In-situ particle segmentation approach based on average background modeling and graph-cut for the monitoring of L-glutamic acid crystallization

The crystal morphology often represents a critically important property for the reason that different morphological characteristics such as crystal shape and size distribution will directly affect crystal growth and the quality of final products. However, minor changes in reaction conditions can have a significant impact on crystal morphology. As a consequence, crystallization processes demand an on-line technique for real-time monitoring of crystal morphology. Image-based monitoring methods show a great potential for real-time monitoring, and performing particle segmentation accurately becomes a key issue in the particle image analysis. To avoid the influences of droplets and to eliminate the particle shadow of particle images obtained by invasive imaging system, an effective approach is proposed for particle segmentation based on combing the background difference method and the graph-cut based local threshold method. Through building the background model and performing subtraction between particle image and background model, droplets could be eliminated effectively. Then the local threshold method is performed further to eliminate the influences of particle shadow. Several particle images are used to demonstrate the efficiency and accuracy of the proposed method. In order to further evaluate the real-time performance of the proposed method, comparison experiments between the proposed method and other advanced or classic algorithms are presented. Experimental results show that the proposed method is efficient and has an impressive real-time performance.