SEGMENTATION AND CLASSIFICATION OF CUTANEOUS

ULCERS IN DIGITAL IMAGES THROUGH ARTIFICIAL

NEURAL NETWORKS

André de Souza Tarallo, Adilson Gonzaga

Department of Electric Engineering, USP University, Av.Trabalhador São Carlense- 400, São Carlos, Brazil

Marco Andrey Cipriano Frade

Department of Medical Clinics, USP University, Ribeirão Preto, Brazil

Keywords: Leg Ulcer, Computer Vision, Artificial Neural Network.

Abstract: Treatments of leg ulcers are generally expensive and those conducted through the direct manipulation for

analysis of its evolution. The treatment efficiency is observed through the reduction of the size of ulcers in

relation to the amount of tissues found in their beds, which are classified as granulated/slough. These results

are obtained through analyses performed after consultation due to the time these analyses take. This work

proposes a new non-invasive technique for the follow-up of treatments aimed at cutaneous ulcers. In this

methodology, it was proposed that digital photos of cutaneous ulcers would be submitted to an artificial

neural network (ANN), so that all surrounding the wound except for the wound itself could be extracted

(skin/background), thus obtaining the ulcerated area. Computer vision techniques have been applied in order

to classify the different types of tissues found in the ulcer bed, thus obtaining the corresponding granulation

and slough percentages as well as its area. The results obtained have been compared with the results

obtained by Image J software. Finally, this methodology will be a useful tool for health professionals in

relation to the quickness and precision that it will provide results along the consultation.

1 INTRODUCTION

Leg ulcers are a public health problem worldwide

and reach from 3% to 5% of the population older

than 65 years of age and 1% of the adult population.

(Mekkes et al., 2003). The treatment presents some

complications due to its long-term characteristic,

discomfort of curatives and uncertainness in relation

to its success, once its cure depends on several

factors that act as intervenient variables in the

process, causing significant social and economic

impact. The treatment is painful, expensive and slow

due to a number of associated etiopathogenic

factors, and the disease represents one of the main

causes for work absenteeism.

The use of computer tools involving image

processing (Computer Vision) and ANN consists of

an alternative analysis method for the follow-up of

leg ulcer treatments. (Albu and Ungureanu, 2005).

This method does not allow the direct contact with

the wound, once ulcers are analyzed through

digitized images (Goldman and Salcido, 2002).

Therefore, the health professional disposes of tool

designed to support the treatment of ulcers.

The objective of this work is to present a

proposal to aid in the quantitative analysis of each

tissue found in the inner part of wounds, which are

classified as granulated and slough and in the

calculation of the wounded area. With these

measurements, one may have a perspective in

relation to the treatment evolution, since it provides

a dynamic-therapeutic healing follow-up.

This work also proposes the development of

methodology to classify leg ulcer tissues in order to

support specialists along the treatment evolution.

The employment of computer software with the

proposed methodology may lead the patient to feel

safer, since there is no direct contact with the wound

to obtain samples for analyses. In a first phase, the

proposal consists of performing the extraction of

features from the leg ulcer digital image base

through color samples removed from ulcer images

manually and of applying them to the neural

de Souza Tarallo A., Gonzaga A. and Andrey Cipriano Frade M. (2008).

SEGMENTATION AND CLASSIFICATION OF CUTANEOUS ULCERS IN DIGITAL IMAGES THROUGH ARTIFICIAL NEURAL NETWORKS.

In Proceedings of the First International Conference on Health Informatics, pages 59-65

 SciTePress

Figure 2: Example of an image with noise, skin and ulcer regions.

network test for images segmentation – Training

Phase. In a second phase image processing

techniques were used to classify tissues found at the

inner region of the wound - Test Phase.

2 LEG ULCERS

Leg ulcers occur due to more than one cause: venous

hypertension (~80%), arterial insufficiency (~10%)

or the combination of both – the called “mixed

ulcers”, among others. (Abbade and Lastória, 2005).

Ulcers occurring at the tip end of the lower limbs are

a result of venous, arterial or neurovascular diseases

such as varix, thrombosis venous, arteriosclerosis,

diabetes and arterial hypertension, among others.

(Dean, 2006).

Each type of ulcer presents own characteristics,

requires different treatments, and must be evaluated

separately. (Borges, 2005 and Dean, 2006).

A deficient blood circulation decreases the

intake of oxygen and nutrients and reduces the

removal of metabolism-derived products such as

free radicals, factors that contribute for the healing

delay. The main problem of leg ulcers is the

recurrence; 30% of healed ulcers recur within the

first year and this rate increases to 78% after two

years when inadequately treated. (Barros, 2000).

