Image Analysis Methodology Research Articles

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine.

Infertility is increasing worldwide; male factors can be identified in nearly half of all infertile couples. Histopathologic evaluation of testicular tissue can provide valuable information about infertility; however, several different evaluation methods and semi-quantitative score systems exist. Our goal was to describe a new, accurate and easy-to-use quantitative computer-based histomorphometric-mathematical image analysis methodology for the analysis of testicular tissue. On digitized, original hematoxylin-eosin (HE)-stained slides (scanned by slide-scanner), quantitatively describable characteristics such as area, perimeter and diameter of testis cross-sections and of individual tubules were measured with the help of continuous magnification. Immunohistochemically (IHC)-stained slides were digitized with a microscope-coupled camera, and IHC-staining intensity measurements on digitized images were also taken. Suggested methods are presented with mathematical equations, step-by-step detailed characterization and representative images are given. Our novel quantitative histomorphometric-mathematical image analysis method can improve the reproducibility, objectivity, quality and comparability of andrological-reproductive medicine research by recognizing even the mild impairments of the testicular structure expressed numerically, which might not be detected with the present semi-quantitative score systems. The technique is apt to be subjected to further automation with machine learning and artificial intelligence and can be named ‘Computer-Assisted or -Aided Testis Histology’ (CATHI).

Inference Tools for Markov Random Fields on Lattices: The R Package mrf2d

Markov random fields on two-dimensional lattices are behind many image analysis methodologies. mrf2d provides tools for statistical inference on a class of discrete stationary Markov random field models with pairwise interaction, which includes many of the popular models such as the Potts model and texture image models. The package introduces representations of dependence structures and parameters, visualization functions and efficient (C++ based) implementations of sampling algorithms, common estimation methods and other key features of the model, providing a useful framework to implement algorithms and working with the model in general. This paper presents a description and details of the package, as well as some reproducible examples of usage.

Microfluidic droplet detection via region-based and single-pass convolutional neural networks with comparison to conventional image analysis methodologies

As the complexity of microfluidic experiments and the associated image data volumes scale, traditional feature extraction approaches begin to struggle at both detection and analysis pipeline throughput. Deep-neural networks trained to detect certain objects are rapidly emerging as data gathering tools that can either match or outperform the analysis capabilities of the conventional methods used in microfluidic emulsion science. We demonstrate that two types of neural-networks, You Only Look Once (YOLOv3, YOLOv5) and Faster R-CNN, can be trained on a dataset which comprises of droplets generated across several microfluidic experiments and systems. The latitude of droplets used for training and validation, produce model weights which are easily transitive to emulsion systems at large, while completely circumventing any necessity of manual feature extraction. In flow cell experiments which comprised of greater than either 10,000 mono- or polydisperse droplets, the models show excellent or superior statistical symmetry to classical implementations of the Hough transform or widely utilized ImageJ plugins. In more complex chip architectures which simulate porous media, the produced image data typically requires heavy pre-processing to extrapolate valid data, where the models were able to handle raw input and produce size distributions with accuracy of ± 2μm for intermediate magnifications. This data harvesting fidelity is extended to foreign datasets not included in the training such as micrograph observation of various emulsified systems. Implementing these neural networks as the sole feature extraction tools in these microfluidic systems not only makes the data pipelining more efficient but opens the door for live detection and development of autonomous microfluidic experimental platforms due to inference times of greater than 100 frames per second.

Image analysis methodology for a quantitative evaluation of coating abrasion resistance

Superhydrophobic materials possess great potential in a range of fields. However, the utilisation of such coatings in real-life applications remains limited, mainly due to their low physical resilience. The fabrication of high-strength superhydrophobic materials is a highly active area in the literature, and the development of techniques to assess their resilience is highly important. Existing resilience analysis of superhydrophobic materials is able to provide some level of understanding. However, reported approaches are often wildly different, in the type of degradation used and the applied analysis method. Comprehensive analysis is rarely carried out as this requires complex operations or equipment, and/or specific testing requirements, which are not widely accessible. As a result, many studies provide limited or solely qualitative insight. Here, we report a methodology for a quantitative assessment of the resilience of superhydrophobic coatings. The method combines physical abrasion with optical imaging (electronic scanner) and mass-loss tracking. Combined examination using image processing and mass-loss allows for achieving a greater understanding of degradation behaviour, including differentiation between cohesive/adhesive failures.

