In clinical diagnosis, diagnostic images that are obtained from the scanning devices serve as preliminary evidence for further investigation in the process of delivering quality healthcare. However, often the medical image may contain fault artifacts, introduced due to noise, blur and faulty equipment. The reason for this may be the low-quality or older scanning devices, the test environment or technicians lack of training etc; however, the net result is that the process of fast and reliable diagnosis is hampered. Resolving these issues automatically can have a significant positive impact in a hospital clinical workflow, where often, there is no other way but to work with faulty/older equipment or inadequately qualified radiology technicians. In this paper, automated image quality improvement approaches for adapted and benchmarked for the task of medical image super-resolution. During experimental evaluation on standard open datasets, the observations showed that certain algorithms perform better and show significant improvement in the diagnostic quality of medical scans, thereby enabling better visualization for human diagnostic purposes.
翻译:在临床诊断中,通过扫描装置获得的诊断图像可作为在提供优质保健过程中进一步调查的初步证据,然而,医疗图像往往可能含有因噪音、模糊和错误设备而引入的故障文物,其原因可能是低质量或陈旧的扫描装置、测试环境或技术人员缺乏培训等;然而,最终结果是快速可靠的诊断过程受到阻碍,解决这些问题自动地对医院临床工作流程产生重大的积极影响,在医院临床工作流程中,往往没有其他方法,只能与有缺陷/老旧设备或不合格的放射技术员一起工作;在本文件中,为医疗图像超分辨率任务而采用和基准调整的自动图像质量改进方法;在对标准开放数据集进行实验性评估时,观察显示某些算法效果更好,并显示医疗扫描诊断质量的显著改善,从而能够更好地为人类诊断目的进行视觉化。