17-03-2014, 10:27 PM
Abstract—Wound healing rate, remains an interesting and
important issue, in which modern imaging techniques have not
yet given a definitive answer. In order to guide better therapeutic
interventions, a better understanding of the fundamental
mechanisms driving tissue repair are required. The wound
healing rate is primarily quantified by the rate of change of the
wound’s surface area. The objective of this study was to establish
a standardised and objective technique to asses the progress of
wound healing in foot by means of texture analysis. The methods
of image pre-processing, segmentation and texture analysis
together with visual expert’s evaluation were used to assess the
wound healing process. A total of 40 digital images from ten
different subjects with food wounds were taken every third day,
for 12 days, by an inexpensive digital camera under variable
lighting conditions. The images were intensity normalized, and
wounds were automatic segmented using a snake’s segmentation
system. From the segmented wounds 15 different texture
characteristics and 4 different geometrical features were
extracted in order to identify features that quantify the rate of
wound healing. We found texture characteristics that may
indicate the progression of wound healing process. More
specifically, some texture features increase (mean, contrast),
while some other texture features decrease (entropy, sum of
squares variance, sum average, sum variance) with the
progression of the wound healing process. Some of these features
were found to be significantly different in a specific time point
and this could be used to indicate the rate of wound healing. No
significant differences were found for all geometrical measures.
The results of this study suggest that some texture features might
be used to monitor the wound healing process, thus reducing the
workload of experts, provide standardization, reduce costs, and
improve the quality for patients. The simplicity of the method
also suggests that it may be a valuable tool in clinical wound
evaluation. Future work will incorporate additional texture and
geometrical features for assessing the wound healing process in
order to be used in the real clinical praxis.
Keywords-Wound segmentation; texture analysis; wound
healing rate.
I. INTRODUCTION
Chronic wounds present an increasing health challenge as
the population ages and the incidence of different chronic
diseases grows worldwide [1]. The progress of wound healing
may be quantified by the rate of change of the wound’s surface
area [2]. However, this is a challenging task due to the
complexity of the wound, the variable lighting conditions, and
the time constraints in clinical laboratories. A color image of a
wound on foot is presented in Fig. 1a). One way to evaluate
wound healing rate is to monitor wound status by taking
images of the wound at regular patient visits (see Fig. 2). If the
physical dimensions of the wound are assessed at regular time
intervals, then the experts will know if the patient is responding
well or not to a particular treatment and if necessary change it
[2]. In 2012 the 22nd annual meeting of the Wound Healing
Society (WHS), set the standards for wound healing procedures
and proposed recommendations for evaluating the optimal
wound treatment [3].
There are not many research groups worldwide that are
involved in color image processing of wounds. In [2], the
authors proposed and evaluated an algorithm for the wound
segmentation with minimal manual input and a high accuracy,
which uses a combination of both RGB and L*a*b* color
spaces, as well as a combination of threshold and pixel-based
color comparing segmentation methods. Jones et al. [4], and
Jones [5], developed the MAVIS system, which is able to
automatically measure the dimensions of skin wounds. Their
method was based on color segmentation algorithms and was
able to segment an image into one of three tissue types: healthy
skin, wound tissue and epithelialisation tissue. Furthermore, six
measurement parameters: the R, G and B color planes, hue,
saturation and gray-level intensity were taken into
consideration. The R, G and B color planes were only
examined in isolation showing that straightforward
thresholding of color planes cannot produce a good
segmentation which distinguishes between wound and skin
tissues. They found that wound segmentation is only partially
succeeded, if only the 1D color histograms were taken into
consideration, while using a 3D RGB histogram space, the
color volume clusters may be more widely separated and a
better segmentation result can be achieved. Mekkes et al. [6],
made some progress with such the 3D RGB color histogram
clustering technique to asses the healing of wounds. It was
shown that clusters in RGB space for a given tissue type
formed an irregularly shaped 3D cloud, and so simple
thresholding along the R, G and B axes would not help to
segment the image into these three tissue types. Some other
researchers presented their techniques on the segmentation of
wounds in color mages based on the use of the black-yellowred
classification scheme to evaluate the debridement activity
of wounds [7].
