Image threshold analysis

In comparison with traditional radiography testing ISONIC system allows to provide image analysis and printout with different sensitivity thresholds at the postprocessing stage... 

There are a number of stains to label the nucleus for the purpose of detection in high content analysis. We use DRAQ5 to label not only the nuclei but also the cytosol to a lesser extent. With thresholds properly adjusted, imaging software can detect the nuclei and cytosol in the same channel in two consecutive steps with just one staining. 

Quantitative analysis of these images is accomplished in the same manner as for any microscope image, depending on the detail of analysis desired. Automated image analysis software can be used to provide a variety of information. Crystal phase volume (solid fat content UNIT D3.i) can be obtained by counting dark pixels (according to some empirical threshold factor). 

Another type of classification is outlier selection or contamination identification. As an example, in Fig. 4.23(b), the butter is the desired material and bacteria the contamination. An arbitrary threshold for this image would be 0.02, in which all pixels >0.02 are considered suspect, and hopefully, because this is a food product, decontamination procedures are pursued. In these two examples of classification, only arbitrary thresholds have been defined and, as such, confidence in these classifications is lacking. This confidence can be achieved through statistical methods. Although this chapter is not the appropriate place for an involved discussion of application of statistics toward data analysis, we will give one example often used in chemometric classification. 

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