independent component analysis for assessing therapeutic response in vitiligo skin disorder

Independent component analysis for assessing therapeuticresponse in vitiligo skin disorder

M. H. AHMAD FADZIL{, S. NORASHIKIN{, H. H. SURAIYA{ and H. NUGROHO*{

{Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Tronoh,Bandar Sri Iskander, Perak Darul Ridzuan, Malaysia

{Dermatology Department, Hospital Kuala Lumpur, Jalan Pahang,Wilayah Persekutuan Kuala Lumpur, Malaysia

This paper describes an image analysis technique that objectively measures skin

repigmentation for the assessment of therapeutic response in vitiligo treatments. Skin

pigment disorders due to the abnormality of melanin production, such as vitiligo, cause

irregular pale patches of skin. The therapeutic response to treatment is repigmentation of

the skin. However the repigmentation process is very slow and is only observable after a

few months of treatment. Currently, there is no objective method to assess the therapeutic

response of skin pigment disorder treatment, particularly for vitiligo treatment. In this

work, we apply principal component analysis followed by independent component

analysis to represent digital skin images in terms of melanin and haemoglobin

composition respectively. Vitiligo skin areas are identified as skin areas that lack melanin

(non-melanin areas). Results obtained using the technique have been verified by

dermatologists. Based on 20 patients, the proposed technique effectively monitored the

progression of repigmentation over a shorter time period of six weeks and can thus be

used to evaluate treatment efficacy objectively and more effectively.

Keywords: Therapeutic response; Vitiligo; Principal component analysis; Independent

component analysis

1. Introduction

Skin pigment disorders cause the skin to appear lighter or

darker than normal skin. The disorders occur due to

abnormal melanin production [1–4]. Melanin is the colour

pigment found in hair, eye and skin. Albinism, melasma

and vitiligo are examples of skin pigment disorders.

Vitiligo is an idiopathic disease that causes destruction of

melanocytes (the pigment producing cells). Due to the lack

of melanin, vitiligo lesions are paler than normal skin, as

shown in figure 1. The prevalence of vitiligo varies from

0.1% to 2% in various global populations without any sex,

racial, skin types or socioeconomic predilection [2,4].

Currently, there is no objective method to assess the

therapeutic response to vitiligo treatment. A global

assessment of therapeutic response is made by physicians

using a scoring system based on an ordinal scale, e.g. the

physician’s global assessment (PGA). The scale is based on

the degree of repigmentation within lesions over time and

an example is shown in table 1. PGA is largely dependent

on the human eye and visual judgment to produce the

scorings. Therefore it is a highly subjective method of

assessment as inter-rate and intra-rate variation is inevi-

table. Moreover, it can be months before repigmentation

within the lesions can be observed. Treatment efficacy thus

cannot be determined quickly.

*Corresponding author. Email: [email protected]

Journal of Medical Engineering & Technology, Vol. 33, No. 2, February 2009, 101–109

Journal of Medical Engineering & TechnologyISSN 0309-1902 print/ISSN 1464-522X online ª 2009 Informa Healthcare USA, Inc.

http://www.informaworld.com/journalsDOI: 10.1080/03091900802454459

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The use of digital image techniques may help in an objec-

tive assessment of repigmentation and in follow-up studies

of skin pigment disorder progression to test the efficacy of

therapeutic procedures [5]. This paper describes the develop-

ment of image processing techniques for a computer-based

image analysis intended for the assessment of therapeutic

response in a skin pigment disorder such as vitiligo.

2. Approach

2.1. Interaction between light and skin

Skin is made up of multiple layers of epithelial tissues which

protect muscles and organs [1]. It has three primary layers: the

epidermis, the dermis and the hypodermis (subcutaneous

tissue). Melanin is a colour pigment found in epidermis layer

of skin. The blood vessels are all located in the dermis [1].

Anderson and Parrish found that when an incident light

approaches the skin surface, a small portion of the light will

be reflected because of the difference in the index of reaction

between the air and skin surface [6]. From their experiment,

the surface reflectance was found to be typically 4–7%

over the entire spectrum of 250–3000 nm [6,7]. Figure 2

illustrates the interaction between skin and incoming light.

The light reflections of skin can be defined by several

components, as illustrated in figure 1. At incident angles

close to normal, +5% of the incident light coming in

contact with skin is directly reflected at the surface. This is

mainly due to the change in refractive index between air

(nD=1.0) and skin (nD=1.5).

The primary chromophores in skin are melanin and

haemoglobin [7], as shown in figure 3. Most of the incident

light (nearly 95%) penetrates into skin and follows

a complex path until it exits back out of the skin or gets

attenuated by skin chromophores [7].

2.2. Flowchart of the algorithm

The spatial distribution of melanin and haemoglobin in

digital images of skin can be separated by employing

independent component analysis of a skin colour image [9].

