Imaging-based particle sizing has become a popular technique in industries ranging from pharmaceuticals to materials science due to its ability to provide visual confirmation of particle morphology alongside size measurements. However, despite its advantages, this method comes with several inherent limitations that can significantly affect the accuracy, reliability, and applicability of the results. The system’s ability to resolve fine details is inherently restricted by optical physics — even high-resolution cameras and microscopes have a physical bound on the smallest feature they can distinguish, which means particles smaller than approximately one micrometer are often not reliably captured or measured. This limitation makes it unsuitable for analyzing sub-micron or ultrafine particulates.
Most imaging platforms capture only flat projections, losing depth data, leading to potential inaccuracies in size estimation. Spherical, elongated, and plate-like particles can be indistinguishable based on projection alone, yet their actual three-dimensional volumes differ substantially. Without additional assumptions or complementary techniques such as stereoscopy or focus stacking, these distortions can introduce systematic errors in size distribution analysis.
Achieving optimal dispersion remains a persistent hurdle — imaging requires particles to be dispersed in a way that prevents clumping, sedimentation, or overlapping. Creating a non-agglomerated, evenly distributed monolayer is rarely achievable, especially with sticky or irregularly shaped materials. Clumped particles are frequently misclassified as individual entities, while particles that are partially obscured or in shadow can be missed entirely. Such errors distort the resulting size distributions and compromise the validity of reported size distributions.
Image processing routines are prone to consistent biases — edge detection, thresholding, and segmentation routines rely on contrast and lighting conditions, which can vary due to changes in illumination, background noise, or particle transparency. Particles with low contrast against their background—such as transparent or translucent materials—are often undercounted or inaccurately sized, and manual correction is sometimes necessary, but this introduces subjectivity and inconsistency, particularly when large datasets are involved.
The small field of view undermines statistical reliability — imaging systems typically analyze only a small fraction of the total sample, making them vulnerable to sampling bias. If the sample is heterogeneous or if particles are unevenly distributed, the images captured may not reflect the true population. Rare particle types are easily missed in heterogeneous systems, where rare but significant particle types may be overlooked.
Finally, imaging-based sizing is generally slower compared to other methods like laser diffraction or dynamic light scattering, as the time required to capture, 動的画像解析 process, and analyze thousands of images can be prohibitive for high-throughput applications or real-time monitoring. Automation has reduced cycle times, they often sacrifice precision for throughput, creating a trade-off that limits their utility in quality-critical environments.
Although imaging provides unique morphological data that other techniques cannot match, its quantitative reliability is constrained by resolution caps, 2D projection errors, dispersion problems, detection inaccuracies, small sample representation, and low throughput. For robust particle characterization, it is often most effective when used in conjunction with other sizing techniques to cross-validate results and compensate for its inherent shortcomings.