The influence of image analysis methodology on the calculation of granular temperature for granular flows

The granular temperature is an index of the level of collisional activity in a granular flow, and increasingly important in the verification of extended kinetic theories. The granular temperature is related to the square of the difference between a particle’s velocity and that of the group mean. Image analysis of high-speed video is the most common method to measure granular temperature in experimental flows and depends on correlation of a search mask or a portion of the original image to the next image frame to determine the particle’s movement. This invariably involves some level of estimation of the location at a resolution finer than the pixels that make up the image. However, errors in determining particle movement at the subpixel level can be shown to have a significant impact on granular temperature identification. We show that taking particle movement to be a chain of displacement vectors provides context to the apparent impulses on the particle. Here we propose two novel methods for determining the granular temperature of experimental flows, namely a novel method of initializing Particle Image Velocimetry (PIV) for granular systems where each search subset is centred on a previously determined particle location to reduce bias, and a method of filtering the apparent impulses on a particle on a frequency basis. We term these methods Guided-PIV and Impulse Frequency Filtering (IFF), respectively. In a verification exercise using synthetically generated images, we show Guided-PIV to produce substantially more accurate results than ordinary applications of PIV. The IFF method is shown to greatly reduce the influence of analyzed framerate on granular temperature results. Our results demonstrate practical improvements for granular temperature identification from image analysis, throughout a range of experimental image quality levels, and we anticipate that these improvements will enable experimental assessment towards verification of theorized models of collisional-frictional granular flows.

Identification, quantification, and growth profiling of eight different microalgae species using image analysis

Algal blooms and associated oxygen depletion have devastating consequences for freshwater ecosystems. Identifying and quantifying microalgae species require highly expensive equipment that is unaffordable to many research units, mostly those in developing countries. The development of a low-cost technique to quantify the microalgae species could reduce the research expenses and boost microalgae research in developing nations. Therefore, a cost-effective and straightforward microalgae quantification method using an image analysis methodology was developed in this study. Eight different microalgae strains include Oocystis minuta, Synechococcus sp., Acrochaetium sp., Rhodosorus marinus, Chaetoceros gracilis, Isochrysis galbana, Phormidium inundatum, and Phormidium fragile were cultured, and their images were captured during the growth periods (0–28 days). A user-coded plugin known as MAIJA – Microalgae ImageJ Analyzer was developed and used for analyzing the images. The principle involved in microalgae quantification relied on the color intensity increase along with the growth of algae. Seven different color vegetation indices were employed to determine a suitable index for algae quantification. Results indicated that the algae species can be differentiated between homogenously and non-homogeneously dissolving species using the developed approach. Linear models were developed to estimate dry cell mass from culture volume (0.86 ≥ R2 ≤ 0.99) as well as from different vegetation indices (R2 up to 0.99). Among five color kinetic models tested, results showed that the first-order kinetic model with vegetative index was the most suitable indicator to obtain the trend of algae growth (RMSE = 0.2–0.4). Further, the vegetative index was also found to be suitable for differentiating similar colored algae species (Oocystis minuta vs. Synechococcus sp., and Phormidium fragile vs. Rhodosorus marinus). The developed method has the potential to improve the quantification of mixed microalgae culture compared to the traditional solid dried mass method.

