Characterizing Biologics Using Dynamic Imaging Particle Analysis - Overcoming limitations of volumetric techniques and detecting transparent particles. - BioPharm International

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Characterizing Biologics Using Dynamic Imaging Particle Analysis
Overcoming limitations of volumetric techniques and detecting transparent particles.


BioPharm International Supplements
Volume 24, Issue 8, pp. s4-s9


Figure 4: The effect of varying threshold on the binarization of a protein-aggregate image. Red pixels are particles, based on the threshold. The green boxes show enclosed particles found after thresholding. Image 1 is the original protein aggregate image. Image 2 shows the result of a threshold of 25 darker than background. Image 3 shows the result of a threshold of 15 darker. Image 4 shows the result of a threshold of 15 lighter. Image 5 shows the result of thresholding 15 lighter and darker. Image 6 is the same as Image 5, but with the addition of neighborhood analysis.
To avoid this problem, the imaging particle analysis system should allow for thresholding based on pixels that are either darker or lighter than the background. While this solution is certainly helpful, it still may allow the thresholding process to form some image artifacts. Fortunately, common image-processing algorithms are available to overcome this problem by using neighborhood analysis to group disparate clusters of thresholded pixels into logical whole images. Figure 4 shows the differences obtained for a thresholded image of a single protein aggregate image using a dark-only pixel threshold, a dark-and-light pixel threshold, and finally, a dark-and-light pixel threshold plus neighborhood analysis.


Figure 5: Thresholded images of National Institute of Standards and Technology traceable 10-m spheres. Sphere images in sharp focus are on the left, and less sharp images are at right. The table shows variance in measured diameter based on different threshold values.
The third factor to consider when using dynamic imaging particle analysis is the effect of image quality. Although the overall topic of image quality is quite broad and well beyond the scope of this article, a basic tenet of the subject is that image sharpness is the most critical measurement of image quality (6). Indeed, in imaging particle analysis, the sharpness of the particle images is directly proportional to the accuracy of the measurements obtained.


Table II: Equivalent spherical diameters (ESD).
Figure 5 demonstrates this by showing images and measurements obtained in an imaging particle analysis system for National Institute of Standards and Technology traceable size bead standards. The images at left are beads in sharp focus, and the images at right are beads in less sharp focus. The variation in size and shape caused when the bead images are less sharp is easy to see, and Table II shows the variation in size measurements that would be obtained by the system with different thresholds. For 10-m calibrated spheres, the variation in measurement for the blurry images is more than 12 m for a difference of 100 in threshold value, whereas the variation in measurement for the sharp images is only 1.67 m over the same threshold range. It is clear that image sharpness greatly affects the accuracy and precision of particle measurements.


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