important issue, in which modern imaging techniques have not
yet given a definitive answer. In order to guide better therapeutic
interventions, a better understanding of the fundamental
mechanisms driving tissue repair are required. The wound
healing rate is primarily quantified by the rate of change of the
wound’s surface area. The objective of this study was to establish
a standardised and objective technique to asses the progress of
wound healing in foot by means of texture analysis. The methods
of image pre-processing, segmentation and texture analysis
together with visual expert’s evaluation were used to assess the
wound healing process. A total of 40 digital images from ten
different subjects with food wounds were taken every third day,
for 12 days, by an inexpensive digital camera under variable
lighting conditions. The images were intensity normalized, and
wounds were automatic segmented using a snake’s segmentation
system. From the segmented wounds 15 different texture
characteristics and 4 different geometrical features were
extracted in order to identify features that quantify the rate of
wound healing. We found texture characteristics that may
indicate the progression of wound healing process. More
specifically, some texture features increase (mean, contrast),
while some other texture features decrease (entropy, sum of
squares variance, sum average, sum variance) with the
progression of the wound healing process. Some of these features
were found to be significantly different in a specific time point
and this could be used to indicate the rate of wound healing. No
significant differences were found for all geometrical measures.
The results of this study suggest that some texture features might
be used to monitor the wound healing process, thus reducing the
workload of experts, provide standardization, reduce costs, and
improve the quality for patients. The simplicity of the method
also suggests that it may be a valuable tool in clinical wound
evaluation. Future work will incorporate additional texture and
geometrical features for assessing the wound healing process in
order to be used in the real clinical praxis.
Keywords-Wound segmentation; texture analysis; wound
healing rate.
I. INTRODUCTION
Chronic wounds present an increasing health challenge as
the population ages and the incidence of different chronic
diseases grows worldwide [1]. The progress of wound healing
may be quantified by the rate of change of the wound’s surface
area [2]. However, this is a challenging task due to the
complexity of the wound, the variable lighting conditions, and
the time constraints in clinical laboratories. A color image of a
wound on foot is presented in Fig. 1a). One way to evaluate
wound healing rate is to monitor wound status by taking
images of the wound at regular patient visits (see Fig. 2). If the
physical dimensions of the wound are assessed at regular time
intervals, then the experts will know if the patient is responding
well or not to a particular treatment and if necessary change it
[2]. In 2012 the 22nd annual meeting of the Wound Healing
Society (WHS), set the standards for wound healing procedures
and proposed recommendations for evaluating the optimal
wound treatment [3].
There are not many research groups worldwide that are
involved in color image processing of wounds. In [2], the
authors proposed and evaluated an algorithm for the wound
segmentation with minimal manual input and a high accuracy,
which uses a combination of both RGB and L*a*b* color
spaces, as well as a combination of threshold and pixel-based
color comparing segmentation methods. Jones et al. [4], and
Jones [5], developed the MAVIS system, which is able to
automatically measure the dimensions of skin wounds. Their
method was based on color segmentation algorithms and was
able to segment an image into one of three tissue types: healthy
skin, wound tissue and epithelialisation tissue. Furthermore, six
measurement parameters: the R, G and B color planes, hue,
saturation and gray-level intensity were taken into
consideration. The R, G and B color planes were only
examined in isolation showing that straightforward
thresholding of color planes cannot produce a good
segmentation which distinguishes between wound and skin
tissues. They found that wound segmentation is only partially
succeeded, if only the 1D color histograms were taken into
consideration, while using a 3D RGB histogram space, the
color volume clusters may be more widely separated and a
better segmentation result can be achieved. Mekkes et al. [6],
made some progress with such the 3D RGB color histogram
clustering technique to asses the healing of wounds. It was
shown that clusters in RGB space for a given tissue type
formed an irregularly shaped 3D cloud, and so simple
thresholding along the R, G and B axes would not help to
segment the image into these three tissue types. Some other
researchers presented their techniques on the segmentation of
wounds in color mages based on the use of the black-yellowred
classification scheme to evaluate the debridement activity
of wounds [7].