CHAPTER 6 TEXTURE ANALYSIS OF THE CALCANEUM …shodhganga.inflibnet.ac.in/bitstream/10603/26944/11/11_chapter6.pdf6.4 RESULTS The mean and standard deviation of the Run-Length parameters

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CHAPTER 6

TEXTURE ANALYSIS OF THE CALCANEUM

RADIOGRAPHS BY THE HIGHER-ORDER STATISTICS:

THE GRAY LEVEL RUN-LENGTH MATRIX (GLRLM)

6.1 BACKGROUND

To assess bone strength precisely, the relationship between material

and structural properties of bone should be evaluated (Martin 1991; Einhorn

1996; Currey 2001). At present, in both research and clinical practice, the

most commonly used method to evaluate bone strength is dual energy x-ray

absorptiometry (DXA), particularly DXA-derived areal bone mineral density

(aBMD, g/cm2) (Marshall et al. 1996; Genant et al 1996; Genant 1998).

However, as aBMD represents a chunky measure of bone size and volumetric

density, it is difficult to interpret (Sievänen 2000a). It is also subject to

considerable patient-specific imprecision (Bolotin et al 2001), and may

mislead the diagnostic interpretations of bone brittleness (Bolotin and

Sievänen 2001). However, in most cases, DXA is the only method available.

Also, the operational guideline to diagnose osteoporosis by the World Health

Organisation (WHO) is based on aBMD as the outcome (Kanis et al 1994),

whereas the "microarchitectural deterioration" in the definition has not

achieved clinical application (Seeman 1997). Although decreased aBMD is

associated with increased risk of fracture at the populational level it cannot

identify those individuals who will eventually fracture (Marshall et al 1996).

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In recent years, texture analysis for 2D image data has been studied

extensively and many algorithms have been developed to deal with 2D texture

(statistical moments, Run-Length matrices, run-length encoding, spectral

measures, wavelets, etc.). We are currently exploring the advantages and

limitations of quantifying the texture information in X-ray images

classification based on its textural properties using Run-Length derived

parameters. In this chapter, we present our results on using run-length

statistics for 2D texture characterization.

6.2 MATERIAL AND METHODS

Standard lateral view of the calcaneum of 53 females (age ranged

from 35 to 79) and 52 males (age ranged from 35 to 73) were taken after

getting informed consent from the volunteers. Those with secondary bone

diseases (those who suffer from diabetes, hyperthyroidism, bone cancers,

fractures by severe trauma also patients with chronic liver and kidney

diseases, malignancy, malabsorption syndrome, inflammatory arthritis, hypo

and hyper thyroidism) were excluded from the study. Anthropometric

measurements such as height in meters and weight in kilograms were

measured for all the subjects and recorded. Both men and women were

divided into three groups based on the T-Score obtained by DXA

measurements as group I (normal), group II (osteopenic) and group III

(osteoporotic).

Group-I comprised 20 females and 20 males who were normal

(mean ± SD age: women = 44.5 ± 8.7 years and men = 56.4 ± 9.5 years).

Group-II comprised 18 females and 18 males who have osteopenia

(mean ± SD age: women = 55.0 ± 12.0 years and men = 63.9 ± 9.4 years).

155

Group-III comprised 15 females and 14 males patients, who have

osteoporosis (mean ± SD age: women =63.8 ± 8.7 years and men = 72.0 ± 4.9

years).

6.2.1 Measurements

The radiographs were digitized with a pixel size of 100 m,

resulting in a matrix of 2048 X 2600 pixels with a gray-scale resolution of 12

bits (Kodak Film Digitizer). (i.e., 4096 gray levels) and are stored as TIFF

files for analysis. Irfan-View software was used for viewing the images. The

region of interest was selected by cropping 64 x 64 pixels (2 X 2 cm) at three

regions of interest namely the thalamic group (ROI-1), Ward’s triangle (ROI-

2) and the region where the posterior compressive and tensile groups

intersects (ROI-3) as shown in the figure 6.1 and these ROIs were fed in the

algorithm developed in Matlab6.1 software. The algorithm was developed to

compute seven texture parameters namely Short Run Emphasis (SRE), Long

Run Emphasis(LRE), Run-Length Non-Uniformity (RLN), Run Percentage

(RP), Gray Level Non-Uniformity (GLN), Low Gray Level

Run-Emphasis (LGRE) and High Gray Level Run-Emphasis (HGRE) on

Run-Length matrix as discussed in the introduction.

