Automated Image Analysis Method Research Articles

A Study on Nuclei Segmentation, Feature Extraction and Disease Stage Classification for Human Brain Histopathological Images

Computer aided diagnosis (CAD) systems are important in obtaining precision medicine and patient driven solutions for various diseases. One of the main brain tumor is the Glioblastoma multiforme (GBM) and histopathological tissue images can provide unique insights into identifying and grading disease stages. In this study, we consider nuclei segmentation method, feature extraction and disease stage classification for brain tumor histopathological images using automatic image analysis methods. In particular we utilized automatic nuclei segmentation and labeling for histopathology image data obtained from The Cancer Genome Atlas (TCGA) and check for significance of feature descriptors using K-S test and classification accuracy using support vector machine (SVM) and Random Forests (RF). Our results indicate that we obtain classification accuracy 98.6% and 99.8% in the case of Object-Level features and 82.1% and 86.1% in the case of Spatial Arrangement features, respectively.

Challenges and Benchmarks in Bioimage Analysis.

Similar to the medical imaging community, the bioimaging community has recently realized the need to benchmark various image analysis methods to compare their performance and assess their suitability for specific applications. Challenges sponsored by prestigious conferences have proven to be an effective means of encouraging benchmarking and new algorithm development for a particular type of image data. Bioimage analysis challenges have recently complemented medical image analysis challenges, especially in the case of the International Symposium on Biomedical Imaging (ISBI). This review summarizes recent progress in this respect and describes the general process of designing a bioimage analysis benchmark or challenge, including the proper selection of datasets and evaluation metrics. It also presents examples of specific target applications and biological research tasks that have benefited from these challenges with respect to the performance of automatic image analysis methods that are crucial for the given task. Finally, available benchmarks and challenges in terms of common features, possible classification and implications drawn from the results are analysed.

석면섬유 자동계수를 위한 고효율 현미경법의 영상처리 알고리즘 개선

We developed a high-throughput microscopy (HTM) method which enabled us to replace a conventional phase contrast microscopy (PCM) method that has been used as a standard analytical method for airborne asbestos. We could obtain the concentration of airborne asbestos fibers under detection limit by automated image processing and analysis using HTM method. Here we propose an improved image processing algorithm with variable parameters to enhance the accuracy of the HTM analysis. Since the variable parameters that compensate the difference of the brightness are applied to the individual images in our new image processing method, it is possible to enhance the accuracy of the automatic image analysis method for sample slides with low asbestos concentration that caused errors in binary image processing. We demonstrated that enumeration of fibers by improved image processing algorithm remarkably enhanced the accuracy of HTM analysis in comparison with PCM. The improved HTM method can be a potential alternative to conventional PCM.

A novel automated image analysis method for counting the population of whiteflies on leaves of crops

Counting the population of insect pests is a key task for planning a successful integrated pest management program. Most image processing and machine vision techniques in the literature are very site-specific and cannot be easily re-usable because their performances are highly related to their ground truth data. In this article a new unsupervised image processing method is proposed which is general and easy to use for non-experts. In this method firstly a hypothesis framework is defined to distinguish pests from other particles in a captured image after texture, color and shape analyses. Then, the decision about each hypothesis is made by estimating a distribution function for sizes of particles which are presented in the image. Performance of the proposed method is evaluated on real captured images that belong to plants in green houses and farms with low and high densities of whiteflies. The obtained results show the greater ability of the proposed method in counting whiteflies on crop leaves compared to adaptive thresholding and K-means algorithms. Furthermore it is shown that better counting of the pest by proposed algorithm not only doesn't lead to extracting more false objects but also it decreases the rate of false detections compared to the results of the alternative algorithms.

Remote sensing analysis of riparian vegetation habitats

The high resolution remote sensing data is becoming available increasingly and facilitating the applications of automated image analysis methods and techniques. These methods are advantageous in the evaluation of natural and semi-natural riparian ecosystems where traditional in situ field surveys are very time consuming and often problematic because of inundation. This study analysed the Szigetkoz riparian wetland (Hungary) based on high resolution aerial imagery. The analysis proved that the detection of hybrid poplar stands is feasible through the application of decision tree image classification method. It is also concluded that a site-specific algorithm is transferable to the datasets with the same spectral and geometric resolutions of distant sites in the same wetland region applying the same spectral and textural parameters..

Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.

