Characterisation of Extruded Products
Samples of extruded products have been imaged and processed using image segmentation
techniques developed within the Image Analysis Project of the CSIRO Mathematical and
Informaton Sciences. If product quality can be characterised by the visual appearance of
the product, we anticipate that image analysis techniques can be used to extract the
salient visual features. These features could then be analysed to provide a quantitative
and objective measure of product quality. We present here the imaging technique used to
measure pore size distribution in a sample of an extruded product.
To begin with, the sample is prepared for imaging. As shown in Fig. 1, at present we
simply obtain a thin slice of the sample by cutting it with a razor blade.
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(b) |
Figure 1: Sample Preparation. (a)
The sample is cut with a razor blade. (b) A thin slice of the sample is used for imaging.
The next step is to obtain an image of the slice of the sample. We have
constructed our own simple image-capturing set-up, consisting of lights, a black and white
CCD camera and a PC with a frame-grabber board. The lighting used is a circular light
which surrounds the sample, thus giving a diffuse light source. The resulting captured
image is shown in Fig. 2. In this image one can see that the sample consists of pores
surrounded by walls. The pores are usually grey, but as the sample is a thin slice they
often manifest as holes in the sample, letting the black background show through. The
walls that surround the pores are brighter and we use this information to identify the
pores in the sample.
Figure 2: An image of the sample.
We then process this image to find the features that we are interested in
(this process is called image segmentation ). At present, we have only targeted the
pores in the sample. In Fig. 3 is shown the resulting segmentation, where the white
objects in this binary image are considered as estimates of cross-sections of pores in the
sample. This segmentation was obtained by using morphological image processing operations
such as openings and closings.
Figure 3: Segmentation of the
pores in the sample, shown in white.
Analysis
Once the image is segmented, we are free to derive summary statistics from
the segmented features, which in our case are the cross-sections of pores. We can
illustrate the kind of summary statistics that can be produced using the example of pore
area. In Fig. 4 is an area-weighted histogram illustrating pore area information in the
sample. Note that the cross-sectional area is the area of the pore as it appears from
above and not the three-dimensional volume of the pore. Along the x-axis of the
histogram is the area of pores in square millimetres and along the y-axis is the area
fraction of pores in the batch which are of this area. One can see from this figure that
cross-sectional areas are mostly less than 1 square millimetre, although some are bigger
than 1.5 and the biggest is around 2.5 square millimetres. Pore area in square millimetres
can be determined from the pore area in pixels by simply taking an image of a ruler and
then deriving the number of pixels per millimetre in this image. Based on the results from
a commercial-in-confidence feasibility study, we found that such a histogram is a good
discriminator for the quality of the particular product that we analysed during this
study.
Figure 4: Area-weighted histogram of pore area.
Future research will involve an experiment to assess the correlation
between various sensory characteristics of the sample and the summary statistics derived
from the image analysis. We plan to use estimates of the width of the inter-pore walls in
this study, as well as the pores themselves |
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