Neural network in medical image processing

Abstract

With medical imaging playing an increasingly prominent role in the diagnosis of disease, interests in medical image processing have increased significantly over the past decades [1]–[3]. Especially methods based on artificial neural networks (ANNs) have attracted more attention. In 1992, a comprehensive survey of neural networks in image processing has been published by Miller et al. [4]. In their review article, Miller etc. predicted that neural networks would become widely used in medical image processing. This predication turned out to be right. According to our searching results with Google Scholar, more than 33000 items were found on the topic of medical image processing with ANNs during the past 16 years. The intention of this article is to cover those approaches introduced and to make a map for ANN techniques used for medical image processing. Instead of trying to cover all the issues and research aspects of ANNs in medical image processing, we focus our discussion on three major topics: medical image preprocessing (i.e. de-noising and enhancement), medical image segmentation, and medical image object detection and recognition.