The quality issues commonly found in original medical images, such as insufficient contrast, blurred details, and noise interference, make it difficult for existing medical image segmentation techniques to achieve new breakthroughs. This study focuses on the enhancement of medical image data. Without significantly altering the appearance of the image, the quality problems of the original image are improved by adding specific pixel compensation and making subtle image adjustments, thereby enhancing the accuracy of image segmentation. Firstly, a new optimizer module is introduced, which generates a continuous distribution space as the target domain for transfer. This optimizer module takes the labels of the dataset as input and maps the discrete label data to the continuous distribution of medical images. Secondly, an EnGAN model based on generative adversarial networks (GAN) is proposed, and the transfer target domain generated by the optimizer module is used to guide the target generation of the adversarial network, thereby implanting the knowledge of improving medical image quality into the model to achieve image enhancement. Based on the COVID-19 dataset, convolutional neural networks, including U-Net, U-Net+ResNet34, U-Net+Attn Res U-Net, were utilized as the backbone network in the experiment, and the Dice coefficient and intersection over union reached 73.5% and 69.3%, 75.1% and 70.5%, and 75.2% and 70.3% respectively. The empirical results demonstrate that the medical image quality enhancement technology proposed in this study effectively improves the accuracy of segmentation while retaining the original features to the greatest extent, providing a more robust and efficient solution for subsequent medical image processing research.

[2024-01-02] Accepted Paper /doc/2023.08.0509/v1