A METHOD FOR DIGITAL IMAGE ANALYSIS OF CERAMIC GRAINS BASED ON SHAPE FACTOR SEGMENTATION

Elson de Campos (1); Émerson F. de Lucena (1); Francisco C.L. de Melo (2); Luis Rogerio de O. Hein (1) 1Laboratório de Análise de Imagens de Materiais (LAIMat), Departamento de Materiais e Tecnologia (DMT), Faculdade de Engenharia do Campus de Guaratinguetá (FEG), Universidade Estadual Paulista (UNESP), 12516-410, Guaratinguetá, SP, Brazil, [email protected] 2 Divisão de Materiais (AMR), Instituto de Aeronáutica e Espaço (IAE), Centro Técnico Aeroespacial (CTA), São José dos Campos, SP, Brazil

ABSTRACT A very simple and robust method for ceramics grains quantitative image analysis is presented. Based on the use of optimal imaging conditions for reflective light microscopy of bulk samples, a digital image processing routine was developed for shading correction, noise suppressing and contours enhancement. Image analysis was done for grains selected according to their concavities, evaluated by the perimeter ratio shape factor, to avoid consider the effects of breakouts and ghost boundaries due to ceramographic preparation limitations. As an example, the method was applied for two ceramics, to compare grain size and morphology distributions. In this case, most of artifacts introduced by ceramographic preparation could be discarded due to the use of the perimeter ratio exclusion range.

Keywords: digital image analysis, perimeter ratio, ceramography, reflective light microscopy

1.Introduction: Ceramics have been used in many engineering applications, which have resulted in extensive research. Most morphological characterization of ceramic grains are performed by qualitative microstructural observations by scanning electron microscope (SEM) [1-5] or few quantitative information [6,7]. From literature, it is still rare [8-10] the use of digital image processing to obtain microstructure morphology distribution from light microscope (LM) imaging. This work presents one method for image analysis of ceramic grains morphology, bypassing many of the practical limitations due to thermal attack or ceramographic preparation for light microscopy.

2. Light imaging and digital image processing: To test the proposed method, two types of ceramics were prepared with compositions: Al2O3 (ALCOA A-16) – 0,15% de MgO e Al2O3 (ALCOA A-16) – 1,5% de CrO. The bulk samples were prepared by using standard ceramographic procedure [11]. A conventional [11] thermal etching, by heating at 1350°C during 20 minutes, was also applied to reveal grain boundaries. Enhancement of grains or pores boundaries are obtained by improving image contrast, e.g., ensuring the larger brightness differences between neighbor regions [12]. In order to obtain optima image sets, it was used a reflective light microscope with a 10X /0.30 NA/6.5 WD planachromatic objective, to evaluate imaging conditions of bulk samples, including both brightfield illumination and DIC (differential interference contrast) Nomarsky with and without green intensity filters. These intensity filters are used to obtain monochromatic light with a fixed wavelength into the optimal spectral sensitivity range of CCD detector of a digital camera. This means that contrast is a function of microstructural components and not from the differences in wavelengths. The larger range on gray- level distribution for the brightness histograms identifies the best contrast. The analysis of fig.1 indicates that the best imaging choice is a combination of DIC Nomarsky and green intensity filtering. Wavelength filtering improved the global contrast, by the larger histogram distribution, and the DIC Nomarsky improved the contrast at grain boundaries and a bimodal distribution for gray levels is observed that can make more uniform the histogram thresholding. The background removal (or shading correction) is necessary to allow the same glare or shading distribution at different picture regions [13]. In real systems, both the illumination intensity distribution and CCD sensor performance are non-uniform throughout the whole image field and, also, the topography of polished surfaces causes irregular shading. So the contrast will be a function of sample preparation and microscopy imaging plus the microstructural reflective properties. There are various methods for shading correction [13]. In this work, an extreme lowpass filter was carried out on the original image to find the shading background. Therefore, it was subtracted from the original image, pixel by pixel (fig. 2a-b). The linear histogram equalization (fig. 2c) was then applied to generate a uniform brightness distribution. The results are better when the original gray-level histogram occupies the largest range on brightness scale, that occurs when the imaging technique is optimized for contrast improvement. The image still can have some saturated points (pixels with brightness very different from neighbors regions) due to the noise from digital acquisition. A 3x3 median filter can reduce this effect (fig. 2c) [14]. After this step, the brightness histogram presents a bimodal distribution and the thresholding for image binarization is easily done by selecting the gray-level at the minimum valley between the two mode peaks (fig. 2d). The contours are now well defined, but some noise points and small false objects are still present inside grains and could distort the results for pore analysis. Again, the use of a 3x3 median filter can reduce or eliminate these features (fig. 2e).