CORRIGENDUM

Hydrological ProcessesVolume 35, Issue 9 CORRIGENDUMFree Access CORRIGENDUM This article corrects the following: Condensation water in heritage touristic caves: Isotopic and hydrochemical data and a new approach for its quantification through image analysis Cristina Liñán, José Benavente, Yolanda del Rosal, Iñaki Vadillo, Lucía Ojeda, Francisco Carrasco, Volume 35Issue 5Hydrological Processes First Published online: May 27, 2021 First published: 22 September 2021 https://doi.org/10.1002/hyp.14355AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In the referenced article, Liñán et al. (2021), the funding statement for the open access charge was missing from the published version of record. It has been added to Acknowledgements section which should now read as follows ACKNOWLEDGEMENTS This work is the result from the studies performed by the Geology area of the General Project for Interdisciplinary Research of the Nerja Cave, which is specifically aimed at the conservation of the cavity. The project is financed by the Nerja Cave Foundation, coordinated by its Research Institute and authorized by the Consejería de Cultura (Junta de Andalucía). The authors thank GeoTranslations for revising the English text, Manuela Vega for her support with image analysis methodology and three anonymous reviewers who have contributed to improving this article. This work is a contribution to the Research Groups RNM-308 and RNM-126 of the Junta de Andalucía. Funding for open access charge: Universidad de Málaga / CBUA. REFERENCE Liñán, C., Benavente, J., del Rosal, Y., Vadillo, I., Ojeda, L., & Carrasco, F. (2021). Condensation water in heritage touristic caves: Isotopic and hydrochemical data and a new approach for its quantification through image analysis. Hydrological Processes, 35, e14083. https://doi.org/10.1002/hyp.14083Wiley Online LibraryCASWeb of Science®Google Scholar Volume35, Issue9September 2021 ReferencesRelatedInformation

A Study on the Color Analysis by Genderless Fashion Expression Characteristics

This study was aimed at analyzing the factors of genderless fashion design and objectifying various categories of neutral images that are colorful and the boundaries of which are vaguely used. The method of this study involved literature research and color image analysis. First, we formulated a theoretical basis for empirical research to derive genderless colors by typifying genderless expression characteristics through prior work. Second, the color image analysis methodology was implemented through an analysis of the color analysis program. From the 2011 S/S collection to the 2020 F/W collection, 20 seasons were analyzed, and there were 120 collections of 20 seasons. On the basis of that, 480 colors of genderless expression characteristics were derived using the Korea Standard Color Analysis Program, and categories and areas of genderless color were analyzed. The results of this study are as follows. The color analysis by genderless expression characteristics showed that genderless’s compromise was dark blue (PB), with low-light and grayish-toned purple blue (PB), and with the highest frequency of colorless (N) and gender. In the obfuscated series, beige and brown’s calm color images were shown, and in the same color, purple blue (PB), purple (P), and blue (B), were analyzed as benign with masculinity. Enjoyment was shown to have a high sense of color rhythm, with low-light dark tones and high-light strong tones. The colors shown in sensuality were mainly achromatic (N) and warm-tone, indicating that the frequency of clothes using skin-exposed materials was high. The colors shown in genderless s exaggeration were analyzed to be used as a means of maximizing and exaggerating the appearance of costumes using medium- and medium-high-light red (R), purple blue (PB), and red purple (RP) with low-light base colors. Historicity is a strong-toned gold color that represents an antique and heavy image, while other obtuse colors represent a past-oriented, calm, and formal image.

Novel quantitative digital image analysis methodology for assessment of inflammatory changes in MRI data in a post-hoc analysis of data acquired from a phase IIb study of baricitinib in patients with active rheumatoid arthritis

PurposeTo evaluate a novel quantitative methodology to assess inflammatory changes in magnetic resonance imaging (MRI) data from patients with rheumatoid arthritis (RA) and the impact of image quality on imaging outcomes compared to the RA Magnetic Resonance Imaging Score (RAMRIS). MethodsThree-dimensional, T1-weighted, fat-suppressed MRI sequences of the hand/wrist before and after intravenous Gadolinium contrast from patients with RA in a placebo-controlled clinical trial (NCT01185353) were re-evaluated post hoc. The methodology was integrated into proprietary software (DYNAMIKA®) and assessed inflammation through pixelated measurements of the contrast-enhancing (inflammatory) volume. A semi-automatic approach outlined contrast-enhancing synovial tissue in the wrist and second to fifth metacarpophalangeal joints with a rough region of interest (ROI); quantitative imaging biomarkers were generated by means of quantitative total volume of inflammation and quantitative degree of inflammation relative to the signal in a 1 cm in diameter ROI in the center of the thenar or lumbrical muscle for internal reference. The time from Gadolinium injection to finalization of the post-contrast images was calculated from the images’ Digital Imaging and Communications in Medicine header. An experienced reader graded image quality as poor, acceptable, or good. ResultsResults from this quantitative methodology, especially when excluding images with poor quality scores (14–32%), provided a more pronounced and monotonically increasing dose-response than the original RAMRIS results on synovitis and osteitis. ConclusionsThis computer-aided quantitative scoring method provided continuous measures of inflammatory changes relative to muscle and may be more sensitive and interpretable concerning dose/response separation between RA treatment groups.

Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies.

Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data.

In-line cosmetic end-point detection of batch coating processes for colored tablets using multivariate image analysis

In this study, an in-line Process Analytical Technology (PAT) for cosmetic (non-functional) coating unit operations is developed using images of the tablet bed acquired in real-time by an inexpensive industrial camera and lighting system. The cosmetic end-point of multiple batches, run under different operating conditions, is automatically computed from these images using a Multivariate Image Analysis (MIA) methodology in conjunction with a stability determination strategy. The end-points detected by the algorithm differed, on average, by 3% in terms of total batch time from those identified visually by a trained operator. Since traditional practice typically relies on a coating overage to ensure full batch aspect homogeneity in the face of disturbances, the current in-line method can be used to reduce coating material and processing time (over 40% for the operating policy adopted in this work). Additionally, monitoring of the color features calculated by the algorithm allowed the identification of abnormal process conditions affecting visible coating uniformity. This work also addresses practical challenges related to image acquisition in the harsh environment of a pan coater, bringing this tool closer to a state of maturity for implementation in production units and opening the path for their optimization, monitoring, and automatic control.

Complex population dynamics in a spatial microbial ecosystem with Physarum polycephalum

This research addresses the interactions between the unicellular slime mold Physarum polycephalum and a red yeast in a spatial ecosystem over week-long imaging experiments. An inverse relationship between the growth rates of both species is shown, where P. polycephalum has positive growth when the red yeast has a negative growth rate and vice versa. The data also captures successional and oscillatory dynamics between both species. An advanced image analysis methodology for semantic segmentation is used to quantify population density over time, for all components of the ecosystem. We suggest that P. polycephalum is capable of exhibiting a sustainable feeding strategy by depositing a nutritive slime trail, allowing yeast to serve as a periodic food source. This opens a new direction of P. polycephalum research, where the population dynamics of spatial ecosystems can be readily quantified and complex ecological dynamics can be studied.

The influence of new and used formwork coated with different release agents on the appearance of the formed concrete surface

The influence of silicon oil and liquid wax based release agents on the formation of surface voids and colour variation in cast-in-place concrete was studied. The performance of the said release agents was compared to conventional release agents based on water-soluble emulsion and mineral oil. In addition, new and used formwork panels made of plywood and steel were used in the research to determine the effect of release agents on contact angle values. The quality of surface finish was evaluated in terms of concrete surface void ratio using the standard image analysis methodology, and in terms of colour variation using a new digital colour reading method. The formed concrete surface category was evaluated according to CIB, ACI, DBV and ÖVBB standards in order to know the applicability limits of liquid wax and silicon oil based release agents. The results revealed that the amount of release agent used and the contact angle values on the facings analysed depend on the formwork surface roughness, i.e. the release agents can lodge inside the formwork surface asperities and trap air bubbles at the formwork/concrete interface. Furthermore, the results showed that plywood coated with silicon oil and liquid wax limited the achievement of the formed concrete surface category that is higher than the surface finish satisfying normal requirements. Steel coated with liquid wax did not produce the formed concrete surface of higher than normal quality either, while the surface voids ratio of concrete cast against steel formwork coated with silicon oil did not satisfy the requirements even of the lowest classification according to ACI, DBV and ÖVBB standards. Colour variation analysis revealed that the formwork with higher absorbing capacity produced a darker colour with visual blackness varying in the range from 45% to 60%, whereas the formwork with low absorbing capacity produced a lighter colour with visual blackness varying in the range from 25% to 35%. Rougher surfaces of the formwork absorb more hydrophilic release agents. This has an effect on concrete surface colour variation and on concrete's hue, mainly on the yellow hue with redness or the green hue with yellowness.

Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

Validation of a quantitative image analysis methodology for the assessment of the morphology and structure of aerobic granular sludge in the presence of pharmaceutically active compounds

Aerobic granular sludge is considered a promising technology for biological wastewater treatment, since it is more compact and resistant to shock and toxic loads than activated sludge systems. Furthermore, the monitoring of the aerobic granules structure, size and morphology has been growing in importance, proving to be suitable for operational and process control purposes. In this work, a sampling methodology is validated for the assessment of the ensemble granular and floccular biomass, by quantitative image analysis, in the presence of pharmaceutically active compounds, including 17 β -estradiol and 17 α -ethinylestradiol and the sulfamethoxazole. The employed sampling volumes were found to be adequate in obtaining a representative number of granules for all samples. Indeed, it was always obtained a variation lower than 5% in the average and standard deviation criterion of the main size, morphological and structural parameters. Moreover, it was also assessed the performance of the sieving process in successfully separating the granular and floccular biomass fractions and established a lower bound fraction size cut-off for the employed sieve. In addition, the continuity of the performed methodology and the image acquisition triplicates approach could also be validated. The ability of the proposed QIA procedure to monitor the studied AGS system was corroborated by the fact that 95% of the samples were able to be classified in the correct group (proof of concept).

Investigation of impact performance of 3-dimensional interlock polymer fabrics in double and multi-angle pass stabbing

This paper presents the influence of fabric structures and stab angles on single-pass stab impact and the repeated and multi-angle stabbings of 3-dimensional (3D) warp interlock structural fabrics based on the high-molecular-weight polyethylene (HMWPE) yarns. Five kinds of samples with different structures were experimentally tested using the drop weight stabbing impact equipment at the same impact energy. Radar charts are plotted to demonstrate the differences among different parameters to a better understanding and a more global characterization of single-pass stabbing impact. An image analysis methodology of Structure From Motion (SFM) is used to compare the fabric deformation in Z direction after the stabbing tests. Results indicated that fabric structures have significant effects on the stab impact property that the orthogonal/through-the-thickness interlock fabric has a good stab impact resistance. Besides, the fabric with stab angle of 90° exhibited better single-pass stab impact property than the counterparts with stab angle of 0°. More importantly, the double-pass stabbing tests are compared to the ones of the single-pass stabbing which shows that a better condition occurs when the stab angles are identical in each pass.

Nanostructure quantification of turbostratic carbon by HRTEM image analysis: State of the art, biases, sensitivity and best practices

High Resolution Transmission Electron Microscopy (HRTEM) has been a widely used technique for the analysis of nanostructure of turbostratic carbon. Since the late nineties, digital imaging and image analysis has made it possible to quantify and derive structural properties from HRTEM images. A number of image analysis methodologies have been proposed, based on which numerous studies drew their conclusions about a variety of materials including soot, carbon black, char, black carbon, carbon nano-onions, fullerenes, pyrocarbon, hard carbon, etc. Recognizing the need for standardization in order to make measurements comparable, repeatable and reproducible, this study is a review of published techniques and a critical analysis of a representative set of them. Based on sensitivity analysis using an extensive dataset of micrographs, best practices for image acquisition and image analysis are provided. Measurement uncertainties and sensitivities to image analysis parameters are estimated. A set of improvements to the state-of-the-art is proposed. Computer code for the presented methods is made available.