Figure 1: Venous Leg Ulcer.

A good analysis of some characteristics,

parameters and interpretation of clinical ulcer

examinations is vital, and among these

characteristics: number and size of the ulcer, edges

and appearance of the lesion bottom, type of ulcer,

skin-ulcer state, arterial test, venous test and

evaluation of the microbiological status (culture,

exams) are worth mentioning. (Kupcinskas, 2006).

Leg ulcer is a very relevant and common

problem in health services worldwide and affects

between 0.1% and 1% of the adult population with

studies pointing to prevalence. (Mekkes et al., 2003).

However, the key for the selection of effective ulcer

treatments is based on the evaluation process of its

etiology.

Venous-origin leg ulcers – Figure 1 – popularly

known as varicose ulcers, are mainly caused by

chronic venous insufficiency, term described as

lower limbs syndrome, which represents the

incapacity of maintaining the balance between the

arterial blood flow that reaches the lower limb and

the venous flow that returns to the right atrium as a

result of the incompetence of the superficial and/or

deep venous system with symptoms such as edema,

pigmentation, pain and disabilities. (Barros, 2000)

(Pitta, Castro and Burihan, 2000).

In normal people, the blood pressure decreases

during the practice of physical exercises, but in

patients with venous incompetence, the pressure

remains high during effort. Venous ulcers are mainly

characterized by the presence of edema, darkened

pigmentation, varicose veins and

lipodermatosclerosis (hardening and fibrosis in the

dermis and subcutaneous tissue) at the lower limbs.

(Phillips and Dover, 1991).

In the study conducted by Skaraborg (cited in

Figueiredo, 2003), 5.6% of people with 65 years of

age or older presented open or healed lower limbs

ulceration and 2.4% of the adult population above 15

years of age have already had ulcers. European data

show that 1.5% of the adults will have ecstasy ulcer

sometime in their lives.

3 MATERIAL AND METHODS

The photographs were taken through a Sony Cyber

shot P-93 camera with 3 mega pixels, 3X optical

zoom and without digital zoom. The images

randomly selected from our the image bank were

standardized and non-standardized in relation to

zoom, illumination, distance between the camera

and the patient’s leg and the focus in the patient’s

leg. We made the image bank because none was

HEALTHINF 2008 - International Conference on Health Informatics

found publicly available library. Fifty images of

thirty five patients were selected to test the validity

of the proposed methodology.

The methodology proposed is divided into two

phases: in the first phase, the extraction of the color

characteristic and the ANN training occur (Training

Phase) with ten images. (Haykin, 2001). The second

phase consists of segmenting images with forty

images (ANN Test), elimination of noises,

improvement of the image quality and later tissue

classification in the wound bed – Test Phase.

(Gonzalez and Woods, 2002).

In the first phase, initially two algorithms were

applied to images in order to obtain, skin, ulcer (bed)

and noise (background - all that is not skin and

ulcerated area) color - Figure 2-, which will serve as

inputs for the ANN training to distinguish the color

characteristics of the wound edge from the other

colors not involved in the wound thus, forming

training standards.

The color characteristics corresponding to skin

and non-skin (skin/noise/background) in the RGB

model are obtained through the first algorithm; this

process is manually performed by the computer

operator (this process should be performed by a

health professional, once he will know which are the

best points to be selected in order to find out what

each color represents in the image). The software

used for the development of this methodology was

the Matlab 7.0, (Math Works, 2004) which shows

the 50 images selected (one at a time) and waits for

the computer operator to select the image region

with the mouse with the aid of the algorithm. Each

color characteristic of the selected region is stored in

a text-type file to form the feature vector (skin/non-

skin matrix), according to Figure 3.

Figure 3: Example of Skin/Non-Skin Matrix.

If one photo contains several interesting

characteristic regions, this image is opened more

than once for the selection of the characteristics.

The values presented in each line of Figure 3

represent the following:

• -1 is a bias used by the neural network for

the activation of the neuron;

• The three next values refer to the RGB

value in relation to the color selected by the

user;

• 1 is the value to be used as exit desired by

the neural network.

The second algorithm is used to obtain the

wound color characteristics in the RGB model,

which are obtained as in the first algorithm. The

feature vector (wound matrix) of each selected color

is saved in another text-type file. The desired exit of

the wound matrix is the 1.

These two matrixes will form the “training

patterns”, which will be used for the training.

