Image Analysis Software Tools Research Articles

Algorithms and software for processing images of the structure of metallic materials for the purpose of determining creep characteristics

The paper contains a description of the approach, algorithms, and software tool for image analysis of deformed material structures. The developed approach is based on the analysis of the microstructure of the material to identify important factors that affect high-temperature deformation during creep in a heat-resistant nickel-based alloy, namely, the dimensions of the channels and their orientation. The developed software tool uses algorithms for converting images into binary black and white using the Otsu method. The intensity of the gradient at each point of the image is visualized using the Sobel operator. Fragment boundaries are determined using the Canny edge detector. Straight line segments are found using the Hough transformation. The software tool is implemented in the Python programming language using the OpenCV library. The main components are described and a flow chart of the program is provided. With the use of the developed software, the transformation of the known experimentally obtained images of the structures of the specimens from heat-resistant nickel-based alloy CMSX-4 deformed at a temperature of 1273K and in a wide range of stresses at different time moments was performed. The results of the analysis of the dimensions of the g-phase channels in the alloy using quantitative evaluation of transformed binary images are discussed. The found characteristics were matched to the value of the creep strain rate, which was determined by calculation based on known experimental data. The possibility of determining the transition from the secondary creep to the stage of avalanche-like growth of strains and hidden damage is shown. For the considered example, the location of the channels in the representative image was determined. A technique for correcting creep curves with the involvement of material structure image processing data is proposed.

Biofilm formation of the food spoiler Brochothrix thermosphacta on different industrial surface materials using a biofilm reactor

Brochothrix thermosphacta is considered as a major food spoiler bacteria. This study evaluates biofilm formation by B. thermosphacta CD337(2) – a strong biofilm producer strain – on three food industry materials (polycarbonate (PC), polystyrene (PS), and stainless steel (SS)). Biofilms were continuously grown under flow at 25 °C in BHI broth in a modified CDC biofilm reactor. Bacterial cells were enumerated by plate counting, and biofilm spatial organization was deciphered by combining confocal laser scanning microscopy and image analysis. The biofilms had the same growth kinetics on all three materials and reach 8log CFU/cm2 as maximal concentration. Highly structured biofilms were observed on PC and PS, but less structured ones on SS. This difference was confirmed by structural quantification analysis using the image analysis software tool BiofilmQ. Biofilm on SS show less roughness, density, thickness and volume. The biofilm 3D structure seemed to be related to the coupon topography and roughness.The materials used in this study do not affect biofilm growth. However, their roughness and topography affect the biofilm architecture, which could influence biofilm behaviour.

Optical microscopic imaging, manipulation, and analysis methods for morphogenesis research.

Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to understand how physical forces serve as both signaling components and driving forces of tissue deformations. In recent years, advances in microscopy techniques have led to improvements in imaging speed, resolution and depth. Concurrently, the development of various software packages has supported large-scale, analyses of challenging images at the single-cell resolution. While these tools have enhanced our ability to examine dynamics of cells and mechanical processes during morphogenesis, their effective integration requires specialized expertise. With this background, this review provides a practical overview of those techniques. First, we introduce microscopic techniques for multicellular imaging and image analysis software tools with a focus on cell segmentation and tracking. Second, we provide an overview of cutting-edge techniques for mechanical manipulation of cells and tissues. Finally, we introduce recent findings on morphogenetic mechanisms and mechanosensations that have been achieved by effectively combining microscopy, image analysis tools and mechanical manipulation techniques.

Blind and visually impaired-accessible software and learning resources open a new window to the universe

In late 2016, Research Supporting Multisensory Engagement by Blind, Visually Impaired, and Sighted Students to Advance Integrated Learning of Astronomy and Computer Science [also known as Innovators Developing Accessible Tools for Astronomy (IDATA)] was funded by the National Science Foundation. IDATA explored the role of computational thinking in astronomy. Our approach partnered blind and visually impaired (BVI) and sighted middle and high school students and their teachers with undergraduate students, software engineers, astronomers, educators, and education researchers to, in part, design and develop a new BVI-accessible image analysis software tool called Afterglow Access (AgA). The goal of the IDATA project is to allow all users, regardless of visual acuity or level of astronomy knowledge, to collect and analyze their own astronomical images.

