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Image Analysis for In-situ Detection of Agglomeration for Needle-like Crystals

机译：针对针状晶体附聚的原位检测图像分析

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摘要

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.
机译：提出了一种用于原位检测用于监测结晶过程的颗粒聚集的原位检测，基于使用非侵入式成像系统。该方法包括图像预处理，特征分析，形状识别和重新分割。首先，预先处理原位捕获的图像以消除不均匀照明背景和粒子运动的影响。然后，基于选择针状晶体的基本图像特征，用针对不同粒子类型定义的灰度共同发生矩阵（GLCM）建立纹理计算算法。随后，给出形状识别算法以将主粒子与捕获的图像中的重叠粒子区分开。最后，构造重新分割算法以通过使用几何方法和和弦点距累积（CPDA）技术来分离来自重叠晶体的单个晶体，然后根据基于的重叠晶体识别伪凝聚物。纹理分析。 β形式L-谷氨酸的冷却结晶的实验结果良好证明了所提出的图像分析方法的有效性。

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