Preprint Article Version 2 This version is not peer-reviewed

Digital Image Segmentation in Matlab: A Brief Study on Otsu’s Image Thresholding

Version 1 : Received: 20 November 2018 / Approved: 21 November 2018 / Online: 21 November 2018 (15:51:51 CET)
Version 2 : Received: 5 February 2019 / Approved: 5 February 2019 / Online: 5 February 2019 (08:12:08 CET)

A peer-reviewed article of this Preprint also exists.

Siddique, M.A.B.; Arif, R.B.; Khan, M.M.R. 2018. Digital Image Segmentation in Matlab: A Brief Study on Otsu’s Image Thresholding. In 2018 International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-5). IEEE. Siddique, M.A.B.; Arif, R.B.; Khan, M.M.R. 2018. Digital Image Segmentation in Matlab: A Brief Study on Otsu’s Image Thresholding. In 2018 International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-5). IEEE.

Journal reference: 2018 International Conference on Innovation in Engineering and Technology (ICIET), IEEE 2018
DOI: 10.1109/CIET.2018.8660942

Abstract

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

Subject Areas

Image segmentation, Otsu’s thresholding, Two level thresholding, Three level thresholding, Error minimization, Bayes decision, Probability density function (PDF).

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