacks as well as to increase the quality of the segmented image, a simple multi-level thresholding method is proposed in this paper. The proposed color image segmentation scheme exploits the concept of local contrast fusion along with CFA to resolve the aforementioned issues. In order to prove the effectiveness of the proposed scheme, experimental evaluations on standard daily-life color images have been reported in this paper. The experimental outputs demonstrate that fusion-based multi-level thresholding is better than the existing dominant segmentation methods.

Prevalence of epiretinal membranes in the ageing population using retinal colour images and SD-OCT: the Alienor Study.

To analyse and compare the prevalence of epiretinal membranes (ERMs) obtained using either standard retinal colour images or spectral-domain optical coherence tomography (SD-OCT) in a population-based setting of French elderly subjects. Six hundred twenty-four subjects of the Alienor cohort aged 75years or older underwent both colour fundus imaging and SD-OCT examinations. The ERMs were graded from retinal images and SD-OCT macular scans in a masked fashion. On SD-OCT images, the early ERMs, mature contractile ERMs without foveal modifications and mature contractile ERMs with foveal alterations were distinguished. 610 (97.8%) subjects had gradable SD-OCT examinations, and 511 (81.9%) had gradable fundus images in at least one eye. According to colour photographs, 11.6% of participants had definite ERMs. From SD-OCT images, 52.8% of the subjects had early ERMs, 7.4% had mature ERMs without foveal involvement, and 9.7% had mature ERMs with foveal alterations. Regardless of the imaging method used, the ERMs were more often observed in pseudophakic eyes than in phakic eyes. Comparison of ERM assessment using fundus photographs versus SD-OCT images demonstrated that the specificity of retinal colour images was good (>89.3%), whereas the sensitivity remained low even though it increased with ERM severity on SD-OCT images. Spectral-domain optical coherence tomography (SD-OCT) examinations have high feasibility in this elderly population and are much more sensitive than standard colour images for ERM assessments, especially in the early stages of the disease. Our results further highlight the need to use SD-OCT instead of colour retinal photographs for the classification of ERMs in epidemiological studies.

Saliency Consistency-Based Image Re-Colorization for Color Blindness

For patients with color vision defects, owing to the destruction of cone cells and loss of function, some color information is lost, hence changing the originally transmitted information by the image. The purpose of the traditional method of correction is to help patients distinguish between colors, but it does not take into account the problem of the saliency of the image. In this paper, we propose a saliency consistency-based image re-colorization for color blindness. We use image retrieval methods to select a large number of images and use co-saliency methods to detect salient areas of standard color images and color-blind simulated images. According to the detection results, an image with the same detection result was selected as the reference image. We grayscale the significantly changed image, recolor the grayscale image using the reference image, and the color scheme of the recolored image is similar to the reference image. The color matching scheme of the reference image makes the significance of the image basically unchanged in standard vision and color vision defects, thereby making the color blindness patient’s perception of the image close to standard vision. We invite green blind patients to evaluate CVD simulation images with different recoloring methods subjectively. In addition, we use different evaluation criteria to evaluate the experimental results objectively. In the subjective evaluation and objective evaluation, the method proposed in this paper has achieved good results, which validates the effectiveness of our method.

An efficient image privacy scheme based on nonlinear chaotic system and linear canonical transformation

Making information invulnerable is one of the most critical issues of today’s era where information is being sent from one source to another destination with expeditious rates. In this research article, linear canonical transform (LCT) along with Lorenz differential equation is subjected for the process of double image encryption for its inherent characteristics which implies the diffusion and confusion of current era. In the contemplated method standard color images; Lena and Pepper of size 256×256 are used. Image scrambling, LCT of first order and second order along with Lorenz system are adapted as the steps on encryption process. The reverse process of defined encryption scheme is endorsed for the reformation of the original plain images separately from the single encrypted image. For quality assessment of the encryption scheme; different security and statistical analysis are performed and histograms of all the images are analyzed. As a result, statistical assessments recommended that our contemplated scheme is legitimate for the security of digital images.

An efficient optimal multilevel image thresholding with electromagnetism-like mechanism

Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.