Development of a comprehensive method for the dermatoscopic images analysis of the facial skin with acne

Background: One of the most common inflammatory chronic and recurrent skin diseases is acne (“acne vulgaris”), which appears itself as open or closed comedones and inflammatory skin lesions in the form of papules, pustules, nodes, etc. It has been established that acne is one of the most common dermatoses, since, according to modern data, it affects about 9.4% of the population. During adolescence, up to 90% of people suffer, and in adulthood — about 20% with varying degrees of severity. Currently, there are many approaches to treating this disease through various cosmetic treatments such as phototherapy, ultrasonic skin cleansing, Mesotherapy, chemical peels, and medication. Therefore, the development of methods and means of differential diagnosis of acne is one of the urgent tasks in the field of biomedical engineering, dermatology, and clinical medicine, since this allows timely identification of the localization of the disease, its causes, and prescribing appropriate treatment. However, the solution to the problem of monitoring the dynamics of external manifestations of the disease is possible only with the use of combined mathematical methods for image analysis. Objectives: To develop a comprehensive method for analyzing dermatoscopic images for monitoring the external manifestations of acne disease during treatment and isolating the affected areas of the facial skin. Materials and Methods: Dermatological preclinical researches of the skin were conducted in the laboratory of 3D-biomedical technologies of the Department of Biomedical Engineering of the Kharkiv National University of Radio Electronics, using a digital videodermatoscope BIO Bm6+ in daylight and a portable skin analyzer Skin Scope F-102 in the ultraviolet range. Clinical researches were conducted based on the Department of Pediatric Propaedeutics #2 of the Kharkiv National Medical University. The development of a software tool for image analysis was conducted out in Python programming using the libraries OpenCV, Scikit-image, Numpy, PIL, Mathplotlib. Determination of the affected skin areas and calculation of the parameters of inflammation were carried out using multi-Otsu methods and morphological segmentation of digital dermatoscopic images. Results: During the research, automated software was developed that allows to analyze in dynamics the nature of inflammatory processes and the area of facial skin lesions, as well as to carry out a differential diagnosis of acne disease. The proposed method for the analysis of dermatoscopic images makes it possible to perform color segmentation and obtain a map of the gradations of skin inflammations to control the dynamics during the prescribed treatment. Conclusions: The comprehensive method of analysis of dermatoscopic images of the skin of the face makes it possible to effectively control the condition of the skin of the face from acne during treatment, while analyzing the degree of inflammatory processes and the area of lesions, where, using the developed software, in an automated mode, red gradations are calculated to detect the boundaries of inflammation, geometric parameters and percentage of lesions in relation to healthy facial skin.

Improved online event detection and differentiation by a simple gradient-based nonlinear transformation: Implications for the biomedical signal and image analysis

Despite recent success in advanced signal analysis technologies, simple and universal methods are still of interest in a variety of applications. Wearable devices including biomedical monitoring and diagnostic systems suitable for long-term operation are prominent examples, where simple online signal analysis and early event detection algorithms are required. Here we suggest a simple and universal approach to the online detection of events represented by abrupt bursts in long-term observational data series. We show that simple gradient-based transformations obtained as a product of the signal and its derivative lead to the improved accuracy of the online detection of any significant bursts in the observational data series irrespective of their particular shapes. We provide explicit analytical expressions characterizing the performance of the suggested approach in comparison with the conventional solutions optimized for particular theoretical scenarios and widely utilized in various signal analysis applications. Moreover, we estimate the accuracy of the gradient-based approach in the exact positioning of single ECG cycles, where it outperforms the conventional Pan-Tompkins algorithm in its original formulation, while exhibiting comparable detection effectiveness. Finally, we show that our approach is also applicable to the comparative analysis of lanes in electrophoretic gel images widely used in life sciences and molecular diagnostics like restriction fragment length polymorphism (RFLP) and variable number tandem repeats (VNTR) methods. A simple software tool for the semi-automated electrophoretic gel image analysis based on the proposed gradient based methodology is freely available online at https://bitbucket.org/rogex/sds-page-image-analyzer/downloads/.

Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers.

Context:Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC).Aims:The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature.Settings and Design:Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected.Subjects and Methods:Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC).Results:Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51–0.54).Conclusions:Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.

Quantitative Neurotoxicology: An Assessment of the Neurotoxic Profile of Kainic Acid in Sprague Dawley Rats.

This study consisted of a qualitative and quantitative assessment of neuropathological changes in kainic acid (KA)-treated adult male rats. Rats were administered a single 10 mg/kg intraperitoneal injection of KA or the same volume of saline and sacrificed 24 or 48 hours posttreatment. Brains were collected, sectioned coronally (∼ 81 slices), and stained with amino cupric silver to reveal degenerative changes. For qualitative assessment of neural degeneration, sectioned material was evaluated by a board-certified pathologist, and the level of degeneration was graded based upon a 4-point scale. For measurement of quantitative neural degeneration in response to KA treatment, the HALO digital image analysis software tool was used. Quantitative measurements of specific regions within the brain were obtained from silver-stained tissue sections with quantitation based on stain color and optical density. This quantitative evaluation method identified degeneration primarily in the cerebral cortex, septal nuclei, amygdala, olfactory bulb, hippocampus, thalamus, and hypothalamus. The KA-produced neuronal degeneration in the cortex was primarily in the piriform, insular, rhinal, and cingulate areas. In the hippocampus, the dentate gyrus was found to be the most affected area. Our findings indicate global neurotoxicity due to KA treatment. Certain brain structures exhibited more degeneration than others, reflecting differential sensitivity or vulnerability of neurons to KA.

BacStalk: A comprehensive and interactive image analysis software tool for bacterial cell biology.

Prokaryotic cells display a striking subcellular organization. Studies of the underlying mechanisms in different species have greatly enhanced our understanding of the morphological and physiological adaptation of bacteria to different environmental niches. The image analysis software tool BacStalk is designed to extract comprehensive quantitative information from the images of morphologically complex bacteria with stalks, flagella, or other appendages. The resulting data can be visualized in interactive demographs, kymographs, cell lineage plots, and scatter plots to enable fast and thorough data analysis and representation. Notably, BacStalk can generate demographs and kymographs that display fluorescence signals within the two-dimensional cellular outlines, to accurately represent their subcellular location. Beyond organisms with visible appendages, BacStalk is also suitable for established, non-stalked model organisms with common or uncommon cell shapes. BacStalk, therefore, contributes to the advancement of prokaryotic cell biology and physiology, as it widens the spectrum of easily accessible model organisms and enables highly intuitive and interactive data analysis and visualization.

SpineJ: A software tool for quantitative analysis of nanoscale spine morphology.

Super-resolution microscopy provides diffraction-unlimited optical access to the intricate morphology of neurons in living brain tissue, resolving their finest structural details, which are critical for neuronal function. However, as existing image analysis software tools have been developed for diffraction-limited images, they are generally not well suited for quantifying nanoscale structures like dendritic spines. We present SpineJ, a semi-automatic ImageJ plugin that is specifically designed for this purpose. SpineJ offers an intuitive and user-friendly graphical user interface, facilitating fast, accurate, and unbiased analysis of spine morphology.

