A Comparison Of Image Segmentation Using Supervised Learning, Unsupervised Learing, And Spine Segmentation

The image segmentation is used to change or simplify the image representation for the purpose of easy understanding or quicker analysis. Image segmentation is a process of segmenting an image into groups of pixels based on some criterions. Image segmentation is the process of partitioning a digital image into multiple segments. The purpose of image segmentation is to partition an image into meaningful regions with respect to particular application. The image segmentation is used for various applications such as medical images, Satellite images, content based image retrieval, machine vision, Recognition Tasks and Video Surveillance. There are so many methods are used for segmentations such as compression based methods, thresholding, and clustering. The clustering methods can be divided into two parts namely supervised and unsupervised. Supervised clustering involves predefining the cluster size for segmenting whereas unsupervised segmentation segments by its own cluster values. The spine segmentation is used to get validate cluster extraction and vertibri output. Comparing the three methods the accuracy level is differ from other methods. The advantages of each method are the speed of time is achieved. Assistant Professor, Department of Computer Science, CBM College, Coimbatore-42. Director, Department of Computer Applications, Sri Ramakrishna Engineering College, Coimbatore 22.