The first phase of the proposed methodology in

divided into two stages. In the first stage, the

entrance characteristics for the neural network are

obtained (color characteristics) and in the second

one, these characteristics are applied in the neural

network for its training – Figure 4.

3.1 ANN Training

The extracted characteristics (training patterns) are

applied to an ANN for its training and later

classification and separation of the wound from the

remaining portion of the image (Test Phase). The

MLP Feedforward neural network architecture was

used with the Back-propagation training algorithm

(Haykin, 2001), which was the architecture most

used for classification in several areas, and the

cutaneous ulcer images were generated in the RGB

color model. Before the Test Phase, the ANN must

be trained in order to learn about the color

characteristics obtained through both algorithms

previously mentioned. The training characteristics

are the following:

 Both features vectors are concatenated in

order to form the training matrix. Bias, RGB

and the desirable output are arranged in

different variables, and the RGB

characteristics are normalized for the [-1, 1]

interval.

 The neural network is initialized using the

minimum/maximum function of the training

matrix.

SEGMENTATION AND CLASSIFICATION OF CUTANEOUS ULCERS IN DIGITAL IMAGES THROUGH

ARTIFICIAL NEURAL NETWORKS

Figure 4: First phase of the proposed methodology.

 The neural network training was performed

using the tangent-hyperbolic sigmoid

activation function (so that the values

corresponding to the RGB characteristics do

not exceed the normalized interval). The

moment gradient is used for the three occult

layers of the neural network plus the output

layer.

 Values corresponding to other parameters

used in this algorithm and in the Neural

Network will be specified in the next topics.

3.2 ANN Test (Classification of Images)

In the second phase, or Test Phase, the efficiency of

the Neural Network is verified in the segmentation

of the 40 images from results obtained in the training

(first phase). A post-processing is required to

eliminate some remaining noises to better prepare

the image for the tissue classification. Figure 5

presents the second phase of the proposed

methodology. The techniques employed are the

erosion and dilation morphologic operations.

(Gonzalez and Woods, 2002). Finally, the tissues are

classified based on the counting of pixels, where

similar colors are associated to the type of tissue.

Besides the granulation and slough tissue

classification, the percentage of these two types of

tissues and the ulcer area in the image were

calculated.

The algorithm used (ANN Test) presents the

following steps:

1) Segmentation of Images;

2) Post-Processing: Images are processed

through dilation and erosion and image

superposition in order to eliminate noises and

to show the wound region only;

3) Counting of pixels corresponding to the

granulation and slough tissue and calculation

of the percentage corresponding to each type

of tissue in the image;

4) Generation of an image with markings in

which pixels corresponding to granulation and

slough are counted: white pixels are

granulation tissue and the others are the

slough tissue;

5) Calculation of the leg ulcer wounded area

in cm2.

The segmentation is performed by the Neural

Network using parameters from the Training Set and

commands based on Neural Networks toolbox from

the Matlab 7.0 software (Math Works, 2004); and

the resulting image that distinguishes the wound

from the rest of the image is obtained – pre-

processed image.

3.2.1 Post-Processing

The pre-processed image is then submitted to a post-

processing in order to eliminate noises and to show

the wound region only. To do so, erosion and

dilation morphologic operators were used.

In order to use erosion and dilation operators in the

Matlab software, the figure has to be converted into

gray scale, which is the only way that the figure

allows the use of such morphologic operators

(Gonzalez and Woods, 2002).

In order to use these morphologic operators, a

structuring element should be created to serve a

Inputs to

the Neural

Network

eural

Network

MLP

Stage 2

1......5

Selection of

Manual Points

Wound

Matrix

Skin/Non-Skin

Matrix

Stage 1

HEALTHINF 2008 - International Conference on Health Informatics

Figure 5: Second phase of the proposed methodology.

Figure 6: Leg Ulcer Segmentation Results. In (a) Original Image – (b) Pre-Processed Image – (c) Post-Processed Image –

(d) Image with counted pixels.

dilation parameter. The structuring element used had

a square format (Math Works, 2004). Following, the

Sobel edge detector was used.

This image was superposed to the original image

with the objective of obtaining an improved and less

noisy new image – post-processed image in which

pixels were counted and calculations were

performed. Finally, an image based on the post-

processed image was generated, with marking of the

sites in which slough and granulation pixels were

counted. Figure 6 presents images according to the

algorithm execution sequence. The parameters used

in the neural network of this methodology may be

observed in Table 1.

Table 1: Values of the Neural Network Parameters.

