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MVA'98 IAPR Workshop on Machine Vision Applications, Nov. 17-19, 1998. Makuhari, Chiba, Japan

8-1

Automatic Judgement of Spinal Deformity from Moire Images

Employing Asymmetry of Local Centroids Location

HyongSeop Kim

Kazufumi Ishida t , Seiji Ishikawa ,

Yoshinori Ohtsuka $

t Department of Mechanical and Control Engineering

Kyushu Institute of Technology

$ National Sanatorium Chiba Higashi Hospital

Abstract

A technique is described for classifying abnormal

cases and normal cases in automatic spinal deformity

analysis by computer based on moire topographic

images of human backs. Displacement of local centroids

is evaluated statistically between the left-hand side and

the right-hand side of the moire images. The technique

was applied to real subjects images in order to draw a

distinction between 60 normal and 60 abnormal cases.

According to the leave-out method, the entire data was

separated into three sets. The linear discriminant

function based on the Mahalanobis distance was defined

on the 2-D feature space employing one of the data sets

containing 40 moire images and classified 80 images in

the remaining two sets. The average classification rate

was 87.9%.

1 Introduction

Spinal deformity is a serious disease for teenagers

and screening has been performed at primary schools

and secondary schools in Kanto, Japan, in order to

realize early check of the illness. For the screening,

moire topographic images of human backs are

conveniently employed, since 3-D asymmetry of a back

caused by spinal deformity is easily observed as an

asymmetric 2-D moire pattern. In practice, orthopaedists

'f Address: Sensuicho 1-1. Tobata, Kitakyushu 804-8550. Japan.

Enuil: kimhs@is.cntl.kyutech.ac.jp, ishikawaBis.cntl.kyutech.ac.jp

$Address: Nitonacho 673, Chuou. Chiba 260-0801, Japan.

inspect spinal deformity visually by the moire images

before they proceed to X-ray check.

This inspection is tough work on account of a large

number of moire images yielded by the screening at

schools and automating the inspection by computer

image processing has strongly been requested by

doctors. Since moire imaging is much safer to a subject

than X-ray imaging and it is also easily obtainable by a

moire camera which is commercially available, every

researcher tries to analyze a moire image of a human

back in order to realize automatic diagnosis of spinal

deformity by computer. Reported techniques to date[l-

31 perform 3-D recovery of the undulation of the back

and try to evaluate its geometrical asymmetry. The

result is that they all suffer from their complicated

image processing techniques and impractical processing

time. Nothing worth discussing has been achieved for

the automatic diagnosis of spinal deformity to date.

Unlike these techniques based on 3-D geometric

analysis, the technique proposed in this paper simulates

doctors' 2-D visual asymmetry inspection on a human

moire image. The technique realizes simpler analysis of

the moire image and therefore achieves much shorter

processing time than ever. Asymmetry of the moire

image of a human back is quantified by examining

displacement of local centroids defined on the moire

image. The technique is evaluated its performance

experimentally by real subjects' moire images. Finally

the result is discussed.

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lr -1r -1r

2 Asymmetry Representation by Local

Centroids Displacement

Gj (x, ,yl ).

deviation a

employed for representing asymmetry of the moire

image in R.

The moire images employed are separated into two

groups, i.e., the training set S?(I, l k=1,2,. . ., M,} and

the test set S~(I,, I k=1,2 ,..., Ms}. The feature vectors

( p ,, a t ) obtained from I, E S, are plotted on the 2-D

feature space and the linear discriminant function L is

defined on it employing the Mahalanobis distance. The

images in S, are classified by the line L into normal

cases and abnormal cases of spinal deformity on the

feature space. The leave-out method is employed in the

classification to exclude biased data sampling.

The mean

of the values 0,

F(

and the standard

(j=1,2, ..., N) are

2.1 Extracting the area of interest

Let us denote a moire image of a human back by I(x,

y). The origin 0 of the xy-coordinate system is located

at the lower left corner of the image. The ranges of the

coordinates are O l x l x , and OSySy,. The middle line

is defined in the first place on I(x, y). Since the moire

pattern of a human back usually exhibits asymmetry, a

potential symmetry axis[4] is extracted from I(x, y) and

the axis is regarded as the middle line of the back. Let

the middle line be located at x=m.

The area of interest denoted by R is defined in the

second place on I(x, 3 in the following way. Image I(x,

y) is binarized and histogram of the binarized pixels

onto the x-axis is calculated. The locations having the

minimum frequency value are found within O l x l m

and mlx&x,, and two such locations, x=xo and x=x,,

that are the nearest to the middle line are chosen from

the respective ranges. The area of interest R is defined

as the inside area of I(x, y) discriminated by the two

vertical lines x=xo and x=x,. The area R excludes arms

of the subject and takes subject's physical dimensions

into account. See Fig.1 for the procedure, where (a) is a

binarized moire image, (b) histogram of the pixels

(doubled in the direction of the y-axis for easier

observation) and the discriminating two lines, and (c)

the area of interest R.

