عنوان انگلیسی مقاله:
MedGA: A novel evolutionary method for image enhancement in medical imaging systems
ترجمه فارسی عنوان مقاله:
MedGA: یک روش جدید تکاملی برای تقویت تصویر در سیستم های تصویربرداری پزشکی
Sciencedirect - Elsevier - Expert Systems With Applications, 119 (2019) 387-399: doi:10:1016/j:eswa:2018:11:013
Leonardo Rundo a , b , 1 , Andrea Tangherloni a , 1 , Marco S. Nobile a , c , Carmelo Militello b , Daniela Besozzi a , Giancarlo Mauri a , c , Paolo Cazzaniga d ,
Medical imaging systems often require the application of image enhancement techniques to help physi- cians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underly- ing sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image pro- cessing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various im- age enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solu- tion for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements.
Keywords: Medical imaging systems | Image enhancement | Genetic Algorithms | Magnetic resonance imaging | Bimodal image histogram | Uterine fibroids