Photographic Reproduction and Enhancement Using HVS-Based Modified Histogram Equalization

Improved U-net-based leukocyte segmentation method

Significance

Leukocytes are mainly composed of neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The number and proportion of different types of leukocytes correspond to different diseases, so an accurate segmentation of each type of leukocyte is important for the diagnosis of disease. However, the acquisition of blood cell images can be affected by external environmental factors, which can lead to variable light and darkness, complex backgrounds, and poorly characterized leukocytes.

Aim

To address the problem of complex blood cell images collected under different environments and the lack of obvious leukocyte features, a leukocyte segmentation method based on improved U-net is proposed.

Approach

First, adaptive histogram equalization-retinex correction was introduced for data enhancement to make the leukocyte features in the blood cell images clearer. Then, to address the problem of similarity between different types of leukocytes, convolutional block attention module is added to the four skip connections of U-net to focus the features from spatial and channel aspects, so that the network can quickly locate the high-value information of features in different channels and spaces. It avoids the problem of large amount of repeated computation of low-value information, prevents overfitting, and improves the training efficiency and generalization ability of the network. Finally, to solve the problem of class imbalance in blood cell images and to better segment the cytoplasm of leukocytes, a loss function combining focal loss and Dice loss is proposed.

Results

We use the BCISC public dataset to verify the effectiveness of the proposed method. The segmentation of multiple leukocytes using the method of this paper can achieve 99.53% accuracy and 91.89% mIoU.

Conclusions

The experimental results show that the method achieves good segmentation results for lymphocytes, basophils, neutrophils, eosinophils, and monocytes.

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

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%.