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Unsupervised range-constrained thresholding

Zuoyong Li a,, Jian Yang b, Guanghai Liu c, Yong Cheng d, Chuancai Liu b

a Department of Computer Science, Minjiang University, Fuzhou 350108, Chinab School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, Chinac School of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, Chinad School of Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China

a r t i c l e i n f o

Article history:Received 5 November 2009

Available online 26 September 2010

Communicated by Y.J. Zhang

Keywords:

Thresholding

Image segmentation

Human visual perception

Standard deviation

Unsupervised estimation

a b s t r a c t

Three range-constrained thresholding methods are proposed in the light of human visual perception. The

new methods first implement gray level range-estimation, using image statistical characteristics in the

light of human visual perception. An image transformation is followed by virtue of estimated ranges. Cri-

teria of conventional thresholding approaches are then applied to the transformed image for threshold

selection. The key issue in the process lies in image transformation which is based on unsupervised esti-

mation for gray level ranges of object and background. The transformation process takes advantage of

properties of human visual perception and simplifies an original image, which is helpful for image thres-

holding. Three new methods were compared with their counterparts on a variety of images including

nondestructive testing ones, and the experimental results show its effectiveness.

2010 Elsevier B.V. All rights reserved.

1. Introduction

Image segmentation intends to extract an object from a back-

ground based on some pertinent characteristics such as gray level,

color, texture and location (Tao et al., 2008). It is a critical prepro-

cessing step in image analysis and computer vision (Huang and

Wang, 2009; Sen and Pal, 2010). Among the existing segmentation

techniques, thresholding is one of the most popular approaches in

terms of simplicity, robustness and accuracy. Implicit assumption

in image thresholding is that object (foreground) and background

have distinctive gray levels. Thresholding serves a variety of appli-

cations, such as biomedical image analysis (Huet al., 2006; Min and

Park, 2009), character identification (Huang et al., 2008; Nomura

et al., 2009; Pai et al., 2010) and industrial inspection (Ng, 2006).

Thresholding techniques fall into bilevel and multilevel cate-

gory (Coudray et al., 2010; Horng, 2010; Malyszko and Stepaniuk,

2010; Wang et al., 2010) according to the number of segments. The

former assumes an image to be composed of two components (i.e.,

object and background), with an aim of finding an appropriate

threshold for distinguishing both parts. Thresholding result is a

binary image where all pixels with gray levels higher than

determined threshold are classified into foreground and the rest

of pixels assigned to background, or vice versa. The latter category

supposes that an image consists of multiple parts, each having

homogeneous gray level. Obviously, multiple thresholds should

be chosen to group pixels with gray level within a specified range

into one class. It can be regarded as an extension of the bilevel one.

Thresholding can also be classified into parametric and non-

parametric approaches from another perspective (Bazi et al.,

2007; Sahoo and Arora, 2004; Tizhoosh, 2005). In the parametric

approach, gray level distribution of an image is assumed to obey

a given statistical model, and optimal parameter estimation for

the model is sought by using image histogram. Fitted model is used

to approximate practical distribution. Bottom of valley in the mod-

el is regarded as the appropriate location of the optimal threshold.

This usually involves a nonlinear estimation of intensive computa-

tion. The nonparametric method determines the optimal threshold

by optimizing certain criterion, such as between-class variance

(Otsu, 1979), variance (Hou et al., 2006) and entropy (Kapur

et al., 1985; Pun, 1980). The nonparametric approach is proved

to be more robust and accurate.

Many thresholding approaches have been developed over the

last few years (Albuquerque et al., 2004; Kwon, 2004; Ramesh

et al., 1995; Sahoo et al., 1988; Wang et al., 2008). For example,

Bazi et al. (2007) proposed a parametric method, which finds the

optimal threshold through parameter estimation based on the

assumption that object and background follow a generalized

Gaussian distribution. Otsus method (1979) chooses the threshold

by maximizing the between-class variance of both object and

background. Sahoo et al. (1988) revealed that Otsus method is

one of the better threshold selection approaches for general real

world images with regard to uniformity and shape measures.

However, Otsus method exhibits a weakness of tending to classify

0167-8655/$ - see front matter 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.patrec.2010.09.020

Corresponding author. Tel.: +86 13906926400; fax: +86 059183761607.

E-mail addresses: [email protected], [email protected] (Z. Li).

Pattern Recognition Letters 32 (2011) 392402

Contents lists available at ScienceDirect

Pattern Recognition Letters

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / p a t r e c
http://dx.doi.org/10.1016/j.patrec.2010.09.020mailto:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.patrec.2010.09.020http://www.sciencedirect.com/science/journal/01678655http://www.elsevier.com/locate/patrechttp://www.elsevier.com/locate/patrechttp://www.sciencedirect.com/science/journal/01678655http://dx.doi.org/10.1016/j.patrec.2010.09.020mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.patrec.2010.09.020


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