3. Shape segmentation for image analysis:

The selected parameter for shape segmentation, the perimeter ratio (PR), is defined by: P PR = cv (Eq. 1) Pcc Where Pcc is the concave (or true) perimeter of the object, Pcv is the convex. Perimeter ratio was chosen because the compressive molding and sintering processes must not change the convex format of original particles, but it is sensible to identify the breakouts and grains connected by ghost boundaries. After binarization, the range values for shape segmentation must be previously defined and, in the present case, a semi-automatic procedure on sample images was adopted as follows: a) the PR for all grains, excluding those on image limits, was measured; b) PR values distributions for both false (breakouts and ghost boundaries limited) and true (well shaped) grains were determined by manual selection of the objects; c) both distributions were compared to define the PR range limits. So only the grains presenting PR values inside the range from 0.96 to 1.00 were considered for image analysis. Table 1 presents the distributions obtained for the following parameters: area, length perimeter and mean Feret diameter (as size measurements); fractal dimension, to describe grain boundaries roughness; and aspect ratio, defined as the ratio between maximum and minimum chord lengths inside an object, to evaluate the grain morphology [15]. Parameter statistics included the coefficient of variation [16], to compare the distributions obtained for MgO and CrO additions in Al2O3. It could be stated that the overall morphology was not significantly changed by these additions. From Fig. 2f, only the gray shaded grains were considered for image analysis. White grains were excluded due to present PR values lower than 0.96, indicating a large concavity in their boundaries, or due to stay on image limits, as presented on Fig.3.

a) Brightfield illumination without green filter.

b) Brightfield illumination with green filter.

c) DIC Nomarsky without green filter.

d) DIC Nomarsky with green filter. Fig. 1. Selection of light contrast technique. Sample images and respective gray scale histograms.

a) Original image. b)Shading correction

c)Equalization and median filtering d)Thresholding

e)Median filtering f)Segmentation by Perimeter Ratio Fig. 2. Sequence for digital image processing.

PARAMETERS Al2O3 - MgO Al2O3 - CrO mean 15.57 17.79 AREA standard deviation 26.90 30.82 coefficient of 1.73 1.73 variation mean 1.87 1.95 ASPECT RATIO standard deviation 0.69 0.78 coefficient of 0.37 0.40 variation mean 4.92 5.45 LENGTH standard deviation 3.41 3.97 coefficient of 0.70 0.73 variation mean 12.63 13.81 PERIMETER standard deviation 8.77 9.80 coefficient of 0.70 0.71 variation mean 1.06 1.06 FRACTAL standard deviation 0.02 0.02 DIMENSION coefficient of 0.02 0.02 variation mean 4.03 4.40 MEAN standard deviation 2.76 3.08 FERET coefficient of 0.68 0.70 DIAMETER variation Table 1. Morphology and size measurements.

ghost boundary

grains at boundaries were discarded

Fig. 3. Grains area distribution example. Grains in white were excluded from morphology and size measurements. Gray-levels indicate different grain area classes. Black objects are pores.

The main shape segmentation error occurred for grains presenting a mean fracture plane making a very low angle with the polished surface. For these cases, the smooth fracture relief produced a local reduction on grain brightness, as can be observed by comparing some grains at the center regions in Fig. 2b and Fig. 2f. This limitation can only be avoided by careful grinding and polishing procedures. The use of the proposed method combined to the right selection of ceramographic preparation techniques must to ensure the better result, since many ceramographic artifacts can be discarded due to the use of perimeter ratio exclusion range.

4. Final Comments:

Digital image processing and analysis is a powerful tool for quantitative ceramography. In this work, a very robust and simple digital image processing and analysis method was presented for grain size analysis of monophase systems. The proposed procedure is effective to avoid most of the defects produced by ceramographic preparation and thermal attack due to the use of perimeter ratio for shape segmentation. The perimeter ratio is a measurement of grain concavity, which is artificially changed by breakouts or ghost boundaries. Best results will be obtained for the association of this method with the right use of ceramographic preparation techniques.

5. Acknowledgments:

To FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo), by the financial support under grant number 1997/06287-5.

6. References:

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