MedGA: A novel evolutionary method for image enhancement in medical imaging systems. (1st April 2019)

• Record Type: Journal Article
• Authors: Rundo, Leonardo; Tangherloni, Andrea; Nobile, Marco S.; Militello, Carmelo; Besozzi, Daniela; Mauri, Giancarlo; Cazzaniga, Paolo
• Is Part Of: Expert systems with applications. Volume 119(2019)
• Page Start: 387
• Page End: 399
• Publication Date: 2019-04-01
• Subjects: Medical imaging systems -- Image enhancement -- Genetic Algorithms -- Magnetic resonance imaging -- Bimodal image histogram -- Uterine fibroids; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.11.013
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel medical image enhancement method based on Genetic Algorithms is proposed. MedGA enhances images characterized by nearly bimodal gray level histograms. The fitness function strengthens the two underlying intensity distributions. MedGA considerably outperforms the classical image enhancement techniques. MedGA achieves excellent results in terms of signal and perceived image quality. Abstract: Medical imaging systems often require the application of image enhancement techniques to help physicians 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 underlying 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 processing 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 image 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 solution 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.