MULTIDIMENSIONAL WAVELET ANALYSIS FOR

RECOGNITION OF LESIONS IN COLPOSCOPY TEST

Diana Ivone Tapia López, Aldrin Barreto Flores and Leopoldo Altamirano Robles

National Institute of Astrophysics, Optics and Electronics, Computer Science Department

Luis Enrique Erro No.1, Sta. María Tonantzintla, 72840, Puebla, México

Keywords: Cervical cancer, temporal texture analysis, wavelet analysis, wavelet-aggregated signal.

Abstract: Cervical cancer is an important worldwide disease due the high rate of incidence in the population.

Colposcopy is one of the diagnostic tests employed in recognition of lesions, which performs a visual

examination of the cervix based on temporal reaction of the surface stained with acetic acid. It is proposed

in this paper to evaluate the temporal texture changes produced by the acetic acid based on the concept of

the wavelet-aggregated signal in order to identify lesions. An aggregated signal is a scalar signal providing

maximum information on the most general variations present in all the processes analyzed and at the same

time suppressing components that are characteristic of individual processes. Texture metrics based on

spatial information are used in order to analyze temporally the acetic acid response and deduce appropriate

signatures. Later, temporal information is analyzed using multidimensional wavelet analysis for

identification of lesions.

1 INTRODUCTION

An important disease that has been widely studied is

the cervical cancer due the high rate of incidence in

the population. Actually, a group of tests have been

developed for the early diagnosis of this illness: Pap

smear, colposcopy and biopsy (Claude, Winzenrieth,

et al, 2002). Colposcopy test evaluates several

reactions appearing after the application of acetic

acid on the surface of the cervix; it makes a visual

analysis of the cervix surface using a large

microscope called colposcope, finding lesions and

observing their severity (Parker, Karins and

O’Connor, 1998). The acid causes the appearance or

emphasis of lesions visually analyzed by the

physician (Burghardt, 1991). Several computer

vision based solutions have been proposed in order

to automatically identify abnormalities in the cervix

using colposcopy images.

An image analysis using pixel intensity is

presented in (Pogue, Myceck and Harper, 2000),

evaluating the Red-Green-Blue (RGB) channels,

spatial frequencies, fractal dimension and Euler

number, which was the best criteria for the

discrimination of cervical neoplasia using a small set

of cases. Different computer vision techniques have

been also used in other works, for example, color

and texture analysis using pixel co-ocurrence matrix

for segmentation of lesions (Claude, Pouletaut, et al,

2001). A colposcopy images classification using

edge features is presented in (Claude, Winzenrieth,

et al, 2002). Authors generate a digital signature of 6

contour patterns and analyze the power spectrum of

each one, in order to detect discriminatory patterns

that are learned in a neural network. Related works,

which analyze images to discriminate colposcopy

lesions lack of generality because they focus in a

single feature of the lesion, reporting a better

characterization of the lesions the use of temporal

information, as is described in (Parker, Karins and

O’Connor, 1998), (Tumer, Ramanujam et al, 1998)

and (Parker, Mooradian, et al, 2002).

In this paper we propose to evaluate the temporal

texture changes produced by the acetic acid based on

the concept of the wavelet-aggregated signal in order

to identify lesions in cervix region. Spatial texture

metrics are used for analysis of temporal changes on

cervix surface. These metrics are based in gray level

information between pixels and they are suitable for

characterization of lesions in cervix. We show how

temporal information of texture analysis can be used

to perform multidimensional wavelet analysis, in

350

Ivone Tapia López D., Barreto Flores A. and Altamirano Robles L. (2007).

MULTIDIMENSIONAL WAVELET ANALYSIS FOR RECOGNITION OF LESIONS IN COLPOSCOPY TEST.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications - IFP/IA, pages 350-355

Copyright

c

SciTePress

order to identify those regions in cervix where

lesions are presented.

The rest of this paper is organized as follows:

next section presents a briefly description of texture

metrics and temporal information obtained; in

section 3 wavelet-aggregated signal is briefly

described; image sequences used in this work are

presented in section 4; experimental results are

described in section 5; finally, some conclusions and

directions for future work are presented in section 6.

2 TEMPORAL TEXTURE

METRICS

2.1 Texture Metrics

The analysis of temporal changes on the cervix

surface has been performed using the metrics

presented in (Baeg, Kehtarnavaz, 2002) to classify

breast mass. These metrics are well correlated with

colposcopy exam because features of lesions are

similar to the breast mass (Tapia, Barreto and

Altamirano, 2006).

