A Comparative Analysis of Otsu Thresholding and K-means Algorithm for Image Segmentation
Date
2019
Authors
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Publisher
Department of Computer Science and Information Technology
Abstract
Image segmentation is an aspect of image processing and is used to find out the object in the
image and dividing the image into different segments and discrete regions. The goal of image
segmentation is to change the representation of an image into something that is more
meaningful and easier to analyze and usually serves as the pre-processing before pattern
recognition, feature extraction, and compression of the image. There are several challenges
emerged in the field of image segmentation such as to differentiate between regions that may
be determined by different factors, such as color, gray level or texture, overlapping objects
can be difficult to separate also, shadowing can create additional borders. Many kinds of
research have been done in the area of image segmentation. This research evaluates the two
image segmentation algorithms Otsu thresholding and K-means using a parameter: Peak
Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Content (SC) and
Structural Similarity Index (SSIM) to measure the quality of segmented image. The lower
value of MSE shows that the higher quality of segmented image is obtained. The higher value
of PSNR and SSIM shows that for K-means original and segmented images do not loss the
much of the property and have the higher similarities in input and segmented image. The
lower SC value of an input and segmented image indicate the better quality of the image is
generated by Otsu thresholding.
Keywords: Image Segmentation, MSE, PSNR, SSIM, SC, K-means, Otsu thresholding,
Description
Keywords
Image Segmentation, K-means, Otsu thresholding, Structural Similarity Index