دورية أكاديمية

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

التفاصيل البيبلوغرافية
العنوان: MedGA: A novel evolutionary method for image enhancement in medical imaging systems
المؤلفون: Tangherloni, Andrea, Cazzaniga, Paolo
المساهمون: Rundo, Leonardo, Tangherloni, Andrea, Nobile, Marco S., Militello, Carmelo, Besozzi, Daniela, Mauri, Giancarlo, Cazzaniga, Paolo
سنة النشر: 2019
المجموعة: Aisberg - Archivio istituzionale dell'Università di Bergamo
مصطلحات موضوعية: Bimodal image histogram, Genetic Algorithm, Image enhancement, Magnetic resonance imaging, Medical imaging system, Uterine fibroid, Engineering (all), Computer Science Applications1707 Computer Vision and Pattern Recognition, Artificial Intelligence, Settore INF/01 - Informatica
الوصف: 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.
نوع الوثيقة: article in journal/newspaper
وصف الملف: remote
اللغة: English
العلاقة: info:eu-repo/semantics/altIdentifier/wos/WOS:000456222700027; volume:119; firstpage:387; lastpage:399; journal:EXPERT SYSTEMS WITH APPLICATIONS; http://hdl.handle.net/10446/131182Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85056485791
DOI: 10.1016/j.eswa.2018.11.013
الإتاحة: https://doi.org/10.1016/j.eswa.2018.11.013Test
http://hdl.handle.net/10446/131182Test
حقوق: info:eu-repo/semantics/openAccess
رقم الانضمام: edsbas.3D395616
قاعدة البيانات: BASE