Detection of bleeding patterns in WCE video using TV-Retinex

doi:10.4236/jbise.2010.312148

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J. Biomedical Science and Engineering, 2010, 3, 1143-1145

doi:10.4236/jbise.2010.312148 Published Online December 2010 (http://www.SciRP.org/journal/jbise/ JBiSE

Published Online December 2010 in SciRes. http://www.scirp.org/journal/JBiSE

Detection of bleeding patterns in WCE video using TV-Retinex

Ming Li

Department of Computer Science, Huazhong University of Science and Technology, Wuhan, China

Email: [email protected]

Received 12 Octorber 2010; revised 18 Octorber 2010; accepted 20 Octorber 2010.

ABSTRACT

The Retinex theory is used to deal with the removal

of unfavorable illumination effects from images. In

this paper, we present the Retinex theory for bleeding

detection in wireless capsule endoscopy (WCE). This

processing is quite appropriate to refresh old bleed-

ing region and bleeding region in shadow. A novel

total variation model (TV-Retinex) is proposed to

solve the Retinex problem quickly; also a support

vector machine is employed for classification. Ex-

perimental results demonstrate the efficacy of the

proposed method.

Keywords: Wireless Capsule Endoscopy; Bleeding

Detection; Retinex Theory; Total Variation

1. INTRODUCTION

Wireless Capsule Endoscopy (WCE) is a novel tool for

visualizing abnormalities in the gastrointestinal tract,

widely used to replace traditional endoscopy diagnosis,

with advantages of the capability for reaching even the

duodenum and small intestine. About 50,000 images

are obtained during an exam. Usually, these images are

reviewed in a form of a video at sp eeds between 5 to 40

frames per second, and the time spent reading the video

varies between 45 to 180 minutes. This long review

time was usually reported as the major weakness of

capsule video endoscopy.

To reduce the assessment time, many methods have

been developed for automatic or semi-automatic detec-

tion of certain type of abnormal images in WCE video.

The bleeding region is one of the most widely used

features for its simple and association with many dis-

eases. The first software tool to detect bleeding images

is provided by the manufacturer; however, sensitivity

and specificity of this system are reported to be only

21.5% and 41.8%, respectively [1]. Recently, Li and

Meng [2] proposed a method using Tchebichef poly-

nomials and hue/saturation/intensity (HSI) color space,

combined with uniform local binary pattern (LBP) to

classify a dataset contain 3600 bleeding patches and

3600 non-bleeding patches. The reported specificity

and sensitivity are 93.2% and 91.6%. However, some

shadow patches are removed in their experiments.

2. TV-RETINEX

In recent years, with the development of color con-

stancy theory, enhancement algorithms based on the

Retinex theory have become a hot spot of research. The

most applied algorithms of them are single-scale Reti-

nex algorithm (SSR) [3] and multi-scale Retinex algo-

rithm (MSR) [4]. Recently, Kimmel [5] introduces a

varia t ional mo d e l fo r th e R eti nex problem.

 





Minimize:

Subject to: , and ,0 on

Elllsl sdxdy

ls ln











(1)

where



is the support of the image, its bound-

ary, and 



is the normal to the boundary.



and



are free non-negative real parameters. A Projected

Normalized Steepest Descent (PNSD) algorithm is de-

veloped to solve this problem. For image of size

250*250 pixels, it is usually needs 3-5 seconds to

process. Although the above model produces good re-

sults, the slow computation makes it unsuitable to deal

with large number of images. To speed up computation,

we modify the model and adopt a more efficient algo-

rithm for computing. The proposed model is defined as

follow:

 

s.t. , ,0 on

ElTVllsl s

ls ln





 

 (2)

We use instead of

()TV l2

ldxdy

.This change

allows us to use a more efficient split Bregman algo-

rithm [6] for calculation. To apply Bregman splitting,

we first replace l



by an auxiliary variable dl





to decoupling the L1 and L2 terms in (2).This yields

the constrained problem

M. Li et al. / J. Biomedical Science and Engineering 3 (2010) 1143-1145

1144



*12 2

argmin 22

ld lsls





(3)

where .To solve this constrained problem, we

convert it to an unconstrained problem by introducing a

quadratic penalty function:

d



** 12

(, )

,argmin 2

ldd ls

lsd l







 



(4)

Although this problem can be solved using an alter-

nating minimization process, it suffers from slowly

convergence rate for large



, which is needed to en-

force the constraint exactly. To avoid this

difficulty, the Split Bregman approach enforces the

constraint using a Bregman iteration technique [7].

dr

Hence, in this work we added a Bregman vector

inside of the quadratic penalty function. Then it be-

come a sequence of unconstrained problems defined by,



11 12

(, )

,argmin

ldd ls

lsd lb









 

(5)

11kkkk

bbld



 1

(6)

Now, the unconstrained problem (5) can be solved

using a simple alternating minimization scheme as fol-

low:

The first step is to minimize with respect to . This

is a differentiable optimization problem and the solu-

tion is obtained by solving



lIsbd

 



