Application Of Digital Image Analysis Research Articles

Application of digital image analysis as a method of assessing the of carbonation process of cement binders – impact of distortion on the results of real and model samples

Nowadays, because of the use of active additives in cement, studies on carbonation progress are crucial to ensure the safety of structures. Carbonation depth measurement involves the use of a calliper to determine the front of the carbonation. The values determined in this way are averaged, and the corrosion progress is estimated based on these values. Due to the fact that the standard approach assumes the use of a discrete method for determining the carbonation depth, the measurement may be subject to error, and its execution, especially for many samples, is time-consuming. An alternative method for determining the depth of carbonation may be continuous measurement, which determines the actual depth of carbonation since the entire sample area is analysed. Therefore, digital image analysis is the more precise and convenient approach. Unfortunately, it is not an approach, covered by the standards, but may help to assess the correctness of the determination, the carbonation depth. The problem with the use of digital analysis in proposed method may be the quality of the image obtained. The main problem may be the sphericity of the image, related to the nature of the camera, but also the lack of perpendicularity of the optical system of the camera to the sample when fixing its image, related to incorrect positioning by the camera operator. The results show that the digital image analysis is more accurate than the traditional approach, and that the distortion caused by the tilt of the image does not affect the obtained values to a greater degree than the measurement accuracy of the traditional approach.

Analysis of application of digital image analysis in histopathology quality control

IntroductionA correct histopathological diagnosis is dependent on an array of technical variables. The quality and completeness of a histological section on a slide is extremely prudent for correct interpretation. However, this is mostly done manually and depends largely on the expertise of histotechnician. In this study, we analysed the application of digital image analysis for quality control of histological section as a proof-of-concept. Material and methodsImages of 1000 histological sections and their corresponding blocks were captured. Area of the section was measured from these digital images of tissue block (Digiblock) and slide (Digislide). The data was analysed to calculate DigislideQC score, dividing the area of tissue on the slide by the tissue area on the block and it was compared with the number of recuts done for incomplete section. ResultsDigislide QC score ranged from 0.1 to 0.99. It showed an area under curve (AUC) of 98.8%. A cut-off value of 0.65 had a sensitivity of 99.6% and a specificity of 96.7%. ConclusionDigiblock and Digislide images can provide information about quality of sections. DigislideQC score can correctly identify the slides which require recuts before it is sent for reporting and potentially reduce histopathologists’ slide screening effort and ultimately turnaround time. These can be incorporated in routine histopathology workflows and lab information systems. This simple technology can also improve future digital pathology and telepathology workflows.

Digital Image Analysis of Ki67 Heterogeneity Improves the Diagnosis and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms

Ki67 is a reliable grading and prognostic biomarker of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). The intratumor heterogeneity of Ki67, correlated with tumor progression, is a valuable factor that requires image analysis. The application of digital image analysis (DIA) enables new approaches for the assessment of Ki67 heterogeneity distribution. We investigated the diagnostic utility of Ki67 heterogeneity parameters in the classification and grading of GEP-NENs and explored their clinical values with regard to their prognostic relevance. The DIA algorithm was performed on whole-slide images of 102 resection samples with Ki67 staining. Good agreement was observed between the manual and DIA methods in the hotspot evaluation (R2 = 0.94, P < .01). Using the grid-based region of interest approach, score-based heat maps provided a distinctive overview of the intratumoral distribution of Ki67 between neuroendocrine carcinomas and neuroendocrine tumors. The computation of heterogeneity parameters related to DIA-determined Ki67 showed that the coefficient of variation and Morisita-Horn index were directly related to the classification and grading of GEP-NENs and provided insights into distinguishing high-grade neuroendocrine neoplasms (grade 3 neuroendocrine tumor vs neuroendocrine carcinoma, P < .01). Our study showed that a high Morisita-Horn index correlated with poor disease-free survival (multivariate analysis: hazard ratio, 56.69), which was found to be the only independent predictor of disease-free survival in patients with GEP-NEN. These spatial biomarkers have an impact on the classification and grading of tumors and highlight the prognostic associations of tumor heterogeneity. Digitization of Ki67 variations provides a direct and objective measurement of tumor heterogeneity and better predicts the biological behavior of GEP-NENs.

