Comparative analysis of image segmentation algorithms for pattern recognition

Image Segmentation is one of the hopeful and emerging fields in image processing. The examined algorithms are compared according to the quality of its operation at distorted images with respect to ground-truth images. It has applications in various fields like medical applications, astronomical, traffic controlling, Fingerprint recognition, digital forensics, self-driving motor cars, locating objects in satellite images etc. It is the process of splitting an image into sub regions with respect to one or more characteristics. Image segmentation is the basic step to analyse images and extract data from them. In the image segmentation process, the anatomical organization or the region of interest needs to be defined and extracted out so that it can be viewed independently. In this comparative study we venture the significant place of segmentation of images in pulling out information for decision making. Hence characterization, visualization of region of interest in any image, delineation plays an important role in image segmentation. Using the different algorithms the current methodologies of image segmentation is reviewed so that user interaction is possible for images. Image segmentation results have an effect on image analysis and it following higher order tasks. Image analysis includes object description and representation, feature measurement. Image segmentation based on Region Based, Edge Detection, Thresholding, Clustering, Fuzzy Logic and Neural Network are discussed and compared.