Extracting Salient Brain Patterns forClassification of Neurodegenerative Diseases

An accurate diagnosis of Alzheimer‟s diseases based on saliency map characterization is carried out on thirty two database images. This paper gives fully automatic image analysis method and attempts an approach for classification of brain images to find out pathology and normality part of brain by extracting salient features of input brain image and the region of interest is identified using kernel k-means algorithm. A support vector machine (SVM) a supervised learning process is used for classification of AD, which is identified as blue color is normal brain part and red color is pathology related.

An automatic counting system for transparent pelagic fish eggs based on computer vision

Alive eggs of marine species with pelagic eggs float, while dead eggs usually sink. A non-invasive method for counting the number of floating eggs therefore gives the possibility to track survival throughout experiments. In this paper we present an automatic image analysis method for counting live pelagic eggs of marine fish. Pelagic fish eggs are typically transparent and difficult to detect in images. Current image analysis methods for counting pelagic fish eggs are therefore done on eggs transfixed in a polymer to create contrast between the eggs and the background. This kills the eggs. The main advantage of the presented method is that it is non-invasive and only requires a minimum of handling of the eggs. As case studies we collected images of Atlantic haddock (Melanogrammus aeglefinus) and Atlantic cod (Gadus morhua) eggs. The eggs in the images were manually counted for verification of the methodology. The average counting error of false positives was 6% and the average counting error of false negatives was 2%. This demonstrates that the method is objective and accurate.

Positive regulation of neocortical synapse formation by the Plexin-D1 receptor

Synapse formation is a critical process during neural development and is coordinated by multiple signals. Several lines of evidence implicate the Plexin-D1 receptor in synaptogenesis. Studies have shown that Plexin-D1 signaling is involved in synaptic specificity and synapse formation in spinal cord and striatum. Expression of Plexin-D1 and its principal neural ligand, Sema3E, by neocortical neurons is temporally and spatially regulated, reaching the highest level at the time of synaptogenesis in mice. In this study, we examined the function of Plexin-D1 in synapse formation by primary neocortical neurons in vitro. A novel, automated image analysis method was developed to quantitate synapse formation under baseline conditions and with manipulation of Plexin-D1 levels. shRNA and overexpression manipulations caused opposite changes, with reduction resulting in less synapse formation, an effect distinct from that reported in the striatum. The data indicate that Plexin-D1 operates in a cell context-specific fashion, mediating different synaptogenic outcomes depending upon neuron type.

Spatial Organization of Oligodendrocytes and Pyramidal Neurons in the Anterior Limbic Cortex in Health and Schizophrenia: New Potentials for Computerized Morphometry

The spatial organization of oligodendrocytes and pyramidal neurons in the anterior limbic cortex in health and schizophrenia was studied using automated two-dimensional image analysis. Layer III of this area of the cortex in schizophrenia showed a 20% reduction in the minimal distance between oligodendrocytes and their nearest neurons and 10% and 9% reductions, respectively, in the mean maximum distance between neurons and their nearest oligodendrocytes. Thus, this automatic image analysis method allows assessment of the spatial organization of neurons and glial cells in the cerebral cortex not only in health, but also in mental pathology.

Quantification of Tumor Burden in a Genetically Engineered Mouse Model of Lung Cancer by Micro-CT and Automated Analysis

Genetically engineered mouse models (GEMMs) of lung cancer closely recapitulate the human disease but suffer from the difficulty of evaluating tumor growth by conventional methods. Herein, a novel automated image analysis method for estimating the lung tumor burden from in vivo micro-computed tomography (micro-CT) data is described. The proposed tumor burden metric is the segmented soft tissue volume contained within a chest space region of interest, excluding an estimate of the heart volume. The method was validated by comparison with previously published manual analysis methods and applied in two therapeutic studies in a mutant K-ras GEMM of non–small cell lung carcinoma. Mice were imaged by micro-CT pre-treatment and stratified into four treatment groups: an antibody inhibiting vascular endothelial growth factor (anti-VEGF), chemotherapy, combination of anti-VEGF and chemotherapy, or control antibody. In the first study, post-treatment imaging was performed 4 weeks later. In the second study, mice were scanned serially on a high-throughput scanner every 2 weeks for 8 weeks during treatment. In both studies, the automated tumor burden estimates were well correlated with manual metrics (r value range: 0.83-0.93, P < .0001) and showed a similar, significant reduction in tumor growth in mice treated with anti-VEGF alone or in combination with chemotherapy. Given the fully automated nature of this technique, the proposed analysis method can provide a valuable tool in preclinical drug research for screening and randomizing animals into treatment groups and evaluating treatment efficacy in mouse models of lung cancer in a highly robust and efficient manner.