Application of Digital Image Analysis for Assessment of Starch Content and Distribution in Potatoes

This study presents the possibility of using digital image analysis for the assessment of the starch content and distribution in potatoes. Tubers of six cultivars that were stored for 3 months in contrasting conditions (4 °C vs. −15 °C) were used in the experiment. The starch distribution in the potato tubers was assessed on the basis of histograms of the pixel values along four lines in the tuber cross-sections. Next, the basic statistics were calculated and used for the analysis of variance. The applied method allowed more precise distinguishing between the studied potato cultivars than comparing the total starch content alone. The new method also clearly distinguished potatoes stored in a freezer from those kept in a cold store.

Application of digital image analysis for monitoring the behavior of factors that control the rock fragmentation in opencast bench blasting: A case study conducted over four opencast coal mines of the Talcher Coalfields, India

Drilling and blasting play a very important role in driving the economy of opencast mines, as various mining activities related to strata handling are dependent on the size of the rock mass created due to blasting. Thus the analysis of fragments created from rock explosion is essential in order to monitor its compatibility with the deployed mining machineries/HEMMs (such as shovel, dumper, dragline, etc.). As over fragmentation as well as under fragmentation both tend to increase the cost of mining, the generation of fragment size in the desired range is necessary. Several factors control the rock fragmentation in blasting, such as the burden, bench height/drilling depth, stemming column, powder factor and hole diameter. The assessment of rock fragmentation with respect to the aforementioned parameters helps to enhance the blast performance and, hence, this study intends to carry out digital image analysis for monitoring the mean fragment size and boulder percentage. A highly consistent result has been obtained using forty blasting datasets carried out in the four different opencast mines of the Talcher Coalfield (India), namely Balram OCP, Ananta OCP, Lakhanpur OCP, and Lajkura OCP.

Application of Digital Image Analysis to the Prediction of Chlorophyll Content in Astragalus Seeds

Chlorophyll fluorescence (CF) has been applied to measure the chlorophyll content of seeds, in order to determine seed maturity, but the high price of equipment limits its wider application. Astragalus seeds were used to explore the applicability of digital image analysis technology to the prediction of seed chlorophyll content and to supply a low cost and alternative method. Our research comprised scanning and extracting the characteristic features of Astragalus seeds, determining the chlorophyll content, and establishing a predictive model of chlorophyll content in Astragalus seeds based on characteristic features. The results showed that the R2 of the MLR prediction model established with multiple features was ≥0.947, and the R2 of the MLP model was ≥0.943. By sorting of two single features, the R and G values, the R2 reached 0.969 and 0.965, respectively. A germination result showed that the lower the chlorophyll content, the higher the quality of the seeds. Therefore, we draw a conclusion that digital image analysis technology can be used to predict effectively the chlorophyll content of Astragalus seeds, and provide a reference for the selection of mature and viable Astragalus seeds.

Application of digital image analysis for monitoring the behavior of factors that control the rock fragmentation in opencast bench blasting: A case study conducted over four opencast coal mines of the Talcher Coalfields, India

Drilling and blasting play a very important role in driving the economy of opencast mines, as various mining activities related to strata handling are dependent on the size of the rock mass created due to blasting. Thus the analysis of fragments created from rock explosion is essential in order to monitor its compatibility with the deployed mining machineries/HEMMs (such as shovel, dumper, dragline, etc.). As over fragmentation as well as under fragmentation both tend to increase the cost of mining, the generation of fragment size in the desired range is necessary. Several factors control the rock fragmentation in blasting, such as the burden, bench height/drilling depth, stemming column, powder factor and hole diameter. The assessment of rock fragmentation with respect to the aforementioned parameters helps to enhance the blast performance and, hence, this study intends to carry out digital image analysis for monitoring the mean fragment size and boulder percentage. A highly consistent result has been obtained using forty blasting datasets carried out in the four different opencast mines of the Talcher Coalfield (India), namely Balram OCP, Ananta OCP, Lakhanpur OCP, and Lajkura OCP.

Application of digital image analysis on histological images of a murine embryoid body model for monitoring endothelial differentiation

The in vitro 3D model established from murine pluripotential stem cells (i.e., embryoid bodies (EBs)) is a dynamic model for endothelial differentiation. The aim of the present study was to investigate whether digital image analysis (DIA) can be applied on histological sections of EBs in order to quantify endothelial differentiation over time. The EBs were established in suspension cultures for 21 days in three independent replicate experiments. At day 4, 6, 9, 14, 18, and 21, the EBs were fixed in formaldehyde, embedded in paraffin and immunohistochemically (IHC) stained for CD31. The IHC-stained slides were digitally scanned and analysed using the Visiopharm® Quantitative Digital Pathology software Oncotopix™. The EBs developed CD31+ vascular-like structures during their differentiation. The quantitative DIA of the EBs showed that the log10 values of the relative CD31+ areas increased from −0.574 ± 0.470 (mean ± SD) at day 4 to 0.093 ± 0.688 (mean ± SD) at day 21 (p < 0.001). The approach presented in this study is a fast, quantitative and reproducible alternative method for an otherwise time-consuming and observer-dependent histological investigation. The future perspectives for such a system would be implementation of a modified version of the method on different 3D cultures and IHC markers.