In our work, principal component analysis (PCA) is used to

reduce the dimensions from three (RGB) to two principal

components: principal component 1 (PC 1) and principal

component 2 (PC 2). Next, independent component

analysis (ICA) is used to align principal component axes

to represent pure density vectors of melanin and haemo-

globin [10]. As a result, we will have skin images that

represent skin areas due to melanin and haemoglobin. The

vitiligo areas in the melanin images are then segmented

using a thresholding method based on the Euclidean

distance. The overall flowchart is shown in figure 4.

2.2.1. Principal component analysis. Principal component

analysis is used as a dimensional reduction tool. To find the

principal components of an image, the mean value is

initially subtracted from each of image spectral bands

(RGB) to obtain zero mean variable data.

R ¼ R0 � mR0; G ¼ G0 � mG0

; B ¼ B0 � mB0; ð1Þ

Figure 1. Vitiligo lesion.

Table 1. Physician’s global assessment scale.

Repigmentation Scale

0–25% Mild

26–50% Moderate

51–75% Excellent

76–100% Complete

Figure 2. Schematic representation of the major optical

pathways in human skin [8].

Figure 3. Distribution model of melanin and haemoglobin

in skin.

102 M. H. Ahmad Fadzil et al.

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where R0, G0 and B0 denote the image spectral band before

subtraction and mR0, mG0

and mB0denote the mean value of

image spectral bands.

The covariance matrix of the three dimensional RGB

dataset is then calculated. The covariance matrix of an

image in the RGB dataset is defined as follows:

COV ¼CRR CGR CBR

CRG CGG CBG

CRB CGB CBB

24

35; ð2Þ

where

CXX ¼1

N

XNi¼1

Xi � mið Þ2 ð3Þ

CXY ¼ CYX¼1

N

XNi¼2

XiYi

!� mXmY ð4Þ

mX ¼1

N

XNi¼1

Xi; ð5Þ

where X;Y 2 R;G;Bf g and Nand m denote number of

pixels in the image and means value, respectively.

The eigenvectors are then determined from the covar-

iance matrix by solving the following equation:

COV ¼ glgT; ð6Þwhere l is a diagonal matrix representing eigenvalues of

covariance matrix, COV and g is a matrix of eigenvectors of

covariance matrix, COV, arranged as a columns.

The eigenvectors are used as linear transform of original

(R, G, B) values. It is reported that the resulting vectors

have uncorrelated components, or in other words, the

primary axis of the data has been aligned where the

variance is maximal [11,12]. The vectors in new space

[X1 X2 X3]T are obtained by

X1

X2

X3

24

35 ¼ g11 g12 g13

g21 g22 g23g31 g32 g33

24

35 R

GB

24

35 ð7Þ

whereg11 g12 g13g21 g22 g23g31 g32 g33

24

35 are the eigenvectors of the covar-

iance matrix.

It has been shown that the RGB skin images can be

adequately represented by using two principal components

with an accuracy of 99.3% [11,12].

2.2.2. Independent component analysis. Skin colour dis-

tribution can be expressed as functions of pure density

vector of melanin and haemoglobin [13]. Let S1(x,y)

and S2(x,y) represent the quantities of the two colour

pigments on image coordinate (x,y), which are independent

variables. The colour vectors of the two pigments per unit

quantity are denoted a1 and a2, respectively. It is also

assumed that the compound colour vector v(x.y) can be

calculated using linear combination of the colour vectors as

follows:

vðx; yÞ ¼ a1s1ðx; yÞ þ a2s2ðx; yÞ: ð8ÞThis equation can be written as:

vðx; yÞ ¼ Asðx; yÞ ð9Þ

A ¼ a1½ a2� ð10Þ

Equation (9) is known as independent component analysis

(ICA), where A is a mixing matrix and s contains the

independent components.

The goal of ICA is to find a linear transformation W of

the dependent sensor signals v that makes the output

independent as possible [9,14–16] (equation (9)). ICA is

illustrated in figure 5. In addition, it is also necessary to

make vector, v, zero mean and unit variance in order to get

stronger independence conditions [14]. A preliminary study

using historical patient data has been reported earlier [17].

u ¼Wv ð11Þ

Figure 4. Flowchart of the algorithm.

Assessing therapeutic response in vitiligo skin disorder 103

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Matrix W is estimated using an ICA method developed by

Hyvarinen, and known as FastICA [9,15,16]. The outputs

resulting from the ICA process are images that represent

skin areas due to melanin and haemoglobin.

2.3. Thresholding based on the Euclidean distance

Thresholding is an image segmentation technique that

segments foregrounds/objects and their background in a

digital image. In the algorithm, thresholding based on

Euclidean distance [18] is performed on the melanin of a

skin colour image to separate vitiligo skin lesion from

healthy skin.

In the technique, two samples of the pigment melanin,

each representing the vitiligo skin lesion and healthy skin

segments are obtained. The Euclidean distance is used to

map each pixel to its closest segments.

