Impact of Image Enhancement Using Contrast-Limited Adaptive Histogram Equalization (CLAHE), Anisotropic Diffusion, and Histogram Equalization on Spine X-Ray Segmentation with U-Net, Mask R-CNN, and Transfer Learning

Image segmentation is one of the important applications of deep learning models, such as U-Net and Mask R-CNN, in medical imaging. The image segmentation process enables automated extraction of important information within images, including spine X-rays, saving medical practitioners hours of work. However, for X-ray images, low contrast and noise may affect the quality of the images and consequently reduce the effectiveness of the deep learning models in providing a robust segmentation. Image enhancement prior to feeding the images to segmentation models can help to overcome the issues caused by the low-quality images. This paper aims to evaluate the effects of three image enhancement methods, namely, the contrast-limited adaptive histogram equalization (CLAHE), histogram equalization (HE), and anisotropic diffusion (AD), for improving image segmentation performance of Mask R-CNN, non-transfer learning Mask R-CNN, and U-Net. The findings show image enhancement methods provide significant improvement to the U-Net, and, interestingly, no noticeable improvement of performance on Mask R-CNN is observed. The application of HE for transfer learning Mask R-CNN achieved the highest Dice score of 0.942 ± 0.001 for binary segmentation. The randomly initialized Mask R-CNN obtains the highest DSC of 0.941 ± 0.002 on the same task. On the other hand, for U-Net, despite the presence of statistically significant change by applying image enhancement methods, the model achieves a maximum Dice score of 0.916 ± 0.003, lower than Mask R-CNN with and without transfer learning. A study on image enhancement methods and recent deep learning algorithms is necessary to better understand the effect of image enhancement techniques using deep learning.