153
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).
154
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.
156
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
157
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
Osteopenia (45) 18 0.021 0.004 II vs III 0.370
Osteoporosis (45) 14 0.020 0.002 III vs I 0.065
Normal (90) 20 0.838 0.024 I vs II 0.435
Osteopenia (90) 18 0.843 0.013 II vs III 0.284
Osteoporosis (90) 14 0.849 0.020 III vs I 0.153
Normal (135) 20 0.022 0.004 I vs II 0.850
Osteopenia (135) 18 0.022 0.006 II vs III 0.386
Osteoporosis (135) 14 0.024 0.006 III vs I 0.377
LRE
Normal (0) 20 97.26 17.30 I vs II 0.522
Osteopenia (0) 18 100.30 10.39 II vs III 0.028
Osteoporosis (0) 14 108.76 10.19 III vs I 0.033
Normal (45) 20 106.46 16.99 I vs II 0.683
Osteopenia (45) 18 104.21 16.65 II vs III 0.649
Osteoporosis (45) 14 106.82 14.97 III vs I 0.950
Normal (90) 20 2.39 0.38 I vs II 0.414
Osteopenia (90) 18 2.31 0.20 II vs III 0.834
Osteoporosis (90) 14 2.33 0.34 III vs I 0.625
Normal (135) 20 101.76 11.54 I vs II 0.404
Osteopenia (135) 18 98.16 14.69 II vs III 0.638
Osteoporosis (135) 14 101.16 19.69 III vs I 0.911
160
Table 6.1 (Continued)
Run-Length Parameters
Groups (angle) N Mean Std.
Deviation P-Value by Students
t-test
RLN
Normal (0) 20 108.708 22.515 I vs II 0.852
Osteopenia (0) 18 107.474 17.292 II vs III 0.278
Osteoporosis (0) 14 114.396 17.980 III vs I 0.438
Normal (45) 20 120.343 21.570 I vs II 0.965
Osteopenia (45) 18 120.627 17.835 II vs III 0.143
Osteoporosis (45) 14 133.606 30.592 III vs I 0.147
Normal (90) 20 1990.56 193.745 I vs II 0.443
Osteopenia (90) 18 2031.92 122.619 II vs III 0.368
Osteoporosis (90) 14 2082.59 189.988 III vs I 0.179
Normal (135) 20 111.337 11.388 I vs II 0.137
Osteopenia (135) 18 118.950 18.899 II vs III 0.803
Osteoporosis (135) 14 117.138 21.723 III vs I 0.317
RP
Normal (0) 20 15.851 0.772 I vs II 0.193
Osteopenia (0) 18 15.503 0.845 II vs III 0.088
Osteoporosis (0) 14 14.722 1.6231 III vs I 0.011
Normal (45) 20 17.375 1.042 I vs II 0.563
Osteopenia (45) 18 17.183 0.985 II vs III 0.589
Osteoporosis (45) 14 17.445 1.710 III vs I 0.884
Normal (90) 20 0.735 0.033 I vs II 0.409
Osteopenia (90) 18 0.742 0.019 II vs III 0.681
Osteoporosis (90) 14 0.746 0.031 III vs I 0.326
Normal (135) 20 16.475 0.825 I vs II 0.006
Osteopenia (135) 18 17.160 0.571 II vs III 0.020
Osteoporosis (135) 14 16.007 1.896 III vs I 0.333
161
Table 6.1 (Continued)
Run-Length Parameters
Groups (angle) N Mean Std.
