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Article

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

by 1,2,†, 3,†, 4,†, 1,† and 4,*,†
1
College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, China
2
Logistic Engineering College, Shanghai Maritime University, Shanghai 200135, China
3
State GRID Quzhou Power Supply Company, No.6, XinHe Road, Quzhou 324000, China
4
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2018, 20(11), 827; https://doi.org/10.3390/e20110827
Received: 1 October 2018 / Revised: 22 October 2018 / Accepted: 24 October 2018 / Published: 28 October 2018
(This article belongs to the Section Information Theory, Probability and Statistics)
Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods. View Full-Text
Keywords: image segmentation; thresholding; non-local filter; two dimensional histogram image segmentation; thresholding; non-local filter; two dimensional histogram
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MDPI and ACS Style

Jiang, C.; Yang, W.; Guo, Y.; Wu, F.; Tang, Y. Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy. Entropy 2018, 20, 827. https://doi.org/10.3390/e20110827

AMA Style

Jiang C, Yang W, Guo Y, Wu F, Tang Y. Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy. Entropy. 2018; 20(11):827. https://doi.org/10.3390/e20110827

Chicago/Turabian Style

Jiang, Chundi, Wei Yang, Yu Guo, Fei Wu, and Yinggan Tang. 2018. "Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy" Entropy 20, no. 11: 827. https://doi.org/10.3390/e20110827

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