 . (7)

The system is strictly diagonally dominant, so we

can use a fast iterative algorithm to get approximate

solutions to it, such as Gauss-Seidel method. We next

minimize (5) with respect to d. This optimization

problem is element-wise decoupled. We can explicitly

compute it using shrinkage operators.



dshrinklb







 (8)

where





,max

shrink xx





 . (9)

When we put all of these pieces together, and the

constraint , we get the following very simple, yet

efficient algorithm:

ls



1Retinex

111

while do

max,,,

kkk

kkkk

lGSsdb

dshrinklb

bbld

















 



Here, we use to denote one

sweep of the Gauss-Seidel formula. For a 250*250 size

image, our method needs at most 0.15 seconds on a

personal computer, and the enhancement results are

nearly same as shown in Figure 1.



Retinex ,,

GSs db

3. EXPERIMENTAL RESULTS

In order to compare the classification performance on

original images and images after TV-Retinex, we built

two datasets. First we select bleeding patches and

non-bleeding patches on original images by experts to

construct an original dataset, and then choose the cor-

responding patches from the enhanced images to form

another dataset. The processing of selecting bleeding

patch es on or iginal imag e s is as fo llo w :

We select 150 images containing bleeding regions of

various kinds from a WCE image database. Another

1000 images containing no bleeding regions are col-

lected from 10 patients’ video segmen ts, each for about

100 images. The size of each image is 280×280. For

every bleed ing image, the bleed ing region is mar ked by

a medical expert with a year of experiences in WCE

images analysis. We then divide every image into small

patches with 20*20 pixels, as illustrated in Figure 2,

since larger siz e may make s ome very sma ll symptoms

undetected; every patch seems homogeneous for color

feature, and finally it is much more robust than a single

pixel.

The patch in the bleeding region is marked as ab-

normal, the patch outside the bleeding region as normal,

and the patches come across the region edge and out-

side the region of circle are discarded as shown in Fig-

ure 2.

The patches in the 1000 non-bleeding images are all

marked as normal. Finally, we get 4,000 bleeding

patches and 50,000 non-bleeding patches for the ex-

Figure 1. The results of TV-Retinex in comparison with Ron’s

method. The first row shows the original images, the second

row shows the results of TV-Retinex, and the third row shows

the results of Ron’s method.

M. Li et al. / J. Biomedical Science and Engineering 3 (2010) 1143-1145

1145

4. CONCLUSION

We propose a new fast calculated color image enhance-

ment method called TV-Retinex for the detection of

bleeding in WCE video. The experimental results show

that the image enhancement processing is helpful fo r the

detection.

REFERENCES

[1] Ftancis, R.D. (2004) Sensitivity and specificity of the red

blood identification (RBIS) in video capsule endoscopy.

The 3rd International Conference on Capsule Endoscopy,

Miami, FL.

Figure 2. Patches chosen from one WCE image.

Table 1. Classification results with and withour TV-Retinex. [2] Baopu, L. (2009) Computer-aided detection of bleeding

regions for capsule endoscopy images, IEEE Trans. on

Biomedical Engineering, 56, 1032-1039.

Before TV-Retinex After TV-Retinex

Color

Space sensitivity specificity sensitivity specificity

RGB 90.2% 98.5% 96.3% 99.3%

HSI 88.3% 98.5% 95.3% 99.1%

RGB+HSI 89.3% 98.6% 96.6% 99.5%

[3] Jobson, D.J., Rahman, Z., and Woodell, G. A. (1997) Prop-

erties and performance of a center/surround retinex.

IEEE Transactions on Image Processing, 6, 451-462.

[4] Jobson, D.J., Rahman, Z., and Woodell, G.A. (1997) A

multiscale retinex for bridging the gap between color

images and the human observation of scenes. IEEE

Transactions on Image Processing, 6, 965-976.

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(2003) Variational framework for retinex. International

Journal of Computer Vision, 52, 7-23.

periments. The unbalance proportion of normal and

abnormal samples is in line with the actual situation,

although it will reduce the accuracy of the system

compared with balanced proportion of samples [8]. [6] Goldstein, T., and Osher S. (2008) The split bregman

method for L1 regularized problems. UCLA CAM Report,

08-29.

We use th e average color of each patch as its f eature

vector. (R,G,B),(H,S,I) and (R,G,B,H,S,I) feature vec-

tors are tested on a radial basis kernel SVM using 10

fold cross-validation. We tabulated the best classifica-

tion results of each feature vector on the two databases

in Table 1. As can be seen in Tabl es 1, the TV-Retinex

processing significantly improves the classification in

all the three different feature sets, and the RGB is better

than HSI color space.

[7] Bregman, L. (1967) The relaxation method of finding the

common points of convex sets and its application to the

solution of problems in convex optimization. USSR

Computational Mathematics and Mathematical Physics,

7, 200-217.

[8] Weiss, G.M. (2001) The effect of class distribution on

classifier learning: An empirical study. Technical Report

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JBiSE