A modified Tseng algorithm approach to restoring thoracic diseases' computerized tomography images.
Dilber Uzun OzsahinAbubakar AdamuMaryam Rabiu AliyuHuzaifa UmarPublished in: PloS one (2024)
It is well-known that the Tseng algorithm and its modifications have been successfully employed in approximating zeros of the sum of monotone operators. In this study, we restored various thoracic diseases' computerized tomography (CT) images, which were degraded with a known blur function and additive noise, using a modified Tseng algorithm. The test images used in the study depict calcification of the Aorta, Subcutaneous Emphysema, Tortuous Aorta, Pneumomediastinum, and Pneumoperitoneum. Additionally, we employed well-known image restoration tools to enhance image quality and compared the quality of restored images with the originals. Finally, the study demonstrates the potential to advance monotone inclusion problem-solving, particularly in the field of medical image recovery.
Keyphrases
- deep learning
- image quality
- convolutional neural network
- machine learning
- optical coherence tomography
- computed tomography
- aortic valve
- chronic obstructive pulmonary disease
- magnetic resonance imaging
- spinal cord injury
- pulmonary artery
- air pollution
- magnetic resonance
- climate change
- contrast enhanced
- idiopathic pulmonary fibrosis
- electronic health record
- human health
- neural network
- pet ct