2.2

centroids

Evaluating asymmetry by local

Within the area of interest R, as shown in Fig.2, two

square regions are defined at symmetric locations with

respect to the middle line x=m. Length of its side a is

defined by a= min(x,-m, 111-x,]. Let us denote the square

regions of the left-hand side and the right-hand side at

I

y=j by Rj and R; , respectively. Here j=1,2,. . ., N.

The center of gravity (or the centroid) of R! and R;

are denoted by G

respectively. The centroid G: (Y: , 7: ) is reflected with

respect to the middle line x=rn into the region Ri and

denoted by G; ( x : , l; ) . The Euclid distance denoted

by Dl

is calculated between

- r - r

) and Gl(x,, yl ) ,

G;(Y,', y,')

and

(a)

Fig.1. Extracting the area of interest: (a) Binarized

moire image; (b) histogram on the x-axis and two

discriminating lines; and (c) the area of interest R.

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Fig.2. Area of interest R on the subject's back and the regions for specifying the centroids.

(a)

(b)

Fig.3. Examples of local centroid locations on the moire images: (a) A normal case; and (b) an abnormal case.

244

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Table 1. Obtained classification rates.

3 Experimental Result

The moire images employed in the experiment

contain 60 normal cases and 60 abnormal cases. They

are respectively separated into three sets each of which

contains 20 images and denoted by S,, for normal and

S,, for abnormal (i=1,2,3). The set S, is defined by S,=

S,, U S,, . Chosen S, as a training set, i.e., S+,,

S, ti, k#i) are used as a test set. The area of interest R is

defined at 19 individual positions which are mutually 10

pixels apart vertically. Employed parameters are

therefore as follows: N=19, M,=40, M ~ 8 0 .

Experimental result is shown in Table 1, where

classification rates are given by percentage. Examples

of local centroids location are depicted in Fig.3. The

processing time of a single moire image is 12 seconds in

average by Sparc Station 20.

S,=S,U

4 Discussion

By the experiment employing real moire images of

human backs, 87.9% of classification rate was achieved

in average. To the best of our knowledge, this is the first

experimental report employing real as well as

substantial image data. The paper therefore holds its

significance in this respect. Although the achieved

classification rate is not yet enough to put the proposed

technique into practice, the result makes us expect

future automation of spinal deformity inspection by

computer.

One of the advantages of the present technique is that

feature extraction is simple and therefore computation

time is shorter than any other techniques reported to

date. The computation time can be improved further by

the employment of the latest PC.

On the other hand, the present technique is sensitive

to the location of the middle line, since the local

centroid of the left-hand side square region is reflected

with respect to the middle line in order to calculate the

displacement with the local centroid of the right-hand

side square region. Larger asymmetry in the moire

image might result in inexact position of the middle line.

On this issue, we are planning to settle the middle line

visually on the stage of taking a subject's moire image.

This may contribute to obtaining better results in the

experiment.

Nine abnormal cases out of 60 were classified as

normal. This is a vital situation compared with the

reverse case that normal cases are classified as abnormal.

All of the nine cases are obviously visible of the

asymmetry of moire patterns and yet extracted local

centroids spread on the image almost in a symmetric

way. This may be because gray values distribution in the

square regions unfortunately operated symmetrically

when the centroids were calculated there. The size and

the location of the square regions need be elaborated

experimentally.

5 Conclusion

A technique was presented for analyzing spinal

deformity by computer based on moire images of

human backs. Asymmetry of local centroids were

statistically evaluated. The technique was examined by

120 real moire images and 87.9% of the classification

rate was achieved. The technique needs further

improvement to obtain higher classification rates.

References

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method and automatic

shapes" , Appl. Opt., 16,2152 (1977).

[2] Barouche, M.: "A knowledge based system for

diagnosing spinal deformations: Moire pattern

analysis and interpretation" , Proc. 11 Int. Conf.

Pattern Recogn., 591 -594 (1 992).

[3] Ishikawa, S., Takagami, S., Kato, K., Otsuka, Y.

"Analyzing deformity of human backs based on

the 3-D topographic reconstruction from moire

images" , Proc. '95 Korea Automat. Control Conf.,

244-247 (1 995).

[4] Ishikawa, S., Kosaka, H., Kato,K., Otsuka,Y.: "A

method of analyzing a shape with potential

symmetry and its application to detecting spinal

deformity" , Comput. Vision, Virtual Reality,

Robotics in Med., 465-470, Springer (1995).

measurement of 3-D