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IEEE SIGNAL PROCESSING LETTERS, VOL. 17, NO. 1, JANUARY 2010 91

Registration of Images With Outliers

Using Joint Saliency Map

Binjie Qin, Member, IEEE, Zhijun Gu, Xianjun Sun, and Yisong Lv

Abstract—Mutual information (MI) is a popular similarity

measure for image registration, whereby good registration can

be achieved by maximizing the compactness of the clusters in the

joint histogram. However, MI is sensitive to the “outlier” objects

that appear in one image but not the other, and also suffers from

local and biased maxima. We propose a novel joint saliency map

(JSM) to highlight the corresponding salient structures in the

two images, and emphatically group those salient structures into

the smoothed compact clusters in the weighted joint histogram.

This strategy could solve both the outlier and the local maxima

problems. Experimental results show that the JSM-MI based

algorithm is not only accurate but also robust for registration of

challenging image pairs with outliers.

Index Terms—Image registration, joint saliency map, mutual in-

formation, outliers, weighted joint histogram.

I. INTRODUCTION

MAGE registration can be considered as ﬁnding the op-

timal transformation

between the reference image and

the ﬂoating image to maximize a deﬁned similarity measure

such as mutual information (MI). Since 1995 [1], [2], MI has

been proved to be very effective in image registration. The MI

between

and (with intensity bins and ) is deﬁned as:

(1)

where

and

are the entropy of the intensities

of image

and the entropy of the joint intensities of two im-

ages,

is the intensity probabilities with

and , is the joint intensity probabili-

ties estimated by the joint histogram

MI-based registration methods take advantage of the fact that

properly registered images usually correspond to compactly-

clustered joint histograms [3]. They measure the joint histogram

dispersion by computing the entropy of the joint intensity prob-

abilities. When the images become misregistered, the compact

Manuscript received August 08, 2009; revised September 27, 2009. First

published October 06, 2009; current version published October 28, 2009. This

work was supported in part by the NSFC (60872102), NBRPC (973 Program

2010CB834303), the Science Foundation of Shanghai Municipal Science &

Technology Commission (04JC14060), Shanghai Municipal Health Bureau

(2008115), and the Small Animal Imaging Project (06-545). The associate ed-

itor coordinating the review of this manuscript and approving it for publication

was Prof. H. Vicky Zhao.

B. Qin, Z. Gu, and X. Sun are with the Department of Biomedical Engi-

neering, School of Life Sciences & Biotechnology, Shanghai Jiao Tong Univer-

sity, Shanghai 200240, China (e-mail: bjqin@sjtu.edu.cn; gzj0126@gmail.com;

sxj_sun@sjtu.edu.cn).

Y. Lv is with the Department of Mathematics, Shanghai Jiao Tong University,

Shanghai 200240, China (e-mail: yslv@sjtu.edu.cn).

Color versions of one or more of the ﬁgures in this paper are available online

at http://ieeexplore.ieee.org.

Digital Object Identiﬁer 10.1109/LSP.2009.2033728

Fig. 1. (a)–(b) Intra-operative and pre-operative MR image with a large tumor

resection. (c) Joint histogram dispersion with two clotted clusters (dark red in

pseudo color). (d) Joint saliency map for (a) and (b).

clusters become disperse sets of points in the joint histogram and

the entropy of the joint intensity probabilities increases. Making

no assumptions about the form of the intensity mapping between

the two images, MI is sensitive to the unmatchable outliers, e.g.

the tumor resection in the intra- and pre-operative brain im-

ages [see Fig. 1(a)–(b)]. To reject the outliers, some approaches

are proposed including consistency test [4], intensity transfor-

mation [5], gradient-based asymmetric multifeature MI [6] and

graph-based multifeature MI [7]. However, all these methods do

not emphasize the corresponding salient structures in the two

images to suppress the outliers. Furthermore, MI likely suffers

from local and biased maxima [8] which are caused by the am-

biguities in deﬁning structure correspondence.

Spatial information, i.e. the dependence of the intensities of

neighboring pixels, has been included in MI [9]–[12] to im-

prove registration. Nevertheless, almost all MI-based methods

equally treat each overlapping pixel pair as a separate point in

the overlap area to calculate the joint histogram. This could

raise three issues: 1) when we equally consider the outlier

pixel pairs, the noncorresponding structures overlap and the

histogram will show certain clusters for the grey values of

the outliers. These clusters easily introduce the histogram

dispersion [see Fig. 1(c)] with increasing misregistration; 2)

while registration can be achieved by maximizing the com-

pactness of the histogram, the undesired clotted clusters [see

Fig. 1(c)] related to many noisy pixel pairs in the structureless

regions, such as background and white matter in the brain

image, increase the MI ambiguities and the local maxima [8]

(Fig. 5(c) shows that the normalized MI [1], [20] is in a biased

global maximum when the whole background areas in the two

endoscopic images are exactly aligned); 3) when we group

the intensity pairs as separate points into the histogram, the

independence of the neighboring bins could increase the MI

ambiguities and the local maxima. To solve this problem, joint

histogram smoothing (or blurring) [5], [8] has been used to

increase the dependence of the neighboring histogram bins. We

address these issues above as follows.

In fact, image registration is to match the corresponding

salient structures in both images. To suppress the outliers and

the homogeneous pixel pairs, the corresponding pixel pairs in

the corresponding salient structures should contribute more to

the joint histogram. For example, the corresponding salient

Authorized licensed use limited to: Shanghai Jiao Tong University. Downloaded on November 6, 2009 at 23:03 from IEEE Xplore. Restrictions apply.

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