Deviation P-Value by Students
t-test
GLN
Normal (0) 20 581.569 68.034 I vs II 0.326
Osteopenia (0) 18 558.977 71.917 II vs III 0.749
Osteoporosis (0) 14 548.361 113.739 III vs I 0.295
Normal (45) 20 737.159 106.594 I vs II 0.521
Osteopenia (45) 18 715.013 103.790 II vs III 0.403
Osteoporosis (45) 14 753.429 153.411 III vs I 0.717
Normal (90) 20 342.539 41.694 I vs II 0.340
Osteopenia (90) 18 330.968 30.574 II vs III 0.434
Osteoporosis (90) 14 342.226 49.376 III vs I 0.984
Normal (135) 20 638.523 79.345 I vs II 0.008
Osteopenia (135) 18 703.761 61.508 II vs III 0.017
Osteoporosis (135) 14 605.281 151.102 III vs I 0.409
LGRE
Normal (0) 20 0.823 0.062 I vs II 0.024
Osteopenia (0) 18 0.783 0.038 II vs III 0.293
Osteoporosis (0) 14 0.765 0.053 III vs I 0.009
Normal (45) 20 0.838 0.037 I vs II 0.546
Osteopenia (45) 18 0.830 0.037 II vs III 0.569
Osteoporosis (45) 14 0.840 0.059 III vs I 0.884
Normal (90) 20 0.020 0.004 I vs II 0.387
Osteopenia (90) 18 0.021 0.004 II vs III 0.635
Osteoporosis (90) 14 0.020 0.005 III vs I 0.819
Normal (135) 20 0.804 0.028 I vs II 0.009
Osteopenia (135) 18 0.827 0.021 II vs III 0.022
Osteoporosis (135) 14 0.785 0.070 III vs I 0.283
162
Table 6.1 (Continued)
Run-Length Parameters
Groups (angle) N Mean Std.
Deviation P-Value by Students
t-test
HGRE
Normal (0) 20 2.533 0.366 I vs II 0.462
Osteopenia (0) 18 2.619 0.343 II vs III 0.021
Osteoporosis (0) 14 3.354 1.221 III vs I 0.008
Normal (45) 20 1.987 0.375 I vs II 0.636
Osteopenia (45) 18 2.042 0.331 II vs III 0.963
Osteoporosis (45) 14 2.053 0.872 III vs I 0.766
Normal (90) 20 100.846 14.845 I vs II 0.823
Osteopenia (90) 18 101.943 15.177 II vs III 0.577
Osteoporosis (90) 14 105.357 19.126 III vs I 0.444
Normal (135) 20 2.266 0.311 I vs II 0.004
Osteopenia (135) 18 2.010 0.178 II vs III 0.033
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
Run-Length Parameters
Groups (angle) N Mean Std.
Deviation P-Value by
Students t-test
SRE1
Normal (0) 20 0.0248 0.007 I vs II 0.650
Osteopenia (0) 18 0.0258 0.005 II vs III 0.834
Osteoporosis (0) 15 0.0263 0.009 III vs I 0.588
Normal (45) 20 0.023 0.005 I vs II 0.371
Osteopenia (45) 18 0.025 0.005 II vs III 0.721
Osteoporosis (45) 15 0.024 0.008 III vs I 0.751
Normal (90) 20 0.838 0.027 I vs II 0.874
Osteopenia (90) 18 0.836 0.021 II vs III 0.744
Osteoporosis (90) 15 0.834 0.022 III vs I 0.668
Normal (135) 20 0.025 0.006 I vs II 0.384
Osteopenia (135) 18 0.028 0.008 II vs III 0.744
Osteoporosis (135) 15 0.026 0.014 III vs I 0.806
LRE1
Normal (0) 20 88.93 18.50 I vs II 0.078
Osteopenia (0) 18 99.87 18.60 II vs III 0.633
Osteoporosis (0) 15 102.76 15.13 III vs I 0.024
Normal (45) 20 94.399 14.7561 I vs II 0.616
Osteopenia (45) 18 91.825 16.637 II vs III 0.769
Osteoporosis (45) 15 93.963 24.567 III vs I 0.948
Normal (90) 20 2.461 0.364 I vs II 0.489
Osteopenia (90) 18 2.551 0.427 II vs III 0.881
Osteoporosis (90) 15 2.531 0.306 III vs I 0.553
Normal (135) 20 92.164 13.206 I vs II 0.985
Osteopenia (135) 18 92.285 25.064 II vs III 0.885
Osteoporosis (135) 15 93.569 25.146 III vs I 0.832
164
Table 6.2 (Continued)
Run-Length Parameters
Groups (angle) N Mean Std.
