The Use of Skeletonization for the Shape Classification of Carbon-Black Aggregates

1993 ◽  
Vol 66 (4) ◽  
pp. 491-509 ◽  
Author(s):  
Charles R. Herd ◽  
Gerard C. McDonald ◽  
Randolph E. Smith ◽  
William M. Hess
Keyword(s):  
Image Analysis ◽  
Carbon Black ◽  
Automated Image Analysis ◽  
Shape Classification ◽  
Linear Type ◽  
Carbon Blacks ◽  
Aggregate Breakdown ◽  
Aggregate Shape ◽  
Type 3

Abstract A specialized image analysis erosion technique, termed skeletonization, has been used for the first quantitative and direct measurement of branching in carbon-black aggregates. Twenty different carbon-black grades were analyzed using transmission-electron-microscopy/automated-image analysis (TEM/AIA). The skeletonization data were used in a discrimination analysis program for detailed shape classification of carbon-black aggregates into four different shape categories that included spheroidal (Type 1), ellipsoidal (Type 2), linear (Type 3) and branched (Type 4). These data were used to examine differences in the aggregate shape distributions between and within grades. Skeletonization and conventional TEM/AIA analyses were also conducted to examine aggregate breakdown as a result of high-shear mixing in rubber (SBR) and cellulose acetate butyrate (CAB) paint chip compounds. It was found that the number of aggregate branches decreased by as much as 50% in rubber and 70% in CAB compounds, and the distributions became narrower. Aggregate breakdown increases in the direction of the larger particle size carbon blacks which contain more linear (Type 3) aggregates. In rubber, an N650 (131 DBPA) and N330 (102 DBPA) carbon blacks were found to be similar in overall aggregate shape properties. Therefore, the significantly higher vulcanizate modulus for N650 appears to be related to a higher level of carbon-black—polymer interaction, as opposed to high amounts of polymer occluded and immobilized within the aggregate structure.

scholarly journals Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin

2021 ◽  
Vol 17 (11) ◽  
pp. e1008946
Author(s):  
Niksa Praljak ◽  
Shamreen Iram ◽  
Utku Goreke ◽  
Gundeep Singh ◽  
Ailis Hill ◽  
...  
Keyword(s):  
Image Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Automated Image Analysis ◽  
Cell Counting ◽  
Data Set ◽  
Analysis Workflow ◽  
Sickle Red Blood Cells ◽  
Validation Set

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.

Characterization of Dispersions

1991 ◽  
Vol 64 (3) ◽  
pp. 386-449 ◽  
Author(s):  
W. M. Hess
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Carbon Black ◽  
Phase Distribution ◽  
Automated Image Analysis ◽  
Carbon Blacks ◽  
Transmission Electron ◽  
Vehicle Systems ◽  
Scanning Electron

Abstract The methods of pigment dispersion analysis have been reviewed in regard to their application to rubber, plastics, and other vehicle systems. The characteristics of dispersions have been divided into three categories: (1) agglomeration (2) microdispersion (networking) and (3) polymer-phase distribution. Stylus roughness measurements on cut surfaces offer the combination of simplicity and speed of operation with high accuracy and precision for measuring pigment agglomeration in elastomer systems of known composition. This method may also be applied to the surface of thin plastic extrudates. However, optical analyses of thin cryosections are preferred for most plastics or unknown rubber compounds containing high loadings of carbon black. X-radiography is generally preferable for the analysis of inorganic agglomeration in most polymeric vehicle systems. The scanning electron microscope is also applicable for this type of analysis and has the added capability of identifying unknown agglomerates by energy dispersive x-ray analysis. Automated image-analysis techniques may also be utilized in conjunction with microscopical methods for quantifying the agglomeration of most types of pigments. For carbon blacks, the most suitable materials for on-line image analyses with transmitted light are plastics, paints, and inks which contain low black loadings. Higher carbon-black loadings in rubber can be analyzed by incident light using metallographic polishing of sulfur-hardened specimens. The microdispersion of carbon blacks at the primary aggregate level can be measured by means of electrical conductivity. This method is not applicable to inorganic pigments, large-particle-size carbon blacks, or blacks at very high or low loadings. Pigment microdispersion in different vehicle systems may also be assessed by means of scanning electron microscopy of thick cross sections (plasma etched to enhance contrast) or by transmission electron microscopy of thin cryosections. The tendency for the finer pigments to form 3-dimensional network structures in elastomers may also be measured as a function of the augmentation of dynamic modulus from high to low strain amplitudes. Pigment phase distribution in elastomer blends may be studied by scanning electron microscopy or transmission electron microscopy of thin cryosections, in conjunction with a staining or etching procedure to produce contrast between the separate polymer components. Selective staining is applicable to blends of polymers which differ significantly in their relative levels of unsaturation (e.g., NR/CIIR). Pyrolytic etching (under vacuum) may be used to produce interzone contrast in blends of polymers which differ significantly in their resistance to thermal degradation (e.g., NR/BR, NR/SBR). Pyrolysis GC may be utilized to determine the amount of carbon black in the separate phases of certain elastomer blends. This method is based on the relative intensity of the primary GC peaks for the individual polymers. The chromatographs are obtained from the bound rubber (carbon-polymer gel) that is developed during the mixing of the compound.