Clinical evaluation of SARS-CoV-2 lung HRCT and RT-PCR Techniques: Towards risk factor based diagnosis of infectious diseases

This study uses image analysis techniques for comparative analysis of the lung HRCT features and RT-PCR of 325 suspected patients to COVID-19 pneumonia. Our findings propose more caution in the interpretation of RT-PCR data, promoting, instead, also the quantification of age and sex-based risk factors using HRCT images. Statistical analysis of our methodology reveals a direct relation between intensity, skewness and kurtosis of the radiological features and the gender of patients. Moreover, we investigate the effect of the age of patients on the appearance of COVID-19 pneumonia in the HRCT images. We have also applied our methodology to investigate the effect of time on the severity of COVID-19 pneumonia within the lungs. Subsequently, we find a strong relationship between image analysis and the informed medical diagnosis asserted by the radiologists. Additionally, our results also indicate increase in the severity of lung infection in the first and second week after the onset of the SARS-CoV-2 symptoms. Thereafter, a gradual decrease in the lung damage is observed during the third week. The proposed image analysis methodology can be used as a simple complementary tool for infectious disease diagnostics as demonstrated in this study with an example of SARS-CoV-2 to provide better understanding of the disease for drug and vaccine development.

Multi-Regional landslide detection using combined unsupervised and supervised machine learning

Landslide detection is concerned with delineating the extent of landslides. Most of existing works on landslide detection have limited geographical extents. Therefore, the models developed out of these studies might perform poorly when applied to regions with different characteristics. This study investigates an Object-Based Image Analysis methodology built on unsupervised and supervised Machine Learning to detect the location of landslides occurred in multiple regions across the world. The utilized data includes Sentinel-2 multi-spectral satellite imagery and ALOS Digital Elevation Model. In the segmentation stage, pre and post-landslide images undergo segmentation using K-means clustering. Following the segmentation stage and dataset preparation and removing highly-correlated features from the dataset, two Random Forest classifiers (RF1 and RF2) are trained and tested on two different datasets to measure the generalization level of the algorithms with RF1 dataset spanning over more geographical diversities than RF2 dataset. The results show that the RF models can successfully detect landslide segments with test precision = 0.96 and recall = 0.96 for RF1 and test precision = 0.90 and recall = 0.87 for RF2. Further validation shows that, compared to RF2, RF1 results in less mislabelled non-landslide segments.

A techno-sustainable bio-waste management strategy for closing chickpea yield gap

Sustainable development goals imply environmentally sound management of all wastes to minimize the waste generation through prevention, reduction, recycling, and reuse. In particular, the poultry industry produces nutrient-rich waste that requires proper management.Additionally, the recycling of bio-wastes in agricultural lands is still a key technology for the sustainable use of nutrients as a renewable fertilizer. Currently, there are very few studies on the utilization of agro-industrial bio-wastes, such as poultry abattoir sludge (PAS), for crop cultivation in soils containing low organic matter and high pH. In this context, it is necessary to make a more particular assessment of poultry industry-oriented and locally available nutrient-rich organic wastes for nodulation, physiological adaptation, and crop yield. Considering the scarcity of the literature in this field, the present study aimed to fulfill the apparent gap by focusing on the applicability of recycled PAS to low fertility soil in the growth of chickpea selected as a model legume, thereby contributing to the development of an agricultural and sustainable industrial management strategy for the relevant sectors. In this study, leaf chlorophyll content and nodule color were also investigated by the image analysis methodology to describe the effects of bio-waste on closing chickpea yield gap in a marginal land with high soil pH and low organic matter. Two-year consecutive field experiments were carried out to explore the effect of the PAS with the application rates of 25 kg N ha−1 (T2), 50 kg N ha−1 (T3), and 100 kg N ha−1 (T4) along with unamended (T0) and fertilized control (T1). The results indicated that the PAS treatments significantly differed in chlorophyll content, nodulation parameters, and biomass and grain yields. The chlorophyll content was correlated (r = 0.910) with the red color value (RGB color model) of nodule image analysis in the response to bio-waste. Based on the two-year average, it was concluded that chickpea yield could be increased 45% by amending with the PAS (T3). The present study clearly demonstrated that the image analysis could be a useful digital tool for the evaluation of chlorophyll content, nitrogen fixation efficiency, and forecasting biomass and grain yields of chickpea. The results also confirmed that the PAS application to low fertility soil could prominently contribute to establish sustainable waste management and crop production alternatives for closing chickpea yield gap.