Image analysis for in-situ detection of agglomeration for needle-like crystals

Kang Zou, Tao Liu, Yan Huo, Fangkun Zhang, Xiongwei Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

A synthetic image analysis method is proposed for in-situ detection of particle agglomeration for monitoring crystallization processes, based on using a non-invasive imaging system. The proposed method consists of image pre-processing, feature analysis, shape identification, and re-segmentation. Firstly, in-situ captured images are pre-processed to eliminate the influence from uneven illumination background and particle motion. Then, based on choosing the fundamental image features of needle-like crystals, a texture computation algorithm is established with a gray level co-occurrence matrix (GLCM) defined for different particle types. Subsequently, a shape identification algorithm is given to distinguish the primary particles from overlapped particles in a captured image. Finally, a re-segmentation algorithm is constructed to separate individual crystals from the overlapped crystals, by using a geometric approach and the chord-to-point distance accumulation (CPDA) technique, and then pseudo agglomerates are recognized from the overlapped crystals based on the texture analysis. Experimental results on the cooling crystallization of β form L-glutamic acid well demonstrate the effectiveness of the proposed image analysis method.
Original languageEnglish
Title of host publication2017 36th Chinese Control Conference (CCC)
PublisherIEEE
Pages11515-11520
Number of pages6
ISBN (Electronic)9789881563934
DOIs
Publication statusPublished - 11 Sep 2017

Publication series

NameChinese Control Conference
PublisherIEEE
ISSN (Print)1934-1768

Keywords

  • agglomeration
  • image analysis
  • re-segmentation
  • texture feature

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    Zou, K., Liu, T., Huo, Y., Zhang, F., & Ni, X. (2017). Image analysis for in-situ detection of agglomeration for needle-like crystals. In 2017 36th Chinese Control Conference (CCC) (pp. 11515-11520). (Chinese Control Conference). IEEE. https://doi.org/10.23919/ChiCC.2017.8029197