Electronic portal image enhancement based on nonuniformity correction in wavelet domain

Abstract Purpose Electronic portal images are one of the most important tools to verify the ongoing radiotherapy treatment through comparison with a reference image generated during treatment planning. In this procedure, two images are geometrically matched by means of visible bone or other landmarks of interest such as implanted fiducials. However, the intrinsically poor contrast and low spatial resolution of portal images can limit image quality. Methods In this study, we have provided a multiresolution approach to enhance the quality of portal images acquired from the pelvis treatment fields. The main idea behind this work aims at removing some of the image artifacts that conceal the anatomical information. For this purpose, we have applied the homomorphic filtering on the approximation sub‐band of wavelet decomposition to enhance local information. Moreover, in order to sharpen the bone edges, wavelet detail sub‐bands were weighted to amplify important image details in the reconstruction of the desired enhanced image. The most appropriate image quality measure was chosen according to the image's characteristics in the spatial domain. By considering the characteristics of portal images as the random and nonperiodic texture, high level of noise, and a nonuniform background, three suitable quality measures of images were assessed: edge content, measure of enhancement, and measure of enhancement by entropy. Results The higher values of these measures indicate the quality improvement in the processed images through our proposed algorithm. Moreover, the subjective evaluation results indicate that the proposed multiresolution approach significantly enhances the perceived quality of images in comparison with original and the similar approach (). Conclusions Our proposed wavelet‐based enhancement algorithm successfully reduced image intensity nonuniformity and enhanced anatomical featured information, which drastically improved the objective metrics values. Subjective evaluation of enhanced image confirmed this quality improvement.