The XVth Banff Conference on Allograft Pathology the Banff Workshop Heart Report: Improving the diagnostic yield from endomyocardial biopsies and Quilty effect revisited.

The XVth Banff Conference on Allograft Pathology meeting was held on September 23-27, 2019, in Pittsburgh, Pennsylvania, USA. During this meeting, two main topics in cardiac transplant pathology were addressed: (a) Improvement of endomyocardial biopsy (EMB) accuracy for the diagnosis of rejection and other significant injury patterns, and (b) the orphaned lesion known as Quilty effect or nodular endocardial infiltrates. Molecular technologies have evolved in recent years, deciphering pathophysiology of cardiac rejection. Diagnostically, it is time to integrate the histopathology of EMBs and molecular data. The goal is to incorporate molecular pathology, performed on the same paraffin block as a companion test for histopathology, to yield more accurate and objective EMB interpretation. Application of digital image analysis from hematoxylin and eosin (H&E) stain to multiplex labeling is another means of extracting additional information from EMBs. New concepts have emerged exploring the multifaceted significance of myocardial injury, minimal rejection patterns supported by molecular profiles, and lesions of arteriolitis/vasculitis in the setting of T cell-mediated rejection (TCMR) and antibody-mediated rejection (AMR). The orphaned lesion known as Quilty effect or nodular endocardial infiltrates. A state-of-the-art session with historical aspects and current dilemmas was reviewed, and possible pathogenesis proposed, based on advances in immunology to explain conflicting data. The Quilty effect will be the subject of a multicenter project to explore whether it functions as a tertiary lymphoid organ.

Archaeo-ceramic 2.0: investigating ancient ceramics using modern technological approaches

Since the first attempts to the petrographic analysis on pottery, a multitude of archaeometric studies have been carried out to solve important issues on provenance, production technology, dating, functionality and conservation state of ancient ceramic materials. According to the scientists’ background, the period when the investigations were done, destructive versus non-destructive analyses (also in relation to the unicity of the studied objects), as well as the purpose of the research, numerous analytical methods, typically used in various disciplines such as geology, physics and chemistry, have been applied. This contribution focuses on the use of innovative approaches to solve archaeological issues on pottery provenance, production technology and post-depositional alteration, reporting some examples of new methods applied to perform mineralogical, petrographic and chemical analyses. The traditional microscopic characterisation of ceramic pastes, for instance, can now be easily coupled with quantitative analysis of abundance and size distribution of their textural features, through the application of digital image analysis. This method, applied to images acquired by polarised transmitted light microscope, scanning electron microscope in back-scattered mode and microchemical mapping, can quantitatively and quickly describe production recipes, contributing to the definition of the pottery production choices and changes over time. As for the mineralogical analysis, the possibility of simultaneously processing numerous X-ray diffraction patterns by statistical tools, such as cluster analysis on raw data, is another example of a modern way to analyse ancient pottery and interpreting, with an objective comparison method, possible differences or analogies in the production recipes and/or firing technology of groups of ceramics. Moreover, modern microdiffraction techniques, both using conventional (X-ray tubes) and advanced sources (synchrotron radiation), also coupled with micro-Raman spectroscopy, allow the determination of mineralogical composition of small-sized phases both in ceramic bodies and slips/glazes, supplying important information on the production technology and possible post-deposition processes. As for the provenance issue, especially on fine ware, constraining relationships between far away communities/societies can now be evaluated not only on the basis of conventional bulk chemistry but also by isotope analysis. These new methodological approaches give rise to the new concept of archaeometric analysis on ancient ceramic materials that can be defined as “archaeo-ceramic 2.0”.