Denseness measure is obtained by calculating the

distribution of gray values, maxima/minima local

points in rows and columns. The idea is to produce a

binary image that represents the distribution of

maxima and minima gray values in a region of

interest. A pixel is considered local column

maximum and it is assigned a zero value in the

binary image if its value in the original image is

greater than its neighbors, in other case it is assigned

one. The equation (1) shows the calculation of local

column maximum and (2) local row maximum.

g(x-1,y) < g(x,y) > g(x+1,y) .

(1)

g(x,y-1) < g(x,y) > g(x,y+1) .

(2)

Architectural distortion is associated with new

details revealed by the acetic acid and it can be

quantified using a gradient based measure like in

(Baeg, Kehtarnavaz, 2002), because new details

appears like small points, mosaics and edges. The

equation used for its calculation is presented in (3).

The values N

1

and N

2

correspond to the number of

pixels in which gradient were different to zero. This

process uses just those pixels that are presenting

changes in the image. The 3 value in the equation is

used in order to increase the presence of changes

presented in the image. A small change cannot

increase so much while a high change can be more

noticeable.

2

),(

3

1

),(

3

),(),1(),()1,(

N

yxgyxg

N

yxgyxg

I

yxyx

∑

∑

∀∀

−+

+

−+

=

.

(3)

A detail description of the metrics can be

consulted in (Baeg, Kehtarnavaz, 2002).

2.2 Temporal Texture Analysis

Denseness and architectural distortion values are

extracted and tracked in each frame of the sequence,

selecting a region of interest during the acetic acid

reaction. Graphs in Figure 1 show the temporal

changes occurring when we select a normal and

abnormal region of interest over the cervix surface.

Density presents no significant changes in its

temporal behavior for normal cases (lower curve)

because the acetic acid is not producing changes in

the cervix surface, while in abnormal cases (upper

curve) significant changes are represented by a

decrease in the slope at the end of the curve.

Architectural distortion evaluation is presented in

Fig. 1.b. The abnormal curve presents an increasing

slope because new details appear as the cervix

surface reacts with the acetic acid, while normal

region is not presenting changes. it’s important to

remark the difference between normal and abnormal

cases and how the temporal behavior of metrics can

be used for discrimination and identification of

lesions in a colposcopy exam using a computer

vision approach.

Figure 1: Temporal texture analysis of medical cases.

MULTIDIMENSIONAL WAVELET ANALYSIS FOR RECOGNITION OF LESIONS IN COLPOSCOPY TEST

351

3 WAVELET AGGREGATED

SIGNAL

The concept of the wavelet-aggregated signal was

previously introduced by (Lyubushin, 1998). An

aggregated signal is a scalar signal providing

maximum information on the most general

variations present in all of the processes analyzed

and at the same time suppressing components that

are characteristic of individual processes and that

usually represent local noise. The aggregated signal

is constructed in two stages that will be described

below. A detailed description of the wavelet-

aggregated signal can be found in (Lyubushin,

2000).

Below, only Haar wavelets (4) are used. This

choice is dictated by the fact that we seek the most

pronounced common variations for which basis (4)

is best suited.

⎥

⎦

⎤

⎜

⎝

⎛

∈−=Ψ

2

1

,01)( tift

,01,

2

1

1 totherfortif

⎥

⎦

⎤

⎜

⎝

⎛

∈+

(4)

Let x(t) be a signal with a discrete time t N

samples long, t = tj= j∆t, j= 1, … , N. It is assumed

that N is an integer of the 2

m

type, which is

convenient for the subsequent use of the fast wavelet

transformation. If N is not equal to 2

m

, the signal x(t)

can be complemented by zeros until its length

becomes 2

m

, where m is the minimum integer for

which N≤2

m

. In the case of a finite sample and

discrete time, the formula for multiresolution

analysis is

.2

),()()(

),()(

)(

)()()(

2

1

)()(

1

)(

)(

tj

tctx

txdtx

j

jj

j

m

m

ΔΧ=

−Ψ=

+=

∑

∑

−

=

=

αα

ααααα

α

α

τ

ττ

α

(5)

The coefficient d in (5) is equal to the mean of

x(t), t = 1, …, N. The set of values c

(

α

)

(

τ

j

(

α

)

) and d

are calculated using the direct fast wavelet

transformation. The wavelet-aggregated signal is

constructed in two stages.

The first stage initially involves the calculation

of the wavelet coefficients for each time series under

study and at each scale level using the fast discrete

wavelet transformation. Before the transformation,

the time series are converted to series in increments

and are normalized in order to provide diverse

physical signals of different scales for join

processing. The initial wavelet coefficients are then

converted to the so-called canonical wavelet

coefficients, which are obtained from covariance

matrices of wavelet coefficients at each detail level

using the method of canonical correlations. This

conversion aims at removing individual noise from

the wavelet coefficients and to amplify the common

component.

