166
RESTORATION OF PRESSURE ULCER AREAS DETECTED BY
COMPUTATIONAL CLASSIFICATION THROUGH IMAGE INCLINATION
CORRECTION1
Inalda Leite Pereira 2, Luciene Carvalho de Sousa 2, Levy Aniceto Santana 3, Renato da Veiga
Guadagnin 4
BSc in Physical Therapy by the Catholic University of Brasilia – UCB,
inaldaleite@hotmail.com, lucyenn@hotmail.com
3 MSc in Health Sciences, Professor in the Catholic University of Brasilia – UCB, levy@ucb.br
4 Dr in Administration, Professor in the Catholic University of Brasilia – UCB,
renatov@ucb.br
2
Pressure ulcer (PU) is a tissue necrosis area that emerges when a soft tissue is
compressed. PU area analysis is useful to evaluate PU evolution and corresponding
responses to therapeutic procedures. Images from a Grade III PU were captured at a
distance of 30 cm with normal camera axis and camera axis with 20° and with 30°
inclination to the skin surface. PU areas were then obtained by classification using
Isoclust procedure from software IDRISI. Then correction coefficients for area
restoration from inclined plane images concerning x- and y-axis were computed and
applied to the classified images. So it was possible to adjust the areas obtained with
inclined camera axis and achieve new values that were very similar to the area obtained
with normal camera axis. So PU areas in such conditions can suitably be estimated.
Introduction1
Pressure Ulcer (PU) is a localized area of
tissue necrosis developed when a soft tissue is
compressed between an osseous prominence
and a hard surface. [1]
According to the “National Pressure Ulcer
Advisory Panel”, the prevalence of PU in USA
hospitals vary from 3% to 14%, increasing to
15% to 25% in rest homes. [2] In a study
carried out in a Brazilian university hospital,
percentages of presented PU cases found were
41.0 in the general intensive care unit, 39.5 in
the surgical ward and 42.6 in the general
practice ward. [3] The most afflicted areas are
the skin regions where there is a smaller
quantity of muscular tissue next to osseous
prominences, such as sacrum, large trochanter,
1
The computational tasks of this work were processed
in the Laboratory for Medical Image Processing in
Catholic University of Brasilia, supported by DAAD
(German Office for Academic Interchange).
scapula, lateral malleolus, thoracic column,
heels, occipital, knees, ischial tuberosities and
lateral epicondyles. [1] [4]
Continuous pressure on the skin, either for a
short period of time with higher intensity or
for a long period with lower intensity, leads to
ischemic phenomena associated to nutrient and
oxygen deficiencies, causing hypoxia, edema,
inflammation and cellular death. [4] [5] [6]
Pressure between 60 and 580 mmHg for a
period of 1 to 6 hours can cause an ulcer, and
shearing and friction forces can contribute to
its development mainly in patients with
sensitivity and consciousness alterations. [4]
The PUs can be classified according to depth,
in relation to the extension of the layer of
tissue involved, in grades from I to IV, been
that grade I manifests itself as a defined area
of persistent hyperemia, grade II as a partial
lesion which comprehends the epidermis, part
of the dermis or both, grade III as loss of
cutaneous total thickness involving subcutaneous tissue lesion or necrosis and grade
167
IV as the destruction of all the skin’s layers,
sub-cutaneous and muscular tissue. [6] [7]
Problem statement
The determination of width, length, depth and
tunnel formation completes and concludes the
medical classification of a wound. [6] For that,
measuring instruments have been developed to
meet the necessity of health services and
health professionals in order to follow
precisely the patient’s history, aiding in the
elaboration of an effective treatment plan. The
wound’s area can be obtained by simple but
less precise methods such as a ruler or tracing
on transparent material [8] [9], photographs or
by more precise methods such as computer
analysis of digital photographs. [7] [8] [9] [10]
Technological advances have allowed wider
access to digital cameras and to computer
systems due to higher availability and lower
costs. In this manner, digital photograph
analyses by computer systems have been
widely used for evaluation and following of
PUs. [8] [11]
In a recent work, distance and angle of the
camera were controlled to prevent probable
errors in the acquisition of the image. [11]
This research’s objective is to correct the
influence of camera’s position on computer
analysis of PU area by an adequate
computational classification procedure.
Approach and techniques
In 25/May/07, three digital photographs were
captured of a Grade III flat PU in the sacral
region of patient L.A., male, 60 years old,
afflicted by hemiplegia on the left side and
cerebral atrophy due to a cerebrovascular
accident which occurred approximately 20
years ago. The consent for taking the
photographs came from image usage
authorization given by a patient’s relative,
after been duly oriented as to the aims of the
study, with the signatures of two witnesses.