Application of digital image analysis for monitoring the behavior of factors that control the rock fragmentation in opencast bench blasting: A case study conducted over four opencast coal mines of the Talcher Coalfields, India

Abstract Drilling and blasting play a very important role in driving the economy of opencast mines, as various mining activities related to strata handling are dependent on the size of the rock mass created due to blasting. Thus the analysis of fragments created from rock explosion is essential in order to monitor its compatibility with the deployed mining machineries/HEMMs (such as shovel, dumper, dragline, etc.). As over fragmentation as well as under fragmentation both tend to increase the cost of mining, the generation of fragment size in the desired range is necessary. Several factors control the rock fragmentation in blasting, such as the burden, bench height/drilling depth, stemming column, powder factor and hole diameter. The assessment of rock fragmentation with respect to the aforementioned parameters helps to enhance the blast performance and, hence, this study intends to carry out digital image analysis for monitoring the mean fragment size and boulder percentage. A highly consistent result has been obtained using forty blasting datasets carried out in the four different opencast mines of the Talcher Coalfield (India), namely Balram OCP, Ananta OCP, Lakhanpur OCP, and Lajkura OCP.

Application of microscopy techniques for a better understanding of biomass pretreatment

In this work, the application of digital image analysis to micrographs obtained by two microscopy techniques, confocal fluorescence microscopy and scanning electron microscopy, is proposed. Two pretreated biomasses (Pinus radiata and Eucalyptus globulus) are characterized. Image processing methodologies are employed to determine equivalent diameters distribution (using x100 SEM micrographs), fractal dimension and lacunarity (using x1000 SEM micrographs), and surface composition and colocalisation (using confocal fluorescence microscopy). Particle size distributions result in median equivalent diameters in the range of 7.9–15.4 μm that follow an exponential tendency, and are strongly affected by the severity of the autohydrolysis pretreatment and the biomass type. Biomass surface composition (in terms of holocellulose/lignin ratios) is not the same than the obtained with standard protocols (NREL), and let distinguish the lignin deposition in the surface, especially in severe autohydrolysis conditions with pine. Fractal dimension (in the range of 2.65–2.74) and lacunarity (in the range of 0.05-0.16) have been found to have a strong dependence of the solid digestibility (linear correlations of 0.93 and 0.70) for the two biomasses, independently of the pretreatment employed.

Comparison between digital image analysis and visual assessment of immunohistochemical HER2 expression in breast cancer

BackgroundAssessment of HER2 status is considered standard of care in the histopathologic workup of breast cancer and conveys prognostic and predictive information used to guide treatment decisions. The assessment is often carried out in a two-step approach where immunohistochemical expression of HER2 protein is first evaluated by conventional microscopy and equivocal cases are further analyzed by in-situ hybridization techniques to assess gene amplification status. In this study we compared conventional manual assessment of immunohistochemical HER2 expression with digital image analysis (DIA) and consensus manual assessment by a panel of three pathologists. MethodsFrom our archive we retrieved sections of 109 invasive breast carcinomas stained for HER2 with corresponding HER2 score from the original pathology report. The glass slides were assessed by three pathologists to reach a consensus score. Next, the slides were scanned into whole slide images and DIA was performed using Aperio Imagescope. The scoring results were then compared with gene amplification status evaluated by dual in-situ hybridization (DISH). ResultsComparing manual assessment with consensus assessment and DIA, good agreement was obtained with weighted kappa coefficients of 0.79 (manual vs. consensus) and 0.67 (manual vs. DIA). When compared with gene status assessment by DISH, agreement analysis yielded weighted kappa coefficients of 0.52 (manual vs. DISH), 0.58 (consensus vs. DISH) and 0.78 (DIA vs. DISH). There were no false negatives by any of the three methods and false positives ranging from 0.9 to 2.8%. The proportion of equivocal cases by each method was 44% (manual), 33.3% (consensus) and 14.7% (DIA). Application of DIA reduced the number of equivocal cases by 67% without increasing the proportion of false negatives. ConclusionWe conclude that DIA is an accurate method to reduce the number of HER2 equivocal cases without affecting the sensitivity of the HER2 assessment.

