Simple Digital Image Analysis Research Articles

A Digital Image Analysis Technique for Improved Strain Measurement in Geosynthetic Tensile Testing

Abstract Tensile testing of geosynthetics for measurement of strength and stiffness plays a critical role in the selection and specification of these materials for civil engineering applications. Measurement of the tensile properties of geotextiles is commonly performed via the wide-width strip method, where wide-width specimens are gradually strained to failure while being held in tight-gripping jaws or wrapped roller grips. During testing, material strains are inferred from the relative displacement of the grips. This approach does not properly account for slippage of the material in the gripping jaws or around the roller grip windings. Moreover, it inherently assumes a uniform distribution of strain across the tested specimen, which is often not the case at failure, as can readily be observed from visual examination of tested specimens. To address these issues, this paper utilizes a simple digital image analysis technique, implemented using open source software tools, to improve strain measurements in geosynthetic tensile testing. The presented technique is simple to use, low-cost, and can be deployed easily using readily available technology, making this technique useful for geotechnical engineering practitioners and laboratory testing managers in a production environment. Results from wide-width geotextile tensile tests on a polypropylene woven fabric are presented and analyzed using this technique; tests conducted using both tight-gripping jaws and wrapped roller grips show significant slippage, stress concentrations, and nonuniform strains. The technique that was utilized allowed for improved strain characterization across the entirety of a given tested specimen, which in turn yielded enhanced interpretation of geotextile test results.

A simple digital image analysis system for automated Ki67 assessment in primary breast cancer.

Ki67 is a well-established immunohistochemical marker associated with cell proliferation that has prognostic and predictive value in breast cancer. Quantitative evaluation of Ki67 is traditionally performed by assessing stained tissue slides with light microscopy. Automated image analysis systems have become available and, if validated, could provide greater standardisation and improved precision of Ki67 scoring. Here, we aimed to evaluate the use of the Cognition Master Professional Suite (CogM) image analysis software, which is a simple system for scoring Ki67 in primary breast cancer samples. Sections from 94 core-cut biopsies, 20 excision specimens and 29 pairs of core-cut biopsies and excision specimens were stained for Ki67 with MIB1 antibody and the Dako EnVision FLEX Detection System. Stained slides were scanned to convert them to digital data. Computer-based Ki67 scoring was performed with CogM. Manual Ki67 scoring assessment was conducted on previously stained sections from the same biopsies with a clinically validated system that had been calibrated against the risk of recurrence. A high correlation between manual and digital scores was observed [rCores =0.92, 95% confidence interval (CI) 0.87-0.94, P<0.0001; rExcisions =0.95, 95% CI 0.86-0.98, P<0.0001] and there was no significant bias between them (P=0.45). There was also a high correlation of Ki67 scores between paired core-cut biopsies and excision specimens when CogM was used (r=0.78, 95% CI 0.59-0.89, P<0.0001). CogM image analysis allows for standardised automated Ki67 scoring that accurately replicates previously clinically validated and calibrated manual scores.

Assessment of bisphosphonate treated-osteoblast behaviors by conventional assays and a simple digital image analysis

The main objective of this study was to analyze the changes in osteoblast behaviors by two different methods, a simple digital image analysis method and conventional assays. Osteoblast progenitor cells and osteoblasts were treated with alendronate (ALN; a nitrogen-containing bisphosphonate). Osteoblast behaviors such as the uptake of ALN, cell proliferation, cell differentiation, and mineralized nodule formation were examined. Quantitative assessments were conducted using a publically available ImageJ software along with conventional methods. Furthermore, ImageJ method and conventional assay for mineralized nodule formation were performed simultaneously and were compared in order to demonstrate the reliability of ImageJ analysis. Osteoblast precursors and osteoblasts responded to ALN treatments. The software could identify various colors and allowed for the quantification of staining intensity and area coverage. Both image analysis and conventional assays detected the changes in cell behaviors between treated and untreated samples. For alizarin red S assay, the staining intensity calculated by ImageJ analysis was comparable to the absorbance measured by conventional assay. These findings showed that digital image analysis along with conventional assays could be used for quantitative assessment to evaluate osteoblast alteration by drug treatment. Image analysis method is practical and might be useful for other applications in the field of biology and medical sciences. It could also be employed in a combination with the conventional assays to strengthen the data.

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach.

Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astrocytes. This could, in turn, lead to various neurological disorders such as Parkinson's or Huntington's disease as well as various dementias. Toxicity assessment is often done by pathologists after these exposures by qualitatively or semiquantitatively grading the severity of neurotoxicity in histopathology slides. Quantification of the extent of neurotoxicity supports qualitative histopathological analysis and provides a better understanding of the global extent of brain damage. Stereological techniques such as the utilization of an optical fractionator provide an unbiased quantification of the neuronal damage; however, the process is time-consuming. Advent of whole slide imaging (WSI) introduced digital image analysis which made quantification of neurotoxicity automated, faster and with reduced bias, making statistical comparisons possible. Although automated to a certain level, simple digital image analysis requires manual efforts of experts which is time-consuming and limits analysis of large datasets. Digital image analysis coupled with a deep learning artificial intelligence model provides a good alternative solution to time-consuming stereological and simple digital analysis. Deep learning models could be trained to identify damaged or dead neurons in an automated fashion. This review has focused on and discusses studies demonstrating the role of deep learning in segmentation of brain regions, toxicity detection and quantification of degenerated neurons as well as the estimation of area/volume of degeneration.

Digital image analysis for measuring nanogap distance produced by adhesion lithography

A simple digital image analysis for measuring nanogap distance produced by adhesion lithography is proposed. Adhesion lithography produces metal electrodes with sub-15 nm undulated space and μm to mm scale width without using electron beam lithography. Although the process has been rapidly improved in recent years, there has been no generalized procedure to evaluate the nanogap distance. In this study, we propose a procedure to evaluate a nanogap electrode with large width/gap distance ratios (>1000). The procedure is to determine the average distance of nanogap space from the area and the perimeter of the space by the analysis of the grayscale image. This procedure excludes any arbitrariness of the estimation and gives quantitative comparison of nanogap electrodes produced by different processes.

Measuring colony development in honey bees by simple digital image analysis

(2011). Measuring colony development in honey bees by simple digital image analysis. Journal of Apicultural Research: Vol. 50, No. 2, pp. 170-172.

A noncontact measurement technique for the density and thermal expansion coefficient of solid and liquid materials

A noncontact measurement technique for the density and the thermal expansion coefficient of refractory materials in their molten as well as solid phases is presented. This technique is based on the video image processing of a levitated sample. Experiments were performed using the high-temperature electrostatic levitator (HTESL) at the Jet Propulsion Laboratory in which 2–3 mm diam samples can be levitated, melted, and radiatively cooled in vacuum. Due to the axisymmetric nature of the molten samples when levitated in the HTESL, a rather simple digital image analysis can be employed to accurately measure the volumetric change as a function of temperature. Density and the thermal expansion coefficient measurements were made on a pure nickel sample to test the accuracy of the technique in the temperature range of 1045–1565 °C. The result for the liquid phase density can be expressed by ρ=8.848+(6.730×10−4)×T (°C) g/cm3 within 0.8% accuracy, and the corresponding thermal expansion coefficient can be expressed by β=(9.419×10−5) −(7.165×10−9)×T (°C) K−1 within 0.2% accuracy.