Human chest CT image enhancement based on basic information preservation and detail enhancement

ABSTRACT

Objective: Human chest computed tomography (CT) image analysis is a key measure for diagnosing human lung diseases. However, the current scanned chest CT images might not meet the requirement of diagnosing lung diseases accurately. Medical image enhancement is an effective technique to improve the image quality and has been used in many clinical applications, such as knee joint disease detection, breast lesion segmentation and corona virus disease 2019(COVID-19) detection. Developing new enhancement algorithms is essential to improve the quality of chest CT images. A simple yet effective chest CT image enhancement algorithm is presented based on basic information preservation and detail enhancement. Method: A good chest CT image enhancement algorithm should well improve the clarity of edges or speckles in the image, while preserving much original structural information. Our human chest CT image enhancement algorithm is developed as follows. First, this algorithm exploits the advanced guided filter to decompose the CT image into multiple layers, including a base layer and multiple different scales of detail layers. Next, an entropy-based weight strategy is adopted to fuse the detail layers, which could well strengthen the informative details and suppress the texture-less layers. Afterwards, the fused detail layer is further strengthened based on an enhancement coefficient. In the end, the enhanced detail layer and the original base layer are integrated to generate the targeted chest CT image. The proposed algorithm could well enhance the details of the chest CT image, as well as transfer much original basic structural information to the enhanced image. Moreover, with the help of our algorithm, the surgeons can inspect more clear medical images without impacting their perception of the pathology information. In order to verify the effectiveness of our proposed algorithm, we have constructed a chest CT image dataset, which is composed of 20 sets/3 209 chest CT images, and then evaluated our algorithm and five state-of-the-art image enhancement algorithms on this large-scale dataset. In addition, the experiments are performed in both qualitative and quantitative ways. Result: Two qualitative comparison cases demonstrate that our algorithm has mainly strengthened the useful details, while effectively suppressing the background information. As for the five comparison algorithms, histogram equalization(HE) and contrast limited adaptive histogram equalization(CLAHE) usually change the whole image intensities with large variation and degrade the image quality as compared to the original image. Alternative toggle operator(AO) could enhance the chest CT image with much better visual quality than HE and CLAHE, but it has excessively enhanced both image details and background noises. Low-light image enhancement(LIME) and robust retinex model(RRM) usually increase the intensities of the whole image and result in images of inappropriate contrast. The quantitative average standard deviation(STD), structural similarity metric(SSIM), peak signal to noise ratio(PSNR) values of our algorithm are significantly greater than those of the other five comparison algorithms (i.e., increased by 4.95, 0.16, 4.47, respectively) on our constructed chest CT image dataset. To be specific, greater average STD value of our algorithm indicates it has enhanced images with more clear details compared to the other five comparison algorithms. Larger average SSIM and PSNR values of our algorithm validate that it has preserved more basic structural information from the original image than the other five comparison algorithms. Meanwhile, the proposed algorithm only costs about 0.10 seconds to enhance a single CT image, which indicates the proposed algorithm has great potential to be efficiently applied in the real clinical scenarios. Overall, our algorithm achieves the best results amongst all the six image enhancement algorithms in terms of both visual quality and quantitative metrics. Conclusion: In this study, we have developed a simple yet effective hum n chest CT image enhancement algorithm, which can effectively enhance the textural details of chest CT images while preserving a large amount of original basic structural information. With the help of our enhanced human chest CT images, the surgeons could diagnose lung diseases more accurately. Moreover, the proposed algorithm owns good generalization ability, and is capable of well enhancing CT images scanned from other sites and even other modalities of images. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.