Adaptive windowed range-constrained Otsu method using local information

Jia Zheng; Dinghua Zhang; Kuidong Huang; Yuanxi Sun; Shaojie Tang

doi:10.1117/1.JEI.25.1.013034

19 February 2016 Adaptive windowed range-constrained Otsu method using local information

Jia Zheng, Dinghua Zhang, Kuidong Huang, Yuanxi Sun, Shaojie Tang

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 1, 013034 (February 2016). https://doi.org/10.1117/1.JEI.25.1.013034

Abstract

An adaptive windowed range-constrained Otsu method using local information is proposed for improving the performance of image segmentation. First, the reason why traditional thresholding methods do not perform well in the segmentation of complicated images is analyzed. Therein, the influences of global and local thresholdings on the image segmentation are compared. Second, two methods that can adaptively change the size of the local window according to local information are proposed by us. The characteristics of the proposed methods are analyzed. Thereby, the information on the number of edge pixels in the local window of the binarized variance image is employed to adaptively change the local window size. Finally, the superiority of the proposed method over other methods such as the range-constrained Otsu, the active contour model, the double Otsu, the Bradley’s, and the distance-regularized level set evolution is demonstrated. It is validated by the experiments that the proposed method can keep more details and acquire much more satisfying area overlap measure as compared with the other conventional methods.

Citation Download Citation

Jia Zheng, Dinghua Zhang, Kuidong Huang, Yuanxi Sun, and Shaojie Tang "Adaptive windowed range-constrained Otsu method using local information," Journal of Electronic Imaging 25(1), 013034 (19 February 2016). https://doi.org/10.1117/1.JEI.25.1.013034

Published: 19 February 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available