6.2.2 Image Acquisition

From the digitised radiographs 64 x 64 pixels are cropped at three

regions of interest (ROI) as shown in the Figure 6.1. The ROIs are selected

manually at three regions. The minimum intensity is subtracted from the ROI

and normalized. Then the normalized images were quantised to 16 gray levels

i.e., at 4 bits/pixel.

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Figure 6.1 ROIs at the calcaneum posterior compressive groups

(Thalamic-ROI-1), Wards (ROI-2) and at the intersection of

anterior compressive and tensile groups of trabecular

network (ROI-3)

6.2.3 Reproducibility

The precision of the selection of the region of interest from the

same radiograph was estimated by repeatedly cropping the ROI of 64 X 64

pixels for twenty five times at different intervals. The protocol was repeated

for 20 individuals. The co-efficient of variation (CV %) for the GLRLM

matrix based texture parameters were found to be 0.95, 0.72, 0.88, 0.61, 0.74,

0.92, and 0.87 for Short Run Emphasis (SRE), Long Run Emphasis(LRE),

Run-Length Non-Uniformity (RLN), Run Percentage (RP), Gray Level

Non-Uniformity (GLN), Low Gray Level Run-Emphasis (LGRE) and High

Gray Level Run-Emphasis (HGRE) respectively.

ROI-1

ROI-2 ROI-3

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6.3 RUN-LENGTH MATRIX

Run-Length Matrix represents frequency of runs of defined length

and gray tones. A run-length matrix P is defined as follows: each element

P(i, j) represents the number of runs with pixels of gray level intensity equal

to i and length of run equal to j along a specific orientation. The size of the

matrix P is n by k, where n is the maximum gray level in the ROI cropped

from the X-ray image and k is equal to the possible maximum run length in

the corresponding image. An orientation is defined using a displacement

vector d(x, y), where x and y are the displacements for the x-axis and y-axis,

respectively. The typical orientations are 0°, 45°, 90°, and 135°, and

calculating the run-length encoding for each direction will produce four

run-length matrices. The 256-gray-level images were mapped to 16 gray

levels (quantized) to speed up computing time. Consecutive pixels of the

same gray value in a given direction constitute a run. In the present study, the

horizontal (0), vertical (90) diagonal (45 and 135) directions were

measured separately. For each direction, a two-dimensional matrix was

computed where p(i, j) represents the number of runs of length j and gray

level i.

The Run-Length matrix was computed for four orientations 0, 45,

90 and 135 and 1, 2 and 3 pixel separations (Figure 6.2) for three ROIs in

men and women. Once the run-length matrices are calculated along each

direction, several texture descriptors are calculated to capture the texture

properties and differentiate among different textures. Some of these

descriptors reflect specific characteristics in the image. For example, SRE

measures the distribution of short runs in an image, while run percentage

measures both the homogeneity and the distribution of runs of an image in a

specific direction.

158

135 90 45

0

Figure 6.2 ROI (64 X 64) pixels with 0 , 45, 90 and 135 orientations

6.3.1 Statistical Analysis

The mean SD values of the fractal measures were calculated for

each group. One way ANOVA was used to investigate whether these values

were different between the groups. Linear regression analysis was used to

study the dependence of fractal measurements on subject age and bone

mineral density. Box plots, discriminant plots and error bars were plotted to

represent the data for enhanced understanding. All data were analysed using

the Statistical Package for Social Sciences (SPSS) software package.

6.4 RESULTS

The mean and standard deviation of the Run-Length parameters at

four orientations at 3 pixel separation at ROI-3 in men and in women are

tabulated (Tables 6.1 and 6.2). The correlation co-efficient between run-

length texture parameters and BMD in the case of men and women are given

in the table 6.3 and 6.4. The Discriminant plots for different regions at four

orientations are plotted for men and women (Figure 6.3 and 6.4). The

Discriminant scores for men and women are tabulated in table 6.5

159

Table 6.1 The mean and standard deviation of the Run-Length

parameters at four orientations at 3 pixel separation at ROI-3 in men

Run-Length Parameters

Groups (angle) N Mean Std.