Improved multiparametric scrape loading-dye transfer assay for a simultaneous high-throughput analysis of gap junctional intercellular communication, cell density and viability

Gap junctional intercellular communication (GJIC) is a vital cellular process required for maintenance of tissue homeostasis. In vitro assessment of GJIC represents valuable phenotypic endpoint that could be effectively utilized as an integral component in modern toxicity testing, drug screening or biomedical in vitro research. However, currently available methods for quantifying GJIC with higher-throughputs typically require specialized equipment, proprietary software and/or genetically engineered cell models. To overcome these limitations, we present here an innovative adaptation of traditional, fluorescence microscopy-based scrape loading-dye transfer (SL-DT) assay, which has been optimized to simultaneously evaluate GJIC, cell density and viability. This multiparametric method was demonstrated to be suitable for various multiwell microplate formats, which facilitates an automatized image acquisition. The assay workflow is further assisted by an open source-based software tools for batch image processing, analysis and evaluation of GJIC, cell density and viability. Our results suggest that this approach provides a simple, fast, versatile and cost effective way for in vitro high-throughput assessment of GJIC and other related phenotypic cellular events, which could be included into in vitro screening and assessment of pharmacologically and toxicologically relevant compounds.

Image based Particle Shape Analysis Toolbox (IPSAT)

Shape analysis can provide vital information regarding the origin, transport and deposition history of grains. Particle shape measurement has been an active area of research for sedimentologists since the 20th century. With advancement in the field of computation and image analysis, shape analysis can be done in a faster and much more accurate way compared to manual measurements. The results obtained are reproducible as compared to visual qualitative analysis. However, there is a lack of image analysis software tools aimed at the field of sedimentology where the fine details of a particle boundaries are required. Image based Particle Shape Analysis Toolbox (IPSAT) developed in the Mathematica environment for the quantitative characterisation of sedimentary grains in 2-dimensions is presented here. This image analysis toolbox can be used to analyse consolidated as well as loose sediment samples. A total of 12 parameters are available for shape measurement comprising conventional shape parameters (roundness, angularity, circularity and irregularity), mathematically complex shape parameters (fractal dimension and Fourier descriptors) and common geometrical shape parameters (aspect ratio, convexity, solidity, mod ratio, rectangularity and compactness). Additionally, IPSAT offers to compute 6 particle size measurement parameters. Furthermore, 2-D particle size distribution can be transformed to a 3-D size distribution for thin section analysis. Example analyses have been carried out on a sandstone and a loose sediment sample. The toolbox presented here aims to establish a textural analysis methodology to be used by geologists and sedimentologists in particular. It will allow users to quantitatively characterise a large set of grains with a fast, cheap and robust methodology.

ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.

BackgroundThe spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key “hallmarks of cancer”. However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone.MethodsIn this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/.FindingsThe overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a “spatial map” of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage.InterpretationThe analysis pipeline developed in this study could convert the pathology image into a “spatial map” of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology workforce shortages, there is a clear need for a robust and evidence-based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM-Path) initiative and the British In Vitro Diagnostics Association (BIVDA) have set out a roadmap to help academia, industry, and clinicians develop new software tools to the point of approved clinical use. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays.

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

Phenotyping root traits for vegetable crop improvement: Advanced tools and techniques

Information on root system architecture is critical to understand crop or cultivar adaptations to variable and dynamic soil environments. Breeders are searching for certain root traits associated with enhanced resource acquisition capacity and adjustments to stress environments. However, the selection for root architectural traits in breeding programmees has been limited by the lack of appropriate root phenotyping techniques. Although vegetable crops possess wide variability for root traits and greatly vary in crop management strategies but a very limited progress has been reported on root system architecture assessment of these crops. This work critically summarizes the potential tools and techniques for high-throughput phenotyping of root architectural traits in vegetable crops. Both destructive as well as non-destructive techniques have been discussed. Several novel imaging technologies in conjunction with image analysis software tools present a great opportunity for in situ root system assessment for cultivar selection in vegetable breeding programmemes.

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

BackgroundCell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity.ResultsWe developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells.ConclusionMatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis.

