Portable chest radiographs (CXRs) are commonly used in the intensive care unit (ICU) to detect subtle pathological changes. However, exposure settings or patient and apparatus positioning deteriorate image quality in the ICU. Chest x-rays of patients in the ICU are often hazy and show low contrast and increased noise. To aid clinicians in detecting subtle pathological changes, we proposed a consistent processing and bone structure suppression method to decrease variations in image appearance and improve the diagnostic quality of images. We applied a region of interest-based look-up table to process original ICU CXRs such that they appeared consistent with each other and the standard CXRs. Then, an artificial neural network was trained by standard CXRs and the corresponding dual-energy bone images for the generation of a bone image. Once the neural network was trained, the real dual-energy image was no longer necessary, and the trained neural network was applied to the consistent processed ICU CXR to output the bone image. Finally, a gray level-based morphological method was applied to enhance the bone image by smoothing other structures on this image. This enhanced image was subtracted from the consistent, processed ICU CXR to produce a soft tissue image. This method was tested for 20 patients with a total of 87 CXRs. The findings indicated that our method suppressed bone structures on ICU CXRs and standard CXRs, simultaneously maintaining subtle pathological changes.