School of Communication and Information Engineering, Xi'an University of Science and Technology, Shaanxi 710054, China.
Comput Math Methods Med. 2022 Jul 4;2022:9951582. doi: 10.1155/2022/9951582. eCollection 2022.
To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a peripheral blood image to clearly distinguish leukocytes from other components in the image. The adaptive retinex correction, which is based on multiscale retinex with colour replication (MSRCR), redefines the colour recovery function by introducing Michelson contrast. Then, the image is trained with the U-net convolutional neural network, and the leukocyte segmentation is completed. The innovation is in the manner of processing peripheral blood images, which improves the accuracy of leukocyte segmentation. This study conducts experimental evaluations on the Cellavision, BCCD, and LISC datasets. The experimental results show that the method in this study is better than the current best method, and the segmentation accuracy rate reaches 98.87%.
为了解决采集细胞图像中光照不均匀和白细胞特征不明显的问题，提出了一种基于自适应反射校正和 U-net 的白细胞分割方法。该方法首先对外周血图像进行处理，以清晰地区分白细胞与图像中的其他成分。基于多尺度反射与色彩复制的自适应反射校正（MSRCR）通过引入米歇尔逊对比度重新定义了色彩恢复函数。然后，使用 U-net 卷积神经网络对图像进行训练，完成白细胞分割。创新之处在于处理外周血图像的方式，提高了白细胞分割的准确性。本研究在 Cellavision、BCCD 和 LISC 数据集上进行了实验评估。实验结果表明，本研究方法优于目前的最佳方法，分割准确率达到 98.87%。