At the second stage, the intensity of the common

component is additionally increased by calculating

the first main component of the covariance matrices

of canonical wavelet coefficients at each detail level.

Thus, a scalar sequence of hypothetical wavelet

coefficients is obtained at each detail level, which

makes it possible to calculate the inverse discrete

fast wavelet transform and to obtain the time

realization of a scalar signal called the wavelet-

aggregated signal of the initial time series.

4 COLPOSCOPY IMAGE

SEQUENCES

Sequences used in this work include 8 abnormal

medical cases shown in Figure 2, captured by a

couple of colposcopy specialists in a public health

institution in Mexico. The equipment used for the

capture was a standard colposcope, a framegrabber

and a digital camera. Sequences were acquired after

the acetic acid application in order to register the

most important changes on the cervix.

Figure 2: Abnormal medical cases used in the study.

The size of the sequences varies in a range of

160 frames to 240 frames and the capture time is

about 8-12 seconds, that is the time that colposcopy

test last, using a rate of 20 frames per second.

Images in Figure 3 present the initial frame before

acetic acid application and the last frame of the

sequence after the acetic acid reaction has occurred.

There are some problems that must be solved in

order to have a correct temporal analysis: the image

stabilization and the non uniform illumination. A

more detailed description of the methods used here

is described in (Barreto, Altamirano, 2005).

VISAPP 2007 - International Conference on Computer Vision Theory and Applications

352

Figure 3: Texture changes after acetic acid application.

5 RESULTS

After image stabilization and non uniform

illumination correction processes are performed,

texture metrics are applied to the sequences for

temporal analysis estimation. After that, results of

temporal analysis are processed using the wavelet-

aggregated signal to identify lesions in cervix.

We just present results of architectural distortion

in this paper because this metric achieved in better

results in wavelet analysis. In every abnormal case a

region of interest to be analyzed along the sequence

was selected. This region was divided in several

subregions that were analyzed with texture metrics,

and the resulting curve describes the temporal

behavior of each subregion. Results of temporal

information are presented in Figure 4.

As we can see in Figure 4, in those subregions

where new details appear, the curve suffers an

increase in its slope value, while values in normal

regions do not present important changes,

identifying lesions in cervix accurately.

Results obtained from temporal analysis were

processed using the wavelet-aggregated signal

described previously. It’s important to highlight that

wavelet analysis allows to identify lesions,

eliminating components that are characteristic of

individual processes, like local noise caused by

measurement uncertainties. The wavelet-aggregated

signal for every abnormal case is constructed with

the time series described by each subregion in the

region of interest. So, we have N time series for

every abnormal case to be analyzed in the

construction of the wavelet. N is the total amount of

subregions in every case.

The method described in section 3 produce a new

signal that is characterized by significantly lower

noise and preserves the most informative variations

in time during the test.

Figure 4 plots the wavelet-aggregated signal for

every abnormal case, showing over the horizontal

axis the subregion where lesions appear. For a better

appreciation of results, we also show standard

deviation and mean values in every case, splitting

data in normal and abnormal subregions and getting

two measures for each case. These measures are

summarized in Table 1. It can be noticed that

standard deviation value in normal subregions is

smaller than those subregions where lesions

appeared, showing data dispersion and variations in

the signal.

In the same way, the wavelet-aggregated signal

generated in every case show variations in those

subregions where significant changes are present.

This analysis give support to temporal analysis

performed with texture metrics.

We can compare graphs for texture metrics with

the wavelet-aggregated signal of every abnormal

case, in order to identify subregions where lesions

appear as for example subregions 9 to 12 and 25 to

26 in the first medical case, where it can be noticed

an increase in the slope of the curve and a significant

variation in the wavelet obtained in these same

regions. The rest of the wavelet signal preserves its

normal value, not showing significant variation in

the amplitude value.

We also performed classification process using

information of wavelet analysis to give support to

our method. We use 1-NN, Simple Naive Bayes and

Backpropagation as learning algorithms and 10-fold

cross validation as test mode. Classification results

are shown in Table 2.

Table 1: Mean and standard deviation values for normal

and abnormal regions in medical cases.