The images were taken using a digital camera
(Kodak, model Easyshare) with a two
megapixel resolution. To infer the area, a
reference object of 4 cm2 was placed in a nonafflicted area. The camera was positioned at a
distance of 30 cm from the PU with the axis at
0o, 20o and 30o inclination in relation to the
normal.
The images were captured in jpg format, 24 bit
pixel, and separated in three color bands
corresponding to the components of RGB
system. (Fig. 1)
Figure 1: Original image captured with 30o inclination
and 30 cm distance from the PU.
Initially the green band was chosen, which
adequately revealed the reference object, so
generating the image (a). After a few tests, the
blue band was chosen to enhance the PU area,
creating the image (b).
Following experimentation with various
supervised and non-supervised classification
procedures using the software IDRISI, the best
results were obtained with the non-supervised
classification procedure Isoclust. The images
were submitted to this classification procedure
for the identification of only two classes, that
is, reference object and background on image
(a) and PU, reference object and healthy skin
on image (b) (Fig. 2 and Fig 3). This
classification procedure is based on the Isodata
and K-means classification procedures,
consisting of an iterative process of class
attribution to all the pixels, ending with a predetermined number of iterations or when a
pre-determined maximum approximation is
reached. [12] [13]
As the image obtained by a photographic
camera is approximately a conicl projection of
the photographed object, in a plan
perpendicular to the camera’s axis, a distortion
of two dimentional images occurs, when they
168
are tilted in relation to the plan perpendicular
to the camera’s axis.
Figure 2: Image of the green band of the original
picture, highlighting the reference object, after Isoclust
classification: Image (a).
Figure 3: Image of the blue band, enhancing the PU and
the reference object, after Isoclust classification: Image
(b).
minus
(obtained area of the reference object)
The addition of correction factors for all the
pixels resulted in PU area calculations for the
different inclinations. Some correction values
can be seen on Table 1. The value variations in
the different columns are inferior to 10-4,
which cannot be seen in this table. The
tendencies of reduction of the correction
factors in the inferior lines in the image occur
due to the approximation of the inferior part of
the camera, with the axis pointing to the
central part of the PU.
Afterwards, the data were tabulated with the
conversion from number of pixels to area and
a histogram containing the calculated areas
was constructed (Fig. 4).
Table 1. Area correction factors obtained
with a 30 degrees camera tilt.
Row N.
0
88
177
266
355
Column number
0
84
169
253
338
1,4195
1,2893
1,1562
1,0231
0,8939
1,4195
1,2893
1,1562
1,0231
0,8939
1,4195
1,2893
1,1562
1,0231
0,8939
1,4195
1,2893
1,1562
1,0231
0,8939
1,4195
1,2893
1,1562
1,0231
0,8939
Results and Interpretation
Figure 4: Non-corrected and corrected PU areas.
UP areas from computational classification and
corrected areas
120,0
Area em sq cm
The camera rotational variation was
considered only in relation to the x-axis, as
this is the most probable variation in capturing
the image by one standing beside the patient’s
bed. The restoration of each pixel’s dimension
in the x direction can be obtained by triangular
similarity, since there are no rotations in
relation to other axis. The restoration in the y
direction must consider the correspondence
between the pixel’s size and dimension in the
y direction in the image system and its size in
the y direction in the object system. [14] [15]
Once the correction factors for each pixel in
the x and y directions were calculated, their
product was taken as the true area of each
pixel of the PU.
The following operation was performed:
(obtained area of the PU plus reference object)
According to figure 4 one can derive that,
raising the inclination of the camera, the PU
area becomes lower due to the distortion of
conic projection. The application of correction
factors transform these values in values very
similar to those obtained with the
perpendicular axis to the PU.
100,0
99,9
96,4 98,5
85,7
80,0
98,2
77,6
Classified areas
60,0
Corrected areas
40,0
20,0
0,0
0
20
30
Camera inclination in degrees
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Conclusion
The results showed that PU areas, computed
by application of classification procedure
Isoclust on images obtained with a camera
with a tilted axis in relation to the normal to a
PU, can be corrected by correction factors of a
conic projection of a flat object.
Considering the variations in texture and tones
of a PU, as well as its shape, this work shows
the adequacy of the Isoclust non-supervised
classification procedure to enhance the wound
and to estimate its area. It also demonstrates
the adequacy of application of area correction
factors when the image cannot be captured in
the ideal manner, that is, normal to the PU.
Images with such characteristics can arise
from lack of resources or due to patients’
adverse conditions. These findings can,
therefore, contribute to a broad utilization of
computerized PU area estimation by health
services at a lower cost.
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