Testing an Image Mining Approach to Obtain Pressure Ulcers Stage

and Texture

Renato V. Guadagnin

, Levy Aniceto Santana

and Rinaldo de Souza Neves

MS Program on Information Technology and Knowledge Management, Catholic University of Brasília, Brasília, Brazil

Physical Therapy Program, Catholic University of Brasília, Taguatinga, DF, Brazil

Head of Scientific Starting Program – Pibic, Health Sciences Graduate School, Brasília, DF, Brazil

Keywords: Pressure Ulcer, Diagnosis, Image Mining, Colour, Texture.

Abstract: Improvement of pressure ulcers (PU) images analysis through computerized techniques is advantageous

both to medical assistance institutions and to patients’ life quality. The scientific challenge is to improve

assistance to patients with PU by means of reliable image analysis procedures. Diagnosis of stage and

predominant texture in a PU is essentially an image colour classification problem that can use existing

knowledge. This study performs a classification of pressure ulcers images through an algorithm based on

ID3 to construct a decision tree that has RGB statistics as input features and PU stage and texture as target

features. A decision tree is constructed first by classification of 18 images of a training set. Then this tree is

tested in a set of 45 PU images. Acceptable classification accuracy for training sets was not confirmed in

test set.

1 INTRODUCTION

Improvement of PU images analysis through

computerized techniques is advantageous both to

improve medical assistance institutions and to

increase life quality for patients. The clinicians

engaged in this area have to follow up a large

amount of patients that often have several pressure

ulcers. They have to actualize the corresponding

data registration based on new PU images that they

visually capture. Their work could be substantially

improved if they received a previous PU diagnostic

that were automatically generated. Eventually a new

visual image capture is not necessary and his work

becomes more productive. Therefore, the scientific

challenge is to improve assistance to patients with

PU by means of reliable image analysis procedures.

Diagnosis of stage and texture in a PU is

essentially an image colour classification problem

that can use existing knowledge.

A technique for automatic evaluation of texture

and stage, based on colour, to support treatment of

patients with PU, was presented in (Guadagnin,

2014). A training set of images features was used.

Present article shows the results of utilization of

such image classification technique to a PU images

test set.

The Background describes a study in present

theme and the main steps of the adopted

classification approach. The Technique details such

steps form image capture up to obtaining

classification results. The Results reports the

classification quality parameters from both PU stage

and PU texture computerized procedures. Some

comments about initial purpose and achieved results

are in Conclusion.

2 BACKGROUND

A classification based on colour and tissue structure

has been performed on wound images analysis

through neural networks and Bayesian classifiers in

a more extensive study. The set of colour and texture

features concern colour models L*u*v, RGB, and

normalized-RGB. Texture features included wavelet

filters too. 63 descriptors were reduced to 19 using

PCA, in order to reduce the dimensionality. The

authors concluded that the technique is appropriate

to obtain uniform and well-contrasted regions.

However PU image peculiarities implies the use of

manually delineated ground-truth images as

inappropriate. It is suggested a more precise

estimation of the approach to compare the results

V. Guadagnin R., Aniceto Santana L. and de Souza Neves R..

Testing an Image Mining Approach to Obtain Pressure Ulcers Stage and Texture.

DOI: 10.5220/0005457500220028

In Proceedings of the 5th International Workshop on Image Mining. Theory and Applications (IMTA-5-2015), pages 22-28

ISBN: 978-989-758-094-9

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

with those from clinicians (Veredas, 2010).

Present study performs image mining with less

image features using software Weka (Waikato

Environment for Knowledge Analysis) (Witten,

1999). Weka is a free open source software, for data

mining. PU attributes are colour, stage and tissue,

which are determined by a healthcare technician.

Each PU image can be expressed through three

stacks in RGB model. It is possible to calculate

statistical indicators for each stack concerning

primary colour space, such as mean, standard

deviation and mode. Software ImageJ was used for

such purpose (Ferreira, 2012).

