Software for the Image Analysis of Cheese Microstructure from SEM Imagery

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1 Software for the Image Analysis of Cheese Microstructure from SEM Imagery Gaetano Impoco dmi.unict.it September 12, Contents This software is intended for the analysis of SEM 1 imagery of cheese microstructure. It might be also used in connection with different acquisition technologies or for different applications where porous materials are involved. However, being tailored to a specific application, you cannot be sure it will be of any use in your application. Applicability of this software should be evaluated for every specific application. This software is in the form of a plug-in for ImageJ [3]. ImageJ is a widely used Open Source software in scientific communities employing Image Analysis and is released under the GPL license [1]. Figure 1 shows a snapshot of ImageJ and the plug-in for the analysis of cheese SEM imagery. The plugin encompasses two commands: BinariseSEM ComputeStats. BinariseSEM segments 2 the input image into holes and structure i.e., in areas that are interpreted as pores or as protein matrix. It is useful to remark that: class. 1 Scanning Electron Microscope. 2 To partition an image into a number of classes, using a coherence criterion within each 1

2 Figure 1: A snapshot of the ImageJ software and the plug-in for Image Analysis. 2

3 Holes and structure that are not imaged (i.e., acquired by the microscope) cannot be processed by no Image Processing software, simply because they are not visible. Pores with diameter of one pixel o less are discarded since they are treated as acquisition noise. The definition of pore used here does not necessarily coincide with the informal definition nor with the expectations of the user. In particular, here a pore is defined as a set of contiguous pixels whose intensity values are coherent. The coherency function derives from the sequence of operations used to enhance the input image before thresholding 3 ) The command ComputeStats employs the output of the previous application BinariseSEM to collect image statistics about the distribution of pores, such as: number of pores, perimeter and area, shape descriptors, orientation, and so on. It is assumed that input images are achromatic (i.e., greylevel) and with a bitdepth of 8-16 bit/pixel. 2 Installation The installation procedure of ImageJ plug-ins is quite simple. Just extract the content of the downloaded compressed file into the directory [Image- JbaseDir]/ plugins/ where [ImageJbaseDir] is the ImageJ installation directory, and run ImageJ. 3 The Plug-in The SEM analysis plug-in has two commands that must be used in the correct sequence. ComputeStats takes a binary image as input, where it is assumed that white pixels belong to holes and black pixels represent structure. BinariseSEM outputs this kind of images, given a greylevel SEM image. 3 Image Processing operation used to segment the image using a threshold value. This term is mainly used for binary segmentation (i.e., with two classes only). 3

4 We chose to break the plug-in into two commands in order to allow easy substitution of BinariseSEM with another binarisation software giving the same output (for example, a simple global thresholding). A more detailed presentation of the two commands follows. For a description of the algorithms used the reader is referred to [2]. 3.1 BinariseSEM This command segments the input image into pores and structure, where a pore is defined as a patch of contiguous pixels with similar intensity values. A discontinuity in the pixel intensity is regarded as a discontinuity in the structure of the material. This might be not always true, due to noise and reflection effects caused by the acquisition device. As shown in Figure 3, after opening an image e selecting the BinariseSEM command in the menu, a dialog window is shown. Two options are given: Bandpass Filter e Fill Inner Holes. It is asked to the user to guide the segmentation mechanism since these two parameters are strongly dependent on the application and on the domain knowledge of the user, and their values cannot be automatically guessed for every application. In particular, the second option lets the user choose wether the holes must be filled or not after thresholding. Hence, it should be turned on only if the user knows that there cannot be any structure inside a hole. The first option is used to correct possible illumination gradient effects due to the acquisition system. When activated, a filter is used to correct for intensity gradients but, at the same time, the image quality is reduced so that the quality of the output could reduced as well. Hence, care should be taken when using this option. Before using the software for massive analysis, we recommend to run this command twice for each image using this two options alternately in order to get the feeling of the result. After selecting the desired options the command is run. A sequence of processing operations are executed to enhance the quality of the input image and to make it more amenable for thresholding [2]. When the processing ends a window is shown to help the user choosing the best threshold value for the application (Figure 3(a)). The plug-in automatically computes a threshold value that is suggested to the user. Ino most cases, this value gives a good thresholding. However, an automatic procedure cannot give optimal results for all applications and has no knowledge about the domain of application. Hence, the thresholding procedure must be assisted by the user. When the 4

5 (a) (b) Figure 2: Options of the BinariseSEM command. 5

6 (a) Thresholding of the processed image. (b) Binary output image. Figure 3: Thresholding. 6

7 threshold value has been chosen the output will appear as in Figure 3(b). 3.2 ComputeStats This command collects various statistics about the holes extracted using the BinariseSEM and shows them using histograms and rose plots. Figure 4 shows the options of this command. The most important is the magnification factor of the microscope, expressed in microns. The other options refer to the statistics to evaluate and show. Figure 5 reports an example of the output of this command. Histograms are shown reporting the distribution of several measures and of the value of some shape descriptors (see [2] for a more detailed description). All the distributions are summarised by histograms. Directionality can be also shown using an angle diagram (rose plot). One such diagram is shown on the bottom right of Figure 5. Notice that the statistics about directionality report a predominant orientation of the pores approximately around 45 with respect to the x-axis (see the rose plot and the histogram on its left). References [1] Free Software Foundation. GNU General Puplic License. Web site: http: // [2] G. Impoco, S. Carrato, M. Caccamo, L. Tuminello, and G. Licitra. Quantitative analysis of cheese microstructure using sem imagery. In SIMAI Minisymposium on Image Analysis Methods for Industrial Application, [3] Research Services Branch NIMH & NINDS. ImageJ - Image processing and analysis in Java. Web site: 7

8 (a) (b) Figure 4: Options of the command ComputeStats. 8

9 (a) (b) Figure 5: Statistics computed. 9