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Open AccessArticle

An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique

1
Department of Mathematics and Statistics, Faculty of Science and Arts, Jordan University of Science and Technology, Irbid 22110, Jordan
2
Departamento de Matemática Aplicada, Universidad de Granada, Granada 18071, Spain
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Departamento de Física Aplicada, Universidad de Granada, Granada 18071, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Entropy 2015, 17(12), 7996-8006; https://doi.org/10.3390/e17127858
Received: 4 August 2015 / Revised: 11 November 2015 / Accepted: 24 November 2015 / Published: 4 December 2015
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory I)
In this paper we develop a new procedure for entropic image edge detection. The presented method computes the Jensen–Shannon divergence of the normalized grayscale histogram of a set of multi-sized double sliding windows over the entire image. The procedure presents a good performance in images with textures, contrast variations and noise. We illustrate our procedure in the edge detection of medical images. View Full-Text
Keywords: entropy; Jensen–Shannon divergence; image segmentation; entropic edge detection; multi-size window processing entropy; Jensen–Shannon divergence; image segmentation; entropic edge detection; multi-size window processing
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Katatbeh, Q.D.; Martínez-Aroza, J.; Gómez-Lopera, J.F.; Blanco-Navarro, D. An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique. Entropy 2015, 17, 7996-8006.

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