Thus, one can first classify the images in a

training set and construct a decision tree. Latter the

so achieved results can be checked in a test set. The

quality of classification process is evaluated this way

(Soukup, 2002).

3 TECHNIQUE

PU image sets for classification were obtained as

follows.

By a non-probabilistic way, a group of nine

individuals of both sexes, aged between 18 and 80

years, with PU, who formally authorized their

participation in the study, was defined to generate

training set.

A Canon superzoom camera, model PowerShot

SX 20 IS, with a resolution of 12.1 megapixels was

used. The camera axis was positioned perpendicular

to the PU plane. A blue-sky background field was

also put, in order to form a homogeneous

background. First 120 pictures that were taken with

flash were selected.

The test set was built with the 18 cases of

training set and new 27 cases that were obtained as

follows.

Data collection for the test set was conducted

between August 2012 and July 2013, in the

Neurosurgery Unit of the Federal District Base

Hospital (HBDF), the Health Secretary of State of the

Federal District (SES / DF). The PU of all patients

admitted in this hospital were photographed. PU

images were taken with a professional camera

Canon® T3i model, 18-55mm, EOS line Rebel®

with 18 megapixel resolution in jpeg format. Camera

flash was turned off and the patient was placed in

order to be illuminated as well as possible.

Photographs were performed with the axis of the

camera lens perpendicular to the bed of the UP, in

order to reduce distortion produced by tilting.

ImageJ calculated colour means concerning RGB.

Table 1 shows these statistical attributes and

predominant stage and tissue for each PU.

Table 1: PU image data.

R G B St Tex

1 179 150 144 II S

2 185 119 109 III G

3 120 95 74 III S

4 156 98 68 IV N

5 108 79 65 III S

6 140 89 70 III G

7 131 81 63 III G

8 123 95 93 II G

9 114 80 61 IV N

10 119 75 60 IV G

11 203 131 120 III S

12 173 130 109 III G

13 179 119 108 II G

14 209 156 90 II S

15 128 83 85 II G

16 185 106 103 II G

17 147 105 73 II S

18 196 102 101 II G

117 113 110

III E

148 91 91

II E

141 96 86

II G

77 54 42

I N

143 87 73

II G

143 79 68

II G

123 84 87

IV G

109 87 88

IV Es

153 104 107

IV G

113 96 103

IV E

97 45 48

III G

30 133 107 110

II E

70 56 54

I N

157 103 104

II G

170 105 112

IV G

114 104 109

IV N

141 86 86

IV G

36 117 75 77

IV G

168 141 119

IV E

114 98 99

IV N

108 89 82

IV N

110 73 64

II E

146 100 101

IV G

42 126 105 105

IV E

159 110 113

IV G

124 95 97

IV E

132 86 83

IV G

The classification used filter J48. It is an open

source Java implementation of the C4.5 algorithm

that is an improvement of the basic ID3 algorithm. In

a decision tree, each non-leaf node is an input

TestinganImageMiningApproachtoObtainPressureUlcersStageandTexture

attribute, and each arc expresses a value of that

attribute. A leaf node corresponds to the expected

value of the target attribute when the input attributes

are described by the path from the root node to that

leaf node. In a satisfactory decision tree, each non-

leaf node should correspond to the input attribute that

is the most informative about the target attribute

amongst all the input attributes not yet considered in

the path from the root node to that node. It is so

expected to predict the target attribute using the

smallest possible number of questions on average

(Squire, 2004).

Entropy is used to determine how informative a

particular input attribute is about the target attribute

for a subset of the training data. Entropy is a measure

of uncertainty in communication systems introduced

by Shannon (1948). The attributes of the training

instances are searched and the attribute that best

separates the given examples is extracted by it. ID3

stops if the attribute perfectly classifies the training

sets; otherwise it recursively operates on the number

of possible values of attribute of the partitioned

subsets to get their "best" attribute (Scharma, 2011)

(Luger, 2004).

