Image segmentation using fuzzy logic, neural networks and genetic algorithms: Survey and trends

Abstract

Image segmentation is a fundamental process employed in many applications of pattern recognition, video analysis, computer vision and image understanding in order to allow further image content exploitation in an efficient way. It is often used to partition an image into separate regions. As recent trends in image segmentation show, the use of artificial and/or computational intelligence (Al and/or CI) techniques has become more popular as an alternative to the conventional techniques. In this paper, we present an extensive and comprehensive review of the image processing area for advanced researchers. This study introduces the theoretical fundamentals of image segmentation using AI and/or CI techniques based on fuzzy logic (FL), genetic algorithm (GA) and artificial neural networks (ANN). Besides, this survey examines the applications of these techniques in different image segmentation areas. In the literature, these techniques are used as an interpretation tool for segmentation. In our study, these tools are focused on because of their capabilities, such as robustness, segmentation accuracy and low computational costs. Moreover, we review 56 remarkable studies from the last decade (i.e., the years between 2001 and 2010), which involve different image segmentation approaches using FL, GAs, ANNs and hybrid intelligent systems (HISs). In our state-of-the-art survey, the comparison of the reviewed papers in related categories is made based on both the corresponding properties of segmentation as well as performance evaluation of the related method proposed in a given reviewed paper. The results and recent trends are also discussed.