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

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...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2015-12, Vol.17 (12), p.7996-8006
Main Authors: Katatbeh, Qutaibeh, Martínez-Aroza, José, Gómez-Lopera, Juan, Blanco-Navarro, David
Format: Article
Language:English
Subjects:
Divergence
Edge detection
entropic edge detection
entropy
Image contrast
Image detection
Image segmentation
Jensen–Shannon divergence
Medical imaging
multi-size window processing
Sliding
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e17127858