Deviation P-Value by

Students t-test

SRE

Normal (0) 20 0.028 0.008 I vs II 0.023

Osteopenia (0) 18 0.022 0.007 II vs III 0.403

Osteoporosis (0) 14 0.020 0.008 III vs I 0.006

Normal (45) 20 0.023 0.005 I vs II 0.353



Normal (90) 20 0.838 0.024 I vs II 0.435



Normal (135) 20 0.022 0.004 I vs II 0.850



LRE

Normal (0) 20 97.26 17.30 I vs II 0.522



Normal (45) 20 106.46 16.99 I vs II 0.683



Normal (90) 20 2.39 0.38 I vs II 0.414



Normal (135) 20 101.76 11.54 I vs II 0.404



160

Table 6.1 (Continued)



Deviation P-Value by Students

t-test

RLN

Normal (0) 20 108.708 22.515 I vs II 0.852



Normal (45) 20 120.343 21.570 I vs II 0.965



Normal (90) 20 1990.56 193.745 I vs II 0.443



Normal (135) 20 111.337 11.388 I vs II 0.137



RP

Normal (0) 20 15.851 0.772 I vs II 0.193



Normal (45) 20 17.375 1.042 I vs II 0.563



Normal (90) 20 0.735 0.033 I vs II 0.409



Normal (135) 20 16.475 0.825 I vs II 0.006



161





t-test

GLN

Normal (0) 20 581.569 68.034 I vs II 0.326



Normal (45) 20 737.159 106.594 I vs II 0.521



Normal (90) 20 342.539 41.694 I vs II 0.340



Normal (135) 20 638.523 79.345 I vs II 0.008



LGRE

Normal (0) 20 0.823 0.062 I vs II 0.024



Normal (45) 20 0.838 0.037 I vs II 0.546



Normal (90) 20 0.020 0.004 I vs II 0.387



Normal (135) 20 0.804 0.028 I vs II 0.009



162





t-test

HGRE

Normal (0) 20 2.533 0.366 I vs II 0.462



Normal (45) 20 1.987 0.375 I vs II 0.636



Normal (90) 20 100.846 14.845 I vs II 0.823



Normal (135) 20 2.266 0.311 I vs II 0.004


Osteoporosis(135) 14 2.603 1.110 III vs I 0.205

163

Table 6.2 The mean and standard deviation of the Run-Length

parameters at four orientations at 3 pixel separation at

ROI-1 in women




Students t-test

SRE1

Normal (0) 20 0.0248 0.007 I vs II 0.650



Normal (45) 20 0.023 0.005 I vs II 0.371



Normal (90) 20 0.838 0.027 I vs II 0.874



Normal (135) 20 0.025 0.006 I vs II 0.384



LRE1

Normal (0) 20 88.93 18.50 I vs II 0.078



Normal (45) 20 94.399 14.7561 I vs II 0.616



Normal (90) 20 2.461 0.364 I vs II 0.489



Normal (135) 20 92.164 13.206 I vs II 0.985



164





Students t-test

RLN1

Normal (0) 20 106.188 13.522 I vs II 0.874



Normal (45) 20 119.056 12.803 I vs II 0.407



Normal (90) 20 1985.320 239.605 I vs II 0.723



Normal (135) 20 110.353 13.550 I vs II 0.597



RP

Normal (0) 20 16.811 1.664 I vs II 0.253



Normal (45) 20 17.864 0.690 I vs II 0.760



Normal (90) 20 0.732 0.037 I vs II 0.619



Normal (135) 20 16.839 0.507 I vs II 0.355



165





Students t-test

GLN

Normal (0) 20 636.166 92.50 I vs II 0.694



Normal (45) 20 786.310 73.857 I vs II 0.749



Normal (90) 20 320.084 27.655 I vs II 0.803



Normal (135) 20 676.593 54.542 I vs II 0.403



LGRE Normal (0) 20 0.832 0.0938 I vs II 0.166



Normal (45) 20 0.854 0.022 I vs II 0.725



Normal (90) 20 0.022 0.006 I vs II 0.845



Normal (135) 20 0.819 0.020 I vs II 0.300



166





Students t-test

HGRE

Normal (0) 20 2.366 0.205 I vs II 0.419



Normal (45) 20 1.821 0.219 I vs II 0.743



Normal (90) 20 98.102 17.577 I vs II 0.916



Normal (135) 20 2.134 0.165 I vs II 0.227



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Table 6.3 Correlation between Run-Length texture parameters

