Particle size and shape significantly affect powder processing and their end-product quality in a variety of industries. Imaging methods can successfully characterize populations of needle-like particles. Prior to off-line imaging, adjusting the particle density can reduce particle overlaps but increase measurement/processing times. Discarding data of overlapping particles, as most image processing algorithms do, biases particle size and shape distributions. Building on previous efforts, we here provide an image processing technique that accurately separates and sizes overlapping needle-like particles. Our algorithm combines edge detection, layer-stripping watershed segmentation and length approximation. To test the algorithm, a large number of real particle projections were randomly overlapped with various overlap intensities. Approximately 92–72% of the particles were detected and the particles’ dimensions were characterized with an accuracy of 87–75%, with these ranges corresponding to low and high overlap intensities. Overall, the algorithm removes biases to considerably improve characterization accuracy of powders containing needle-like particles.

粒度和形状显着影响各种行业的粉末加工及其最终产品质量。成像方法可以成功地表征针状颗粒的种群。在离线成像之前，调整粒子密度可以减少粒子重叠，但会增加测量/处理时间。正如大多数图像处理算法所做的那样，丢弃重叠粒子的数据会使粒子大小和形状分布产生偏差。在以前的努力的基础上，我们在这里提供了一种图像处理技术，可以准确地分离和调整重叠的针状颗粒的大小。我们的算法结合了边缘检测、层剥离分水岭分割和长度近似。为了测试该算法，大量真实粒子投影以不同的重叠强度随机重叠。检测到大约 92-72% 的颗粒，颗粒尺寸的特征精度为 87-75%，这些范围对应于低和高重叠强度。总体而言，该算法消除了偏差，从而显着提高了含有针状颗粒的粉末的表征精度。