Comparative Analysis of Image Segmentation using Thresholding

Image segmentation is an important technology for image processing. It is a critical and essential component of an image analysis and/or pattern recognition system, and is one of the most difficult tasks in image processing. Image segmentation is the process by which we segment a given image into several parts so that we can further analyzed each of these parts present in the image. We can extract some information by analyzing them and this information is useful for high-level machine vision applications. In this paper, we are analyzing and evaluating the various types of thresholding techniques such as single-value thresholding, multiplethresholding, adaptive thresholding, optimal thresholding and local thresholding. These different thresholding techniques are extensively used in image segmentation. We have taken into consideration the threshold value which is used to segment a given image. The experimental results show that each technique performs better depending on the different situations. The results are implementing and shown on various images using Image Processing Toolbox (IPT) in MATLAB.

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