measured at the calcaneum and BMD at hip in men

Texture

Parameters TBMD FNBMD

Trochantar

BMD

Inter-

Troch

BMD

Ward’s

BMD Age

SRE r

P

0.242

0.042

0.144

0.155

0.258

0.032

0.219

0.059

0.339

0.007

-0.098

0.245

LRE r

P

-0.144

0.155

-0.076

0.297

-0.222

0.057

-0.113

0.212

-0.251

0.037

0.003

0.491

RLN r

P

-0.066

0.322

0.063

0.329

-0.158

0.131

-0.045

0.376

-0.040

0.390

-0.028

0.421

RP r

P

0.370

0.003

0.164

0.123

0.357

0.005

0.364

0.004

0.303

0.015

-0.242

0.042

GLN r

P

0.150

0.144

-0.034

0.406

0.172

0.112

0.148

0.147

0.106

0.227

-0.065

0.323

LGRE r

P

0.372

0.003

0.273

0.025

0.349

0.006

0.360

0.004

0.385

0.002

-0.421

0.001

HGRE r

P

-0.384

0.002

-0.243

0.038

-0.361

0.004

-0.381

0.003

-0.357

0.005

0.264

0.029

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Table 6.4 Correlation between Run-Length texture parameters

measured at the calcaneum and BMD at hip in women

Texture Parameters

TBMD FNBMD Trochanta

r BMD Inter-Troch

BMD Ward’s BMD

Age

SRE

r

P

-0.092

0.257

-0.025

0.430

-0.133

0.172

-0.078

0.289

-0.121

0.194

0.178

0.102

LRE

r

P

-0.289

0.018

-0.328

0.008

-0.298

0.015

-0.309

0.012

-0.289

0.018

0.157

0.131

RLN

r

P

-0.096

0.247

-0.242

0.041

-0.179

0.100

-0.123

0.190

-0.083

0.278

0.321

0.010

RP

r

P

0.303

0.014

0.264

0.028

0.194

0.082

0.313

0.011

0.315

0.011

-0.111

0.215

GLN

r

P

0.210

0.066

0.285

0.019

0.096

0.248

0.228

0.050

0.320

0.010

-0.243

0.040

LGRE

r

P

0.230

0.049

0.245

0.038

0.152

0.139

0.235

0.045

0.154

0.135

-0.345

0.006

HGRE

r

P

-0.275

0.023

-0.413

0.001

-0.204

0.071

-0.323

0.008

-0.345

0.006

0.253

0.034

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three ROIs in women

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three ROIs in men

175

Table 6.5 Discriminant Analysis by Run-Length Matrix for Women

ROI Computed direction in degrees

Normal

N=20

Osteopenia

N=18

Osteoporosis

N=15

% Correctly Classified

ROI-1

0 11 10 8 54.7

45 12 8 5 47.2

90 10 7 9 49.1

135 13 9 6 52.8

ROI-2

0 15 6 7 52.8

45 8 10 10 52.8

90 9 8 6 43.4

135 6 12 6 45.3

ROI-3

0 9 10 8 50.9

45 7 11 2 47.2

90 18 9 9 67.9*

135 12 7 10 54.7

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Table 6.6 Discriminant Analysis by Run-Length Matrix for Men

ROI Computed direction

in degrees

Normal

N=20

Osteopenia

N=18

Osteoporosis

N=15

% Correctly Classified

ROI-1

0 16 3 10 55.8

45 8 9 10 51.9

90 12 9 8 56.9

135 13 14 4 59.6

ROI-2

0 10 10 7 51.9

45 15 10 7 61.5

90 12 10 7 55.8

135 13 10 4 51.9

ROI-3

0 7 11 7 48.1

45 10 8 4 42.3

90 14 11 8 63.5*

135 10 9 9 53.8

* Highest Score attained at ROI-3 when Run-Length Matrix computed at

90 orientation

6.5 DISCUSSIONS

Run-length statistics capture the coarseness of a texture in specified

directions. A run is defined as a string of consecutive pixels which have the

same gray level intensity along a specific linear orientation. Fine textures tend

to contain more short runs with similar gray level intensities, while coarse

textures have more long runs with significantly different gray level intensities.