AB290. SPR-17 Prosthesis insertion into segmented biomechanics simulation models

ObjectiveBiomechanical simulation requires accurate representation of the geometry of the structures to be studied. Specifically, when simulating the interaction of implanted prosthetics with surrounding tissues, the geometric and mechanical properties of the prosthesis and the surrounding tissues need to be adequately represented. The present work describes methods for inserting test prostheses into magnetic resonance imaging (MRI) derived geometric models of the pelvic floor structures, in order to create realistic simulation models.MethodsWe modified an existing public domain image analysis software tool to allow placement and segmentation of arbitrarily shaped 3D objects into the output segmented geometry of a pelvic MRI image dataset. The tool was applied to create composite segmented geometry and 3D models of the MRI derived pelvic floor structures with the inserted prostheses in the intended anatomic locations, suitable for biomechanical simulation model creation.ResultsSegmentations of the organs in the source pelvic MRI datasets were created, showing the segmented embedded prostheses in the planned location. Three dimensional reconstructions of the segmented datasets were generated, which were viewable from multiple angles, and the ability to turn on and off all tissue and prosthesis layers was demonstrated.ConclusionsWe created a software application for inserting prostheses into segmented MRI based datasets. The output segmentations were suitable for input into a soft-tissue simulation tool suite, which generated simulation results suitable for analysis. This tool has the potential to enable patient specific, iterative surgical planning of prolapse repair strategies.Funding Source(s)None

Otolith microstructure analysis reveals differentiated growth histories in sympatric black hakes (Merluccius polli and Merluccius senegalensis)

This is the first study to provide age estimations of the two black hake species living in Mauritanian waters. Here we propose a new interpretation of microstructural growth increments that are assumed to be deposited daily. Our approach to age-reading combining the counts in both sagittal and transverse planes proved successful with black hake otoliths, including adults’. A newly developed image analysis software tool (OTOLab) was used to help in identifying and marking daily growth increments throughout the otoliths. Larval growth was analyzed from otolith sagittal sections and showed wider mean daily increments in Merluccius polli (3.32μ) than in Merluccius senegalensis (2.85μ). Conversely, once recruited to demersal life after metamorphosis, juvenile and adult growth was analyzed from otolith transverse sections, where the widest mean daily increments were found in M. senegalensis (4.19μ) (M. polli, 3.19μ). Although the growth models of both species were not statistically different, the two black hakes probably have alternate growth strategies and, like the European hake, belong to the group of fast-growing hakes. When both species were considered together, the mean length of one-year-old black hakes was 36.7 cm, while the averaged individual growth rate was 1.12mmday−1. These growth values are the highest found for any hake species and are probably explained by the influence of one of the world’s most important upwelling systems, off northwest Africa. Although environmental factors may influence individual growth, ontogeny is the main factor determining growth rates at any life stage. Our results support otolith microstructural analysis as a realistic choice to regularly age hakes.

Open Source Systems In Digital Pathology

Introduction/ Background Digital pathology is rapidly becoming popular world- wide. It not only helps to understand the nature of the disease through the analysis of tissue-based images but also advances our statistical knowledge of the disease-related factors via high throughput analysis of the imaging data. Such analysis can be done by using either commercial or open source systems. Aims Generally in comparison to commercial software and depends upon the scientific question to be answered, implementation of open source systems tend to be more challenging. However, unlike commercial soft- ware, open source systems are free with a high level of flexibilities in their functionalities which in turn makes them suitable for designing complex image processing systems as well as performing massive data analysis. Methods Here I review OpenCV (Open Source Computer Vision), one of the most powerful library of programming functions mainly aimed at real-time computer vision and its application in Digital Pathology. I also briefly introduce the Image Segmentation & Registration (ITK) library which is another open-source, cross-platform system with extensive suite of software tools for image analysis. Finally I give an overview of two other open source software, i.e. Aladin and Topcat , developed mainly for astronomical applications, and their usage in Digital Pathology. Results Open source systems/software provide researcher with an alternative method to successfully address complex problems in Digital Pathology.