Techniques of image segmentation: a review

Image segmentation is a popular topic of research. Image segmentation divides an image into different parts that can be used for further analysis. By doing so, the image becomes simple and more meaningful information can be extracted. The segmentation techniques divide an image into multiple parts based on certain features of the image namely: color, texture, and intensity value of the pixel. Segmentation is considered as one of the toughest tasks for extracting features from an image, detection of objects and lastly classification of the image. The applications of image segmentation in every aspect of life such as satellite image analysis, object detection and recognition, in agricultural field, self-driving vehicles, and medical imaging. Has become indispensable. Till date, though researchers have developed many segmentation techniques, they are unable to design a generalized methodology for the image segmentation problems. A review of image segmentation techniques has been presented in this study. A summary of the advantages and disadvantages of these techniques has been presented. The focus of this manuscript is to provide a summary of the available research work on segmentation which will benefit the enthusiastic researchers in gaining better understanding about segmentation models in various application domains.