Application of Image Analysis Method Combined with Microhardness Measurement to Determine Phase Fractions

2021 ◽  
Vol 1016 ◽  
pp. 1153-1158
Author(s):  
Aarne Pohjonen ◽  
Sami Koskenniska ◽  
Juha Uusitalo ◽  
Tun Tun Nyo ◽  
Jari Larkiola ◽  
...  
Keyword(s):  
Image Analysis ◽  
Isothermal Holding ◽  
Microhardness Measurement ◽  
Automated Image Analysis ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Microscope Image ◽  
Different Temperatures ◽  
Microscopy Images

We have determined different phase fractions from microscopy images using semi-automated image analysis fitting technique, and in addition we have classified each phase according to its hardness. The distribution of grayscale pixels of different phases is first characterised separately for each phase, which are sampled from the microscope image. After this the distributions of the separate phases are fitted to give the corresponding distribution of the whole image. The microhardness measurement provides reliability on the classification of the different phases to ferrite, bainite or martensite. In addition to describing the applied techniques in detail, we present the results obtained from the analysis for one steel subjected to isothermal holding experiments at different temperatures.

scholarly journals A reproducible number-based sizing method for pigment-grade titanium dioxide

2014 ◽  
Vol 5 ◽  
pp. 1815-1822 ◽  
Author(s):  
Ralf Theissmann ◽  
Manfred Kluwig ◽  
Thomas Koch
Keyword(s):  
Image Analysis ◽  
Titanium Dioxide ◽  
Model Systems ◽  
Automated Image Analysis ◽  
Particulate Materials ◽  
Characterization Methods ◽  
International Regulations ◽  
Embedded Particles ◽  
Reliable Classification

A strong demand for reliable characterization methods of particulate materials is triggered by the prospect of forthcoming national and international regulations concerning the classification of nanomaterials. Scientific efforts towards standardized number-based sizing methods have so far been concentrated on model systems, such as spherical gold or silica nanoparticles. However, for industrial particulate materials, which are typically targets of regulatory efforts, characterisation is in most cases complicated by irregular particle shapes, broad size distributions and a strong tendency to agglomeration. Reliable sizing methods that overcome these obstacles, and are practical for industrial use, are still lacking. By using the example of titanium dioxide, this paper shows that both necessities are well met by the sophisticated counting algorithm presented here, which is based on the imaging of polished sections of embedded particles and subsequent automated image analysis. The data presented demonstrate that the typical difficulties of sizing processes are overcome by the proposed method of sample preparation and image analysis. In other words, a robust, reproducible and statistically reliable method is presented, which leads to a number-based size distribution of pigment-grade titanium dioxide, for example, and therefore allows reliable classification of this material according to forthcoming regulations.

scholarly journals Significant Geometry Features in Tongue Image Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bob Zhang ◽  
Han Zhang
Keyword(s):  
Image Analysis ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Decision Tree ◽  
Shape Classification ◽  
Large Dataset ◽  
Medical Practices ◽  
Human Tongue ◽  
Average Accuracy

The shape of a human tongue and its relation to a patients’ state, either healthy or diseased (and if diseased which disease), is quantitatively analyzed using geometry features by means of computerized methods in this paper. Thirteen geometry features based on measurements, distances, areas, and their ratios are extracted from tongue images captured by a specially designed device with color correction. Using the features, 5 tongue shapes (rectangle, acute and obtuse triangles, square, and circle) are defined based on traditional Chinese medicine (TCM). Classification of the shapes is subsequently carried out with a decision tree. A large dataset consisting of 672 images comprising of 130 healthy and 542 disease examples (labeled according to Western medical practices) are tested. Experimental results show that the extracted geometry features are effective at tongue shape classification (coarse level). Even if more than one disease class belongs to the same shape, the disease classes can still be discriminated via fine level classification using a combination of the geometry features, with an average accuracy of 76.24% for all shapes.

scholarly journals SBR and BR Tread Performance as a Function of Carbon Black Properties

1968 ◽  
Vol 41 (5) ◽  
pp. 1271-1284 ◽  
Author(s):  
E. Micek ◽  
F. Lyon ◽  
W. M. Hess
Keyword(s):  
Electron Microscope ◽  
Carbon Black ◽  
Iodine Number ◽  
Surface Area ◽  
Oil Absorption ◽  
Carbon Blacks ◽  
Grade Classification ◽  
Good Classification

Abstract All commercial tread grade carbon blacks, may be classified on the basis of electron microscope surface area and oil absorption. Similar industry wide grade classification on the basis of iodine number, tinting strength and oil absorption is considerably less reliable. However, among the blacks of individual carbon black suppliers, reasonably good classification of the tread grades is possible on the basis of these latter three carbon properties.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

Progress In The Practical Application Of Automated Image Analysis To Tem Negatives

1977 ◽  
Vol 35 ◽  
pp. 214-217
Author(s):  
F. A. Heckman ◽  
E. Redman ◽  
J.E. Connolly
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Exposure Time ◽  
Image Contrast ◽  
Automated Image Analysis ◽  
Practical Application ◽  
Factors Affecting ◽  
High Image Quality ◽  
Qualitative Evidence ◽  
Comprehensive Study

In our initial publication on this subject1) we reported results demonstrating that contrast is the most important factor in producing the high image quality required for reliable image analysis. We also listed the factors which enhance contrast in order of the experimentally determined magnitude of their effect. The two most powerful factors affecting image contrast attainable with sheet film are beam intensity and KV. At that time we had only qualitative evidence for the ranking of enhancing factors. Later we carried out the densitometric measurements which led to the results outlined below.Meaningful evaluations of the cause-effect relationships among the considerable number of variables in preparing EM negatives depend on doing things in a systematic way, varying only one parameter at a time. Unless otherwise noted, we adhered to the following procedure evolved during our comprehensive study:Philips EM-300; 30μ objective aperature; magnification 7000- 12000X, exposure time 1 second, anti-contamination device operating.

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