Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.chemolab.2011.04.014
Title: Optimization of image processing parameters for large sets of in-process video microscopy images acquired from batch crystallization processes: Integration of uniform design and simplex search
Authors: Zhou, Y.
Lakshminarayanan, S. 
Srinivasan, R. 
Keywords: Crystallization
Parameter optimization
Particle size distribution
Real-time image processing
Sequential Simplex Optimization
Uniform design
Issue Date: Jul-2011
Source: Zhou, Y., Lakshminarayanan, S., Srinivasan, R. (2011-07). Optimization of image processing parameters for large sets of in-process video microscopy images acquired from batch crystallization processes: Integration of uniform design and simplex search. Chemometrics and Intelligent Laboratory Systems 107 (2) : 290-302. ScholarBank@NUS Repository. https://doi.org/10.1016/j.chemolab.2011.04.014
Abstract: A narrow particle size distribution with desired particle shape usually characterizes the expected product quality for pharmaceutical crystallization processes. Real-time estimation of particle size and shape from in-process video images is emerging as a new process analytical technology (PAT) tool for crystallization process monitoring and control. Any image processing algorithm involves a number of user-defined parameters and, typically, optimal values for these parameters are manually selected. Manual selection of optimal image processing parameters may become complex, time-consuming and unfeasible when there are a large number of images and particularly if these images are of varying qualities, as could happen in batch crystallization processes. This paper combines two optimization approaches to systematically locate optimal sets of image processing parameters - one approach is a model-based optimization method in conjunction with uniform experimental design; another approach is the Sequential Simplex Optimization method. Our study shows that these two approaches or a combination of them can successfully locate the optimal sets of parameters and the image processing results obtained with these parameters are better than those obtained via manual tuning. Combination of these two approaches also helps to overcome the drawbacks of each individual method. Our work also demonstrates that the optimal sets of parameters obtained from one batch of process images can also be successfully applied to another batch of process images that are obtained from the same system. The in-process video microscopy (PVM) images that are acquired from Monosodium Glutamate (MSG) seeded cooling crystallization process are used to demonstrate the workability of the proposed approach. © 2011 Elsevier B.V.
Source Title: Chemometrics and Intelligent Laboratory Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/64349
ISSN: 01697439
DOI: 10.1016/j.chemolab.2011.04.014
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