Parameter Value

Neurons in the 1

hidden layer 4

Neurons in the 2

hidden layer 4

Neurons in the 3

hidden layer 1

Moment Term 0.5

Maximum Number of Iterations 1000

Training Error Rate 1x10

-3

4 RESULTS AND ANALYSES

Considering the 40 test images and the segmented

wound area only, the average slough and granulation

Training Results

eural Network Tes

1...50

Image Processing

Techni

ues

1...50

Counting of pixels

of each image

Calculation of the

percentage of each

tissue

1...50

SEGMENTATION AND CLASSIFICATION OF CUTANEOUS ULCERS IN DIGITAL IMAGES THROUGH

ARTIFICIAL NEURAL NETWORKS

percentages in relation to the total image may be

verified in Table 2. The results were obtained

through the proposed methodology.

Table 2: Arithmetic average of the tissue percentage–

Proposed Methodology.

Total Image Wound Area

Slough 10.5% 26.1%

Granulation 18.4% 73.9%

The same images tested in the proposed

methodology were applied to the Image J for

comparison purposes, because this software is used

for made analyses of images of leg ulcers at

department of dermatology of FMRP (Ribeirão

Preto Medical School) and it is desirable to have a

tool more practical than the Image J.

The results obtained through Image J may be

observed in Table 3. (Gomes, Santana and Minatel,

2005).

Table 3: Arithmetic average of the tissue percentage –

Image J.

Total Image Wound Area

Slough 18.9% 43.3%

Granulation 30.0% 56.7%

The area of each wound in cm

in relation to the

total image was also calculated both through the

proposed methodology and through the Image J; the

arithmetic averages of results may be observed in

Table 4.

Table 4: Arithmetic average of the Wound Areas.

Proposed

Methodology

Image J

Average Area 13.1cm

14.1cm

The results obtained through the Image J

freeware software and with our methodology

seemed to be satisfactory; in the total tissue area, the

average was 13.1 cm

through the proposed

methodology and 14.1 cm

through Image J (Figure

7). In relation to the granulation, the average

obtained was 12.4 cm

through the proposed

methodology and 12.6 cm

through Image J (Figure

8). In relation to slough, the average obtained was

1.8 cm

through and 1.9 cm

through Image J

(Figure 9).

Figure 7: Results of the t Test for total area.

Figure 8: Results of the t Test for granulation.

Figure 9: Results of the t Test for slough.

It is worth reminding that the area evidenced

through Image J is manually performed, and takes a

long time until it comes to the final results, whereas

in the proposed methodology, this process is

automatically performed by the neural network,

HEALTHINF 2008 - International Conference on Health Informatics

which makes the processing faster and safer. This

area evidenced manual affects in the difference of

the results of the table 2 and 3 as well as some

terrible interpretations of RNA owed the qualities of

the images and of obtaining of the same ones.

The results were analyzed by a medical area

specialist, who verified the concordance of results

obtained.

Figure 7 shows the graphic of the t-student test

applied to results obtained through both the

proposed methodology and Image J for total areas.

Lines in the center of the graphic show the

arithmetic averages of results obtained through each

methodology and one may observe that they are very

close to each other.

Similarly, there are two other graphics that also

corroborate the efficiency of results obtained

through both Image J and the approach of this paper.

Figure 8 shows the results of t-student tests for

granulation area and Figure 9 for slough area.

5 CONCLUSIONS

Both the Image J and our methodology based on

ANN presented satisfactory results. The t-student

test at 95% was applied and the results confirmed

the efficiency of both methods. This finding testifies

that the variation observed between the results

obtained through both methodologies is acceptable

and that they can be applied in practice.

The results obtained suggest that both image

analysis methods are effective in the measurement of

total area, granulation and slough, being considered

as adequate for the dynamic-therapeutic evaluation

of leg ulcers. Artificial Neural Networks seem to be

a high-level methodology for the analysis of images

due to the lower interference from the

operator/researcher, since it does not require manual

design.

This new application will be one more tool to aid

in the diagnosis at FMRP and perhaps replace the

image J because of its little practicality. For better

performance of this new application is desirable to

use standardized images, as mentioned in item 3,

because the images non-standardized not behaved so

well on the standardized; but nevertheless been

achieved good and acceptable results general finals.

This project encourages and contributes for the

application of new technologies and hence the use of

softwares in this area with the emergence of new

research lines.

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SEGMENTATION AND CLASSIFICATION OF CUTANEOUS ULCERS IN DIGITAL IMAGES THROUGH

ARTIFICIAL NEURAL NETWORKS