Abstract
Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image’s brightness information is taken into account in the procedure of threshold selection. Considering the contextual information between pixels can improve segmentation accuracy. To to this, a new thresholding method is proposed in this paper. The proposed method constructs a new two dimensional histogram using brightness of a pixel and local relative entropy of it’s neighbor pixels. The local relative entropy (LRE) measures the brightness difference between a pixel and it’s neighbor pixels. The two dimensional histogram, consisting of gray level and LRE, can reflect the contextual information between pixels to a certain extent. The optimal thresholding vector is obtained via minimizing cross entropy criteria. Experimental results show that the proposed method can achieve more accurate segmentation results than other thresholding methods.
Highlights
Image segmentation is a fundamental task in many computer vision based applications, such as medical image analysis [1], crack detection [2, 3], video analysis [4], plant disease recognition [5], etc
In order to illustrate its performance of our proposed method, it is used to segment several images and compared to Otsu thresholding method [14], Otsu method based on Gray level-local relative entropy (GLLRE) histogram (Otsu-GLLRE), Kapur method [15] and Kapur method based on GLLRE histogram (Kapur-GLLRE)
A new method is proposed for image segmentation in this paper
Summary
Image segmentation is a fundamental task in many computer vision based applications, such as medical image analysis [1], crack detection [2, 3], video analysis [4], plant disease recognition [5], etc. These classical thresholding segmentation methods and their variants take only the brightness information into account and neglect the contextual information between pixels, which may result in poor segmentation performance or even false segmentation To solve this problem, Abutaleb proposed the concept of two-dimension histogram [18]. Xiao proposed a new method to construct two dimension histogram by using the resemblance between a pixel and it’s neighbors as the contextual information and the resulted two dimension histogram is called gray level spatial correlation (GLSC) histogram [21]. Zheng et al constructed a two-dimension histogram using gray level of original image and its local variance [23]. The proposed method constructs a new two dimension histogram using gray level of a pixel and it’s local relative entropy of it’s neighbors. Let C0 and C1 denotes object and background, their probability distribution are
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