177

Texture analysis techniques are based either on mathematical morphology or

on fractal geometry.

The seven run-length parameters computed at three pixel separation

adapted in this study shows a significant decrease or increase in the value

with respect to bone mineral density depletion. SRE measures the distribution

of short runs. The SRE is highly dependent on the occurrence of short runs

and is expected large for fine textures, in this study, similar large values for

the normal group which is a fine texture compared to the osteoporotic group

which is a coarse texture was observed. LRE measures distribution of long

runs. The LRE is highly dependent on the occurrence of long runs and is

expected large for coarse structural textures is also observed in our study

(Table-6.1 and 6.2).

RLN measures the similarity of the length of runs through out the

image. The RLN is expected small if the run lengths are alike through out the

image. The RLN is high for normal subjects than osteoporotic in this study.

RP Measures the homogeneity and the distribution of runs of an image in a

specific direction. The RP is the largest when the length of runs is 1 for all

gray levels in specific direction. The RP in normal group in this study is

significantly high when compared to osteoporotic group at = 90 degree.

GLN measures the similarity of gray level values throughout the image. The

GLN is expected small if the gray level values are alike throughout the image.

The osteoporotic group has smaller values of these parameters which show

the gray level values are alike at ROI-3.

LGRE measures the distribution of low gray level values. The

LGRE is expected large for the image with low gray level values. It is large

for normal group than osteoporotic group in this study which shows that the

ROIs cropped from the images of the normal x-rays has got lower valued

178

intensity pixels. HGRE measures the distribution of high gray level values.

The HGRE is expected large for the image with high gray level values. This

parameter is also found to be higher in its value for normal than osteoporotic

group in this study.

DXA is a nondestructive method for the samples but it works on

projected images combining trabecular and cortical bone. Ash-weight

measurement of bone samples is a destructive technique that provides similar

results that DXA: the correlation coefficient is usually very high close to 0.99

(D. Chappard et al 2001). In this study, the run-length matrix computed at 90

degree in the ROI-3 gave higher discriminant score of 56.6 % correct

classification and 63.5 % respectively for women and men.

Texture is usually defined as “a global pattern arising from the

repetition, either deterministically or randomly, of local sub-pattern”. Sub

patterns are sometimes referred to as “primitive elements” in the image

analysis literature (e.g., a tile is a primitive in a mosaic). A large body of the

image analysis literature is concerned with texture analysis. Several papers

have reported the use of texture analysis to estimate the trabecular architecture

on X-ray images but a single method is usually used.

In a series of fractured and non-fractured osteoporotic patients, the

ability of the skyscraper analysis of X-rays to separate both groups was

evaluated using receiver operating characteristic (ROC) analysis and was

found superior to ROC analysis of lumbar BMD. In this study, the trabecular

architecture appeared rather well defined on the X-ray images provided by the

CCD camera. DXA measurement is usually considered to be the “gold

standard” to appreciate bone loss in clinical practice. From this study it is

evident that texture analysis may help in the assessment of trabecular

architecture.

179

6.5.1 Conclusions

Run-length matrix computed at 90 degree in the ROI-3 gave higher

discriminant score of correct classification with a percentage of 56.6% and

63.5% for women and men respectively. All the run length parameters

presented in this chapter were found to be most relevant when the Gray Level

Run-Length Matrix (GLRLM) computed at 90 orientation at ROI-3.

Selection of some more regions of interest at the posterior group of trabecular

network may also yield discriminating values like this which has to be still

explored. Texture analysis of X-ray images can reveal differences that were

not evidenced by naked eyes. However, a combination of several methods

appears necessary to appreciate the bone loss. From this we can conclude that

texture analysis of X-ray radiographs by higher order statistics is able to

detect architectural differences in the trabecular architecture with respect to

bone loss detected by DXA even when the regions of analysis of bone

structure and BMD are different.

Documents

CHAPTER 6 TEXTURE ANALYSIS OF THE CALCANEUM …shodhganga.inflibnet.ac.in/bitstream/10603/26944/11/11_chapter6.pdf6.4 RESULTS The mean and standard deviation of the Run-Length parameters