An automated image analysis method to measure regularity in biological patterns: a case study in a Drosophila neurodegenerative model.

The fruitfly compound eye has been broadly used as a model for neurodegenerative diseases. Classical quantitative techniques to estimate the degeneration level of an eye under certain experimental conditions rely either on time consuming histological techniques to measure retinal thickness, or pseudopupil visualization and manual counting. Alternatively, visual examination of the eye surface appearance gives only a qualitative approximation provided the observer is well-trained. Therefore, there is a need for a simplified and standardized analysis of fruitfly eye degeneration extent for both routine laboratory use and for automated high-throughput analysis. We have designed the freely available ImageJ plugin FLEYE, a novel and user-friendly method for quantitative unbiased evaluation of neurodegeneration levels based on the acquisition of fly eye surface pictures. The incorporation of automated image analysis tools and a classification algorithm sustained on a built-in statistical model allow the user to quickly analyze large sample size data with reliability and robustness. Pharmacological screenings or genetic studies using the Drosophila retina as a model system may benefit from our method, because it can be easily implemented in a fully automated environment. In addition, FLEYE can be trained to optimize the image detection capabilities, resulting in a versatile approach to evaluate the pattern regularity of other biological or non-biological samples and their experimental or pathological disruption.Electronic supplementary materialThe online version of this article (doi:10.1186/s13024-015-0005-z) contains supplementary material, which is available to authorized users.

High content screening in chemical biology: overview and main challenges

The last two decades have seen the development of high content screening (HCS) methodology and its adaptation for the evaluation of small molecules as drug candidates or their use as chemical tools for research purpose. HCS was initially set-up for the understanding of the mechanism of action of compounds by testing them on cell based-assays for pharmacological and toxicological studies. Since the last decade, the use of HCS has been extended to academic research laboratories and this technology has become the starting point for numerous projects aiming at the identification of molecular targets and cellular pathways for a given disease on which novel type of drugs could act. This screening approach relies on image capture of fluorescently labeled cells therefore generating a large amount of data that must be handled by appropriate automated image analysis methods and storage instrumentation. These latter in addition to the integration and data sharing are current challenges that HCS must still tackle.

Automated Assessment of Hemodynamics in the Conjunctival Microvasculature Network.

The conjunctival microcirculation is accessible for direct visualization and quantitative assessment of microvascular hemodynamic properties. Currently available methods to assess hemodynamics in the conjunctival microvasculature use manual or semi-automated algorithms, which can be inefficient for application to a large number of microvessels within the microvascular network. We present an automated image analysis method for measurements of diameter and blood velocity in microvessels. The method was applied to conjunctival microcirculation images acquired in 15 healthy human subjects. Frangi filtering, thresholding, and morphological closing were applied to automatically segment microvessels, while variance filtering was used to detect blood flow. Diameter and blood velocity were measured in arterioles and venules within the conjunctival microvascular network, and blood flow and wall shear rate were calculated. Repeatability and validity of hemodynamic measurements were established. The automated image analysis method allows reliable, rapid and quantitative assessment of hemodynamics in the conjunctival microvascular network and can be potentially applied to microcirculation images of other tissues.

Development of an automatic image analysis method for recognizing sea ice conditions on the Arctic Ocean

Development of an automatic image analysis method for recognizing sea ice conditions on the Arctic Ocean

Imaging Pluripotency: Time-Lapse Analysis of Mouse Embryonic Stem Cells.

The current view of the pluripotent state is that of a transient, dynamic state, maintained by the balance between opposing cues. Understanding how this dynamic state is established in pluripotent cells and how it relates to gene expression is essential to obtain a more detailed description of the pluripotent state.In this chapter, we describe how to study the dynamic expression of a core pluripotency gene regulator-Nanog-by exploiting single-cell time-lapse imaging of a reporter mESC line grown in different cell culture media. We further describe an automated image analysis method and discuss how to extract information from the generated quantitative time-course data.

Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images.