Results of classification are reported by software

Weka as follows.

In our case we have (a) four classes (stages I, II,

III and IV) and (b) three classes (slough – E,

granulation - G and necrotic tissue – N), and

therefore a 4x4 confusion matrix and a 3x3 confusion

matrix respectively. The number of correctly

classified instances is the sum of diagonals in the

matrix; all others are incorrectly classified.

The True Positive (TP) rate is the proportion of

examples which were classified as class x, among all

examples which truly have class x, i.e. how much

part of the class was captured. It is equivalent to

Recall. In the confusion matrix, this is the diagonal

element divided by the sum over the relevant row.

The False Positive (FP) rate is the proportion of

examples which were classified as class x, but belong

to a different class, among all examples which are

not of class x. In the matrix, this is the column sum

of class x minus the diagonal element, divided by the

rows sums of all other classes.

The Precision is the proportion of the examples

which truly have class x among all those which were

classified as class x. In the matrix, this is the

diagonal element divided by the sum over the

relevant column.

The F-Measure is simply

2*Precision*Recall/(Precision+Recall).

It is a combined measure for precision and recall

(Bouchaert, 2014).

Classification of training set with colour means

and stage resulted in the report in Fig. 1. The

confusion matrix pointed an accuracy percentage of

83,3%. The corresponding decision tree is in Fig. 2.

Fig. 3 reports classification of training set with

colour means and texture attributes. An 88.9%

accuracy was achieved. Fig. 4 shows the

corresponding decision tree.

A small difference with results achieved for both

training sets in the previous study results from

exclusion from texture attribute in the first one and

exclusion of stage attribute in the second one, in

present article.

Classification of test set with colour means and

stage resulted in the report in Fig. 5. It uses the same

decision tree obtained by training set. The confusion

matrix pointed an accuracy percentage of 44.4 %.

Fig. 6 reports classification of training set with

colour means and texture attributes. It uses the same

decision tree obtained by training set. A 64.4 %

accuracy was achieved.

4 RESULTS

Relationship between colour and stage of PU in 45

cases test set (44.4 % accuracy) was quite lower than

in 18 cases training set (83,3 % accuracy). A yet

significant difference can be noticed for relationship

between colour and texture (88.9 % accuracy for 18

cases training set and 64.4% accuracy for 45 cases

test set).

5 CONCLUSIONS

The high percentage of correct classification in 18

cases test set was not confirmed in 45 cases test set.

Therefore, the results obtained with test sets are

inadequate for the test sets of PU images. Possibly

the insertion of additional picture features in

classification could improve adequacy. Different

groups took the pictures in the training set and in the

remainder 27 cases set under different illumination

conditions. Some attention with picture capturing

procedures may improve the quality of test results

too. Anyway present analysis results encourage the

development of image capturing and processing

devices for practical use in healthcare institutions.

IMTA-52015-5thInternationalWorkshoponImageMining.TheoryandApplications

Figure 1: Classification report for training set with PU stage as leaf attribute.

Figure 2: Decision tree for the training set with PU stage as leaf attribute.

TestinganImageMiningApproachtoObtainPressureUlcersStageandTexture

Figure 3: Classification report for the training set with PU texture as leaf attribute.

Figure 4: Decision tree for the training set with PU texture as leaf attribute.

IMTA-52015-5thInternationalWorkshoponImageMining.TheoryandApplications

Figure 5: Classification report for the test set with PU stage as leaf attribute.

Figure 6: Classification report for the test set with PU texture as leaf attribute.

ACKNOWLEDGEMENTS

This study was partially sponsored by Brazilian

National Council for Technological and Scientific

Development (CNPq) and is part of the cooperation

between Catholic University of Brasília and German

Office for Academic Interchange (DAAD).

TestinganImageMiningApproachtoObtainPressureUlcersStageandTexture

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IMTA-52015-5thInternationalWorkshoponImageMining.TheoryandApplications