MC Normal Abnormal

1

83931.0

23316.0

=

=

σ

x

3664.5

8631.1

=

=

σ

x

2

73117.0

17536.0

=

=

σ

x

2666.4

365.1

=

=

σ

x

3

72382.0

12835.0

=

=

σ

x

6212.3

4254.1

=

=

σ

x

4

47121.0

1592.0

=

=

σ

x

84576.0

062167.0

=

=

σ

x

5

1207.1

33906.0

=

=

σ

x

5982.2

85605.0

=

=

σ

x

6

53559.0

09307.0

=

=

σ

x

8261.1

65914.0

=

=

σ

x

7

689.5

8197.1

=

=

σ

x

611.45

619.23

=

=

σ

x

8

7862.1

64328.0

=

=

σ

x

466.17

9494.5

=

=

σ

x

MULTIDIMENSIONAL WAVELET ANALYSIS FOR RECOGNITION OF LESIONS IN COLPOSCOPY TEST

353

Can be observed that features used in

classification process achieved in good results, even

when accuracy in classification is not a high value in

some cases. The F-measure is a popular combination

of precision and recall into a single parameter,

showing that classifications of lesions can be

identified with some precision. Although

classification gives good results, more features can

be used in order to increase accuracy in

identification of lesions.

Multidimensional wavelet analysis performed in

this work shows how useful is temporal information

for identification of lesions in cervix, enhancing our

previous work, and giving reliability to our method.

6 CONCLUSIONS

We have presented an approach about identification

of lesions in the cervix based on temporal texture

analysis. Results show the importance of temporal

analysis in identification of lesions and how this

information can be used in later analysis, in order to

enhance lesions detection process.

Wavelet analysis allows to process data at

different scales or resolutions. Main advantage over

traditional Fourier methods is that wavelet analyzes

physical situations where the signal contains

discontinuities and sharp spikes, highlighting

abnormalities found in temporal analysis of

colposcopy test information.

The wavelet-aggregated signal used in this work

allows to identify those regions in cervix where

lesions are present, complementing our previous

work and giving support to the approach presented

before. The main advantage of wavelet analysis is

that this technique supress local noise present in the

curve obtained from temporal analysis, preserving

just the variations corresponding to lesions in the

cervix surface.

Texture metrics results showed a good

correlation with the changes presented in the images.

Results show to be promising because there are

important differences between normal and abnormal

cases using a set of medical cases.

Direction for future work is to use not just

texture information but other parameters like three-

dimensional data as well as use another basis

function in wavelet analysis instead of just Haar

function.

ACKNOWLEDGEMENTS

First author wishes to thank CONACyT the support

for studies of masters in Computer Science under

scholarship number 189941.

REFERENCES

Baeg S., Kehtarnavaz N., 2002. Classification of Breast

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Barreto F.A., Altamirano R.L., Morales T.R.M., Cisneros

A.J.D., 2005. Identifying Precursory Cancer Lesions

Using Temporal Texture Analysis, Second Canadian

Conference on Computer and Robot Vision.

Burghardt E., 1991. Colposcopy-Cervical Pathology,

Textbook and Atlas, Thieme Germany.

Claude I., Pouletaut P., Huault S., Boulanger J-C, 2001.

Integrated Color and Texture Tools for Colposcopy

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Claude I., Winzenrieth R., Pouletaut P., Boulanger J-C,

2002. Contour Features for Colposcopic Image

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Lyubushin A.A., 1998. An Aggregated Signal of the Low-

Frequency Geophysical Monitoring Systems, Fiz.

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Hyperspectral Diagnostic Imaging of the Cervix:

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Imaging, American Journal of Obstetrics and

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Analysis for Discrimination of Cervical Neoplasia,

Journal of Biomedical Optics SPIE, Vol. 5 pp.72–82.

Tapia L.D., Barreto F.A., Altamirano R.L., 2006.

Identifying cervical cancer lesions using temporal

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VISAPP 2007 - International Conference on Computer Vision Theory and Applications

354

Region of interest Temporal texture analysis Wavelet-aggregated signal

Figure 4: Temporal information obtained with temporal texture analysis and wavelet-aggregated signal for every case.

Table 2: Classification results using several learning algorithms.

C ALGORITHMS

1-NN Naive Bayes Backpropagation

Accuracy (%) F-Measure (%) Accuracy (%) F-Measure (%) Accuracy (%) F-Measure (%)

1 73.33 77.8 73.33 77.8 73.33 77.8

2 75 85 83.33 88.9 75 85

3 75 83.9 73.684 81.5 70 81.3

4 56.25 72 62.5 70 43.75 60.9

5 50 61.5 70 76.9 30 34.8

6 65 78.8 80 84.6 80 87.5

7 75 85 87.5 90.9 75 85

8 70 80 83.33 87.2 80 87

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