Quantifying variability in protein expression is a major goal of systems biology and cell-to-cell variability in subcellular localization pattern has not been systematically quantified. We define a local measure to quantify cell-to-cell variability in high-throughput microscope images and show that it allows comparable measures of variability for proteins with diverse subcellular localizations. We systematically estimate cell-to-cell variability in the yeast GFP collection and identify examples of proteins that show cell-to-cell variability in their subcellular localization. Automated image analysis methods can be used to quantify cell-to-cell variability in microscope images.

Development and optimization of a high-throughput micro-computed tomography imaging method incorporating a novel analysis technique to evaluate bone mineral density of arthritic joints in a rodent model of collagen induced arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease resulting in joint inflammation, pain, and eventual bone loss. Bone loss and remodeling caused by symmetric polyarthritis, the hallmark of RA, is readily detectable by bone mineral density (BMD) measurement using micro-CT. Abnormalities in these measurements over time reflect the underlying pathophysiology of the bone. To evaluate the efficacy of anti-rheumatic agents in animal models of arthritis, we developed a high throughput knee and ankle joint imaging assay to measure BMD as a translational biomarker. A bone sample holder was custom designed for micro-CT scanning, which significantly increased assay throughput. Batch processing 3-dimensional image reconstruction, followed by automated image cropping, significantly reduced image processing time. In addition, we developed a novel, automated image analysis method to measure BMD and bone volume of knee and ankle joints. These improvements significantly increased the throughput of ex vivo bone sample analysis, reducing data turnaround from 5days to 24hours for a study with 200 rat hind limbs. Taken together, our data demonstrate that BMD, as quantified by micro-CT, is a robust efficacy biomarker with a high degree of sensitivity. Our innovative approach toward evaluation of BMD using optimized image acquisition and novel image processing techniques in preclinical models of RA enables high throughput assessment of anti-rheumatic agents offering a powerful tool for drug discovery.

Characterization of the microstructure of dairy systems using automated image analysis

A sound understanding of the microstructure of dairy products is of great importance in order to predict and control their properties and final quality. The aim of this study was to develop an automated image analysis procedure to characterize the microstructure of different dairy systems. A high pressure freezing coupled with freeze-substitution (HPF-FS) protocol was applied prior to transmission electron microscopy (TEM) in order to minimize any modification of the microstructure of the dairy systems investigated. The developed image analysis procedure was first validated on synthetic images of suspensions, and then on two types of concentrated milk suspensions. Microstructural data relating to casein micelles in milk suspensions were taken from the literature. The established procedure was then applied to the two corresponding rennet-induced milk gels, prepared from the same milk concentrates used for suspensions preparation. The automated image analysis procedure allowed the reliable estimation of several characteristic microstructural parameters including area fraction, porosity, boundary length per unit area, particle aggregate size, inter-particle aggregate distance and tortuosity. The relative ease of estimating these microstructural parameters from the automated image analysis method could make it useful for routine measurements of milk gels. Moreover, the method enabled a useful discrimination between two different types of milk gels. This novel approach can contribute to a better understanding of the effects of processing on the structure-property relationships in dairy products, and may be applied to other food systems.

Extracting salient brain patterns for imaging-based classification of neurodegenerative diseases.

Neurodegenerative diseases comprise a wide variety of mental symptoms whose evolution is not directly related to the visual analysis made by radiologists, who can hardly quantify systematic differences. Moreover, automatic brain morphometric analyses, that do perform this quantification, contribute very little to the comprehension of the disease, i.e., many of these methods classify but they do not produce useful anatomo-functional correlations. This paper presents a new fully automatic image analysis method that reveals discriminative brain patterns associated to the presence of neurodegenerative diseases, mining systematic differences and therefore grading objectively any neurological disorder. This is accomplished by a fusion strategy that mixes together bottom-up and top-down information flows. Bottom-up information comes from a multiscale analysis of different image features, while the top-down stage includes learning and fusion strategies formulated as a max-margin multiple-kernel optimization problem. The capacity of finding discriminative anatomic patterns was evaluated using the Alzheimer's disease (AD) as the use case. The classification performance was assessed under different configurations of the proposed approach in two public brain magnetic resonance datasets (OASIS-MIRIAD) with patients diagnosed with AD, showing an improvement varying from 6.2% to 13% in the equal error rate measure, with respect to what has been reported by the feature-based morphometry strategy. In terms of the anatomical analysis, discriminant regions found by the proposed approach highly correlates to what has been reported in clinical studies of AD.