Applying Online Image Analysis to Simultaneously Evaluate the Removals of Suspended Solids and Color from Textile Wastewater in Chemical Flocculated Sedimentation

The removal of suspended solids (SS) and color is critically important for textile wastewater treatment processes. Typically, chemical coagulation and sedimentation have been used as pretreatment processes to remove SS and color from textile wastewater. The effective removal of SS depends significantly on the particle size distribution, density, and fractal dimension. In practice, a batch settling test is used in the laboratory to evaluate the performance of chemical coagulation for the removal of SS. In this paper, we present the application of digital image analysis (DIA) for on-line and simultaneous measurement of the variations of the characteristics of particles in textile wastewater. This technology was used during a batch settling test to measure the characteristics of particles, including the mean gray value (MGV) of the captured images, particle size (i.e., equivalent diameter (ED)), total area, total volume, the fractal dimension, and the mean red/green/blue (R/G/B) values of the captured images. The on-line DIA data were used as input to regression and artificial neural network (ANN) models that predicted the efficiencies of SS removal and color removal in real textile wastewater after chemical coagulation and sedimentation. The experimental results indicated that the ANN models predicted both the SS and color removal efficiencies precisely, with correlation coefficients (R2) of 0.93 to 0.96. Thus, digital image analysis and ANN models can be used to simultaneously evaluate the removal of SS and color from textile wastewater by chemical coagulation.

Digital image analysis-based strategies for quantitative monitoring of rock art sites

Approaching rock art as one of many elements of greater, complex natural systems allows for a better understanding of rock art panels as dynamic realities. This stands in contrast to the traditional concept in which rock art is framed as a static set of paintings and/or engravings over a rock surface. The nature of the rock surface, and even other elements related to the supporting rock, such as the network of microfissures or the very composition of the rock, have generally been of secondary interest. Information on the environmental conditions, even at the most basic level, has been absent, as has the nature (or even the mere presence) of the biological communities harboured by the supporting rock. This new, dynamic, concept of rock art, needs to be understood by means of empirical data, in which quantitative and non-contact methods are of particular use. This paper deals with the application of digital image analysis (Principal Component Analysis and digital classification algorithms) as the basis of several research strategies for identifying these elements, measuring their extent, and monitoring the effects of these elements over time. It also presents the advantage of freely available software, minimising the overall cost of the analyses. Some examples were performed in laboratory conditions, and others in subterranean environments such as Roman hypogean tombs and Altamira Cave.

Characterization of the Morphological State of a Culture of Filamentous Microorganisms Using Digital Image Analysis

The paper examines the potential of the application of digital image analysis for the monitoring of morphological changes in cultures of filamentous microorganisms used in pharmaceutical production processes. First, an overview of selected methods and techniques used in digital image analysis is given with respect to their possible application in this project. This section also includes the description of corresponding software tools used in the presented study. The potential of the proposed approach is then demonstrated using a set of microscopic images from two typical productive cultivations. Finally, the obtained results are discussed within the context of a potential future application of these techniques for advanced monitoring of the given productive bioprocess.

Application of Digital Image Analysis to Determine Pancreatic Islet Mass and Purity in Clinical Islet Isolation and Transplantation.

Pancreatic islet mass, represented by islet equivalent (IEQ), is the most important parameter in decision making for clinical islet transplantation. To obtain IEQ, the sample of islets is routinely counted under a microscope and discarded thereafter. Islet purity, another parameter in islet processing, is routinely assessed by estimation only. In this study, we validated our digital image analysis (DIA) system by using the software of Image Pro Plus and a custom-designed Excel template to assess islet mass and purity to better comply with current good manufacturing practice (cGMP) standards. Human islet samples (60 collected from a single isolation and 24 collected from 12 isolations) were captured as calibrated digital images for the permanent record. Seven trained technicians participated in determination of IEQ and purity by the manual counting method (manual image counting, Manual I) and DIA. IEQ count showed statistically significant correlations between the Manual I and DIA in all sample comparisons (r > 0.819 and p < 0.0001). A statistically significant difference in IEQ between Manual I and DIA was not found in all sample groups (p > 0.05). In terms of purity determination, statistically significant differences between assessment and DIA measurement were found in high-purity 100-µl samples (p < 0.005) and low-purity 100-µl samples (p < 0.001) of the single isolation. In addition, islet particle number (IPN) and the IEQ/IPN ratio did not differ statistically between Manual I and DIA. In conclusion, the DIA used in this study is a reliable technique to determine IEQ and purity. Islet sample preserved as a digital image and results produced by DIA can be permanently stored for verification, technical training, and information exchange among islet centers. Therefore, DIA complies better with cGMP requirements than the manual counting method. We propose DIA as a quality control tool to supplement the established standard manual method for islet counting and purity estimation.