Deviation P-Value by
Students t-test
RLN1
Normal (0) 20 106.188 13.522 I vs II 0.874
Osteopenia (0) 18 106.767 7.562 II vs III 0.091
Osteoporosis (0) 15 115.275 19.030 III vs I 0.108
Normal (45) 20 119.056 12.803 I vs II 0.407
Osteopenia (45) 18 122.689 13.908 II vs III 0.762
Osteoporosis (45) 15 124.524 20.497 III vs I 0.339
Normal (90) 20 1985.320 239.605 I vs II 0.723
Osteopenia (90) 18 1959.605 199.039 II vs III 0.795
Osteoporosis (90) 15 1941.446 196.336 III vs I 0.567
Normal (135) 20 110.353 13.550 I vs II 0.597
Osteopenia (135) 18 112.688 13.367 II vs III 0.743
Osteoporosis (135) 15 114.310 14.777 III vs I 0.417
RP
Normal (0) 20 16.811 1.664 I vs II 0.253
Osteopenia (0) 18 16.333 0.553 II vs III 0.800
Osteoporosis (0) 15 15.850 0.956 III vs I 0.054
Normal (45) 20 17.864 0.690 I vs II 0.760
Osteopenia (45) 18 17.927 0.536 II vs III 0.775
Osteoporosis (45) 15 17.869 0.605 III vs I 0.983
Normal (90) 20 0.732 0.037 I vs II 0.619
Osteopenia (90) 18 0.726 0.035 II vs III 0.865
Osteoporosis (90) 15 0.724 0.030 III vs I 0.503
Normal (135) 20 16.839 0.507 I vs II 0.355
Osteopenia (135) 18 16.570 1.154 II vs III 0.855
Osteoporosis (135) 15 16.631 0.609 III vs I 0.279
165
Table 6.2 (Continued)
Run-Length Parameters
Groups (angle) N Mean Std.
Deviation P-Value by
Students t-test
GLN
Normal (0) 20 636.166 92.50 I vs II 0.694
Osteopenia (0) 18 626.057 59.08 II vs III 0.940
Osteoporosis (0) 15 581.407 88.53 III vs I 0.087
Normal (45) 20 786.310 73.857 I vs II 0.749
Osteopenia (45) 18 793.556 63.434 II vs III 0.731
Osteoporosis (45) 15 785.426 71.322 III vs I 0.972
Normal (90) 20 320.084 27.655 I vs II 0.803
Osteopenia (90) 18 317.367 38.572 II vs III 0.665
Osteoporosis (90) 15 311.743 40.107 III vs I 0.471
Normal (135) 20 676.593 54.542 I vs II 0.403
Osteopenia (135) 18 653.247 109.378 II vs III 0.998
Osteoporosis (135) 15 653.340 65.355 III vs I 0.260
LGRE Normal (0) 20 0.832 0.0938 I vs II 0.166
Osteopenia (0) 18 0.800 0.0236 II vs III 0.085
Osteoporosis (0) 15 0.781 0.0379 III vs I 0.054
Normal (45) 20 0.854 0.022 I vs II 0.725
Osteopenia (45) 18 0.857 0.019 II vs III 0.689
Osteoporosis (45) 15 0.854 0.021 III vs I 0.942
Normal (90) 20 0.022 0.006 I vs II 0.845
Osteopenia (90) 18 0.022 0.005 II vs III 0.766
Osteoporosis (90) 15 0.023 0.006 III vs I 0.635
Normal (135) 20 0.819 0.020 I vs II 0.300
Osteopenia (135) 18 0.808 0.040 II vs III 0.813
Osteoporosis (135) 15 0.810 0.023 III vs I 0.226
166
Table 6.2 (Continued)
Run-Length Parameters
Groups (angle) N Mean Std.
Deviation P-Value by
Students t-test
HGRE
Normal (0) 20 2.366 0.205 I vs II 0.419
Osteopenia (0) 18 2.314 0.185 II vs III 0.006
Osteoporosis (0) 15 2.633 0.413 III vs I 0.017
Normal (45) 20 1.821 0.219 I vs II 0.743
Osteopenia (45) 18 1.801 0.154 II vs III 0.863
Osteoporosis (45) 15 1.810 0.165 III vs I 0.873
Normal (90) 20 98.102 17.577 I vs II 0.916
Osteopenia (90) 18 97.484 18.326 II vs III 0.537
Osteoporosis (90) 15 93.417 18.995 III vs I 0.456
Normal (135) 20 2.134 0.165 I vs II 0.227
Osteopenia (135) 18 2.272 0.469 II vs III 0.589
Osteoporosis (135) 15 2.201 0.198 III vs I 0.288
167
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
168
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
169
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Figure 6.3 Discriminant plots of the run length parameters at all the
three ROIs in women
172
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Figure 6.4 Discriminant plots of the run length parameters at all the
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.
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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.