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

Lu, Zhi; Zhu, Fan C.; Gao, Xin; Chen, Bing C.; Gao, Zhen

doi:10.1016/j.chemolab.2018.04.009

Cited by 24 publications

(14 citation statements)

References 27 publications

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“…Finally, the threshold operation is performed in each crystal region to obtain clean particles. The specific algorithm is described in [28]. Finally, the final image segmentation result is obtained using the morphological opening operation and the region filling as shown in Fig.…”

Section: Advances In Computer Science Research Volume 88mentioning

confidence: 99%

“…Secondly divide the agglomerates into multiple single crystals in the image with agglomerates, and then monitor the morphology of each crystal, including size information, texture information and so on. The previous method [28] regards agglomerates as single crystals, which in turn produces false statistical information. Taking the size information as an example, the length and width of particles obtained by the two methods are measured by the best-fit rectangle method [43], respectively, as shown in Fig.…”

Section: Agglomeration Segmentationmentioning

confidence: 99%

“…13. It can be seen from the figure that the method of [28] regards agglomerate 1 as a crystal, which causes the increase of particle size and then produces erroneous information. The method proposed in this paper can identify agglomerates as multiple single crystals; more accurately count the size information of the crystal, and then monitor the crystal morphology.…”

Section: Agglomeration Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Crystal Morphology Monitoring based on In-situ Image Analysis of L-glutamic Acid Crystallization

Lu¹,

Zhang²,

Yong-ming³

et al. 2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

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In the process of chemical industry and biological pharmacy, the morphology of the crystal plays a vital role; different morphological characteristics such as shape and size have a direct impact on production. Therefore, this paper proposes a synthetic in-situ image analysis method for monitoring the morphology of crystals based on using an invasive imaging system. The proposed method includes image preprocessing, feature extraction, morphological identification and agglomeration re-segmentation. The in-situ image is first pre-processed to eliminate the effects of water droplets, particle shadows, and uneven illumination. Then, texture features are extracted by using Improved-Basic Gray Level Aura Matrix (I-BGLAM) for different types of particles, and shape features are extracted by using image descriptors. Afterwards, the extracted features are used to morphologically identify the different particles. At last, the salient corners of the particles are detected, and a segmentation algorithm that separates individual crystals from the agglomerates is constructed by clustering the same type of salient corners. The case study and experimental results of L-glutamic acid cooling crystallization show that the image analysis method can be effectively used for morphology analysis of in-situ crystals with good precision.

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Section: Advances In Computer Science Research Volume 88mentioning

confidence: 99%

Section: Agglomeration Segmentationmentioning

confidence: 99%

Section: Agglomeration Segmentationmentioning

confidence: 99%

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Crystal Morphology Monitoring based on In-situ Image Analysis of L-glutamic Acid Crystallization

Lu¹,

Zhang²,

Yong-ming³

et al. 2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

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show abstract

“…The optimal thresholds are gained by minimizing the cut between different pixel sets [24]. Lu et al proposed an effective approach for particle segmentation based on combing the background difference method and the graph cut based local threshold method [25]. Jimenez et al presented a specifically designed graph cut methodology that ensures spatial and directional consistency [26].…”

Section: Introductionmentioning

confidence: 99%

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Wang

et al. 2019

Mathematical Problems in Engineering

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In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

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“…To improve the performance of real-time image analysis, great efforts have been invested in the design of in situ cameras, e.g., flow cell camera device, − stereo vision imaging system, ,− endoscopy–stroboscope, − and particle vision and measurement system (PVM, Mettler Toledo Inc.). In addition, the development of mathematical algorithms, e.g., region-based segmentation (threshold, watershed), , edge detection, , and clustering segmentation have advanced to some extent. Several papers studied the model compound l-Glutamic acid (LGA) for crystal segmentation and classification, which realized the online imaging analysis and proved its feasibility of analyzing crystal evolution. − However, processing an image usually takes one to several seconds using traditional algorithms, and there is a big challenge in accuracy and transfer from a single image to conduct in situ analysis of a crystallization process.…”

mentioning

confidence: 99%

Image Analysis for In-line Measurement of Multidimensional Size, Shape, and Polymorphic Transformation of l-Glutamic Acid Using Deep Learning-Based Image Segmentation and Classification

Gao

Bao

et al. 2018

Crystal Growth & Design

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In situ tracking of the crystallization process through image segmentation has been developed and has encountered many challenges including improvement of in situ image quality, optimization of algorithms, and increased computation efficiency. In this study, a new method based on computer vision was proposed using the state-of-the-art deep learning technology to track crystal individuals. For the model compound l-glutamic acid, two polymorphic forms with different morphologies were segmented and classified during a seeded polymorphic transformation process. Information such as counts, size, surface area, crystal size distribution, and morphology of α- and β-form crystals was extracted for the individual crystals during the process. A comparative analysis was conducted with traditional process analytical technologies such as in situ Raman and focus beam reflection measurement. Results show a high accuracy of segmentation and classification technique and a reliable tracking of crystals evolution. The image processing speed of up to 10 frames per second makes the proposed approach suitable for in situ tracking and control of crystallization and particulate processes. Our work in this study attempts to bridge the gap between the advanced imaging analysis technology that is available today and the specific needs of solution crystallization, to track, count, and measure the individual crystals.

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

Cited by 24 publications

References 27 publications

Crystal Morphology Monitoring based on In-situ Image Analysis of L-glutamic Acid Crystallization

Crystal Morphology Monitoring based on In-situ Image Analysis of L-glutamic Acid Crystallization

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Image Analysis for In-line Measurement of Multidimensional Size, Shape, and Polymorphic Transformation of l-Glutamic Acid Using Deep Learning-Based Image Segmentation and Classification

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