Abstract P1-07-05: Evaluation of tissue processing factors affecting HER2 IHC staining intensity in breast cancer cell lines

Abstract Background: In breast cancer the HER2 status as established by IHC (e.g. HercepTest™) determines eligibility for HER2 targeted therapy. Pre-analytical procedures including tissue processing create variation in HER2 intensity seen in breast cancer tissues. Therefore, there is a need for a greater understanding of the effects of the individual tissue processing steps on HER2 staining intensity. Control cell lines (CCL) are commonly used as staining run controls in immunohistochemistry (IHC) in both pharmacodiagnostic, routine and in clinical research. As a surrogate for cancer tissue, these immortalized cancer cells can serve as a model system for investigating the effect of process variables on staining intensity. This study was designed to investigate if variations in cell/tissue processing (fixation, embedding and sectioning) affect the IHC staining intensity in a HER2 formalin- fixed, paraffin-embedded (FFPE) control cell lines. To introduce a more objective scoring we have evaluated the application of digital image analysis combined with design of experiment (DOE) statistics. Methods: The following tissue processing parameters were investigated: embedding medium (HistoGel™ or agarose), fixation time (18 or 48 hours in neutral buffered formalin), density of cells (25% or 50%) and thickness of cut sections (2 μm, 4 μm or 6 μm). Factors were set up in a 2k factorial experimental design; all slides were stained with Dako HercepTest™. All slides were scanned, analyzed and compared on two different image analysis systems: ACIS®III (Dako) and Scanscope CS (Aperio). The DOE statistical analysis revealed the contribution of individual factors as well as any additive effect of a combination of factors. Analysis was based on the level of intensity as well as histoscores. Results: In a 24 DOE experiment, several of the parameters described above were associated with decreased staining intensity: increased fixation time (p &amp;lt; 0.001), decreased section thickness (p &amp;lt; 0.001) and decreased cell density (p &amp;lt; 0.001). In a separate 23 experiment, it was shown that fixation media in combination with cell density was associated with changes in staining intensity (p values from 0.001 to 0.022). Three different algorithms and two different slide scanners were tested and results were consistent regardless of the image analysis system or the algorithm used. Conclusions: This study identified several tissue processing factors which impact IHC staining intensity in a HER2 expressing CCL. The discovery of these effects has advanced the understanding of processing methodology and the influence of pre-analytical factors on HER2 IHC staining intensity. The application of a DOE setup with quantification by image analysis can be a useful tool for the systematic evaluation of several variables compared with the standard “one factor at a time” design. A multifactor effect between fixation media and cell density was detected which would not have been possible in a “one factor at a time” design. Implementing the knowledge achieved from this study provides a methodology for standardizing tissue processing and thereby improving HER2 testing in breast cancer. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P1-07-05.

Application of digital image analysis in evaluating pathologic changes in rat model with acute pancreatitis

Objective To apply computer digital image analysis to evaluate pathologic changes in rat model with acute pancreatitis, and explore digitalization of the evaluating process. Methods The model was established by injection of 5% sodium taurocholate into the pancreatic duct of Sprague-Dawley rats. Rats were killed 3, 6, 12, 24, and 48 h after the surgery respectively. Paraffin-embedded sections were made and scanned by Virtual Microscopy system. Digital image analysis was applied to analyze necrosis, edema,hemorrhage and infiltration of inflammatory cells in pancreatic tissues. Results As compared with other groups, the pathologic changes of pancreatitis were significantly more severe in 48-h group [inflammatory cell number: (67.00 ±49.49)/200 × field;necrosis: (49.86±21.74)%;hemorrhage: (14 445. 60 ±6940. 35) μm2;edema: 9.58 ±0. 81;P<0. 05];Cohen's kappa statistics showed that digital image analysis had better inter-rater agreement than Schmidt' s evaluation (Kappa coefficient;0. 7vs0. 3). Conclusion Digital image analysis could improve the objectivity, accuracy, and inter-rater agreement in evaluating pathologic changes in rat model with acute pancreatitis. Key words: Acute pancreatitis; Pathology; Computer

Basic study on contour tracing method in softwood cell contour extraction

The application of digital image analysis to calculate the parameters of softwood cells and to understand their microstructure is of importance in the area of basic micro-wood research such as wood anatomy. In order to obtain cell parameters, the most important part is to separate the cells from their background in an image, which is accomplished by drawing cell contours. In this study, a contour tracing method based on digital image binarization is introduced to extract the contours of softwood cells. As well, a comparison with other traditional methods is included. The results show that using this contour tracing method, clear and complete contours can be obtained, which solves the problems of traditional methods where sharp corners and turning points in an image could not be addressed.