Enhancement of Image Segmentation osing Automatic Histogram Thresholding

Abstract

This study is focused on histogram thresholding methods automatically. In computer vision, Image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. In other words Segmentation refers to the process of partitioning a digital image into the multiple segments (set of pixels as known as super pixels). Two very simple image segmentation techniques that are based on the gray level histogram of an image are Thresholding and Clustering. Thresholding method is widely used for image segmentation approach. It is useful in discriminating foreground from the background. By selecting an adequate threshold value T, or automatically computing threshold value T, the gray level image can be converted in to binary image. Several methods are there to find the threshold automatically for image segmentation. Some of the methods like Otsu, Kapur, Triangle, Iterative and also manually threshold is calculated for different type of images like X-ray computed tomography (CT-Scan), magnetic resonance imaging (MRI), synthetic aperture radar (SAR), Ultrasound image were explained and the results are presented to show the validity of the methods.
DOI: 10.17762/ijritcc2321-8169.160427