2.4. Determination of repigmentation area

Vitiligo areas are defined as skin areas that lack melanin.

From skin images that represent skin areas due to melanin,

we can determine the area of vitiligo lesions and thus,

progression of repigmentation.

The difference in the vitiligo areas between skin images

before and after a treatment period will be expressed as

a percentage of repigmentation in each vitiligo lesion.

This percentage will represent the repigmentation pro-

gression of a particular body region. Dermatologists

choose the vitiligo surface areas whilst the details of

camera position and locations of the lesions are recorded

by clinicians. The information will be used to ensure

similar condition in subsequence camera shots during

treatment process. Errors due to geometrical changes are

reduced.

The measurement is used as an objective method to

determine the progression of repigmentation during treat-

ment, and the efficacy of vitiligo treatment.

3. Performance measurement

The performance of the algorithm is measured by employ-

ing the algorithm on reference model images.

3.1. Reference model

Reference model images are simulated images that repre-

sent healthy skin and vitiligo skin images. The images are

modelled based on the distribution of colour combinations

in the three spectral bands, namely red, green and blue

(RGB). The distribution models are developed using sam-

ples of skin colour taken from historical data of patients.

These samples are chosen by dermatologists to obtain valid

Figure 5. Independent component analysis.

Figure 6. Simulated (a) healthy skin and (b) vitiligo lesion

images.

Figure 7. (a) Reference model A; (b) reference model B;

(c) reference model C; and (d) reference model D.


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reference model. The distribution of each spectral value is

then modelled using Gaussian distribution. Equation (12)

shows the Gaussian distribution function.

fðx; m; sÞ ¼ 1

sffiffiffiffiffiffi2pp e�

x�mð Þ2

2s2 ; ð12Þ

where s is the standard deviation and m is the mean value.

The Gaussian distribution is chosen based on previous

studies of skin modelling by Caetano and Barone [10], Zhu

et al. [19] and Chang et al. [20]. They reported that the skin

colour distribution can be modelled using Gaussian

distributions.

A healthy skin model image as shown in figure 6(a) is

produced using a distribution model of healthy skin. The

vitiligo lesion model image, as shown in figure 6(b) is

produced using the distribution model of vitiligo lesions.

Using the healthy skin and vitiligo lesion model images,

four reference images, i.e. reference images A, B, C and D

(see figure 7) are constructed and used in investigating the

capability of the developed algorithm. Each reference

image is 2006 200 pixels in size. The size of the reference

image is constructed based on the size of vitiligo lesion

found on the data.

Reference image A models a skin image having a vitiligo

lesion with an area of 406 50 pixels (see figure 7(a)). Refer-

ence image B is created like reference image A (figure 7(b)),

Figure 8. Reference model A, (a) melanin and (b)

haemoglobin.

Figure 9. Reference model B, (a) melanin and (b)

haemoglobin.

Figure 10. Reference model C, (a) melanin and (b)

haemoglobin.

Figure 11. Reference model D, (a) melanin, (b) haemoglo-

bin and (c) 1-by-1 pixel repigmentation.

Figure 12. Patient C (a) RGB image taken at baseline,

(b) RGB image taken six weeks later.


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but in its vitiligo lesion area three areas which represent

repigmented skin areas are added. The size of each

repigmentation area is 56 5 pixels. Reference images C

and D (figures 7(c), (d)) are created like reference image B.

In reference image C the size of the repigmentation areas is

36 3 pixels, whilst in reference image D the size is reduced

to 16 1 pixel.

3.2. Testing the algorithm

By applying the developed system to all the reference ima-

ges, it has been able to discern vitiligo lesions, healthy skin

and skin repigmentation areas, as depicted in figures 8–11.

Under ideal conditions, the system can even detect skin

repigmentation areas whose sizes are only 1-by-1 pixel.

4. Results and analysis

4.1. Sample data

The vitiligo digital image analysis system is applied to the

skin images provided by Hospital Kuala Lumpur, Malay-

sia. A group of 20 patients with different clinical features of

vitiligo (generalized vitiligo, acrofacial vitiligo, segmental

vitiligo, focal vitiligo and universal vitiligo) are randomly

chosen by a physician. The images of the vitiligo lesions are

then captured using Nikon digital camera SLR D100.

Figure 12 shows samples from patient C. The images refer

to the same skin area and are taken within a 6-week interval.

It is observed that some repigmentation has occurred.

Figures 13(a)–(f) show the same skin area with lesion

(digitally enlarged) of patient C being processed by the digital

image analysis technique. The processed images now repres-

ent skin areas due to the melanin component (figures 13(b)

and (e)) and the haemoglobin component (figures 13(c)

and (f)). As discussed in x1, vitiligo lesions occur due to the

abnormality of melanin production. From the melanin

component image, we can determine melanin and non-

melanin (vitiligo) areas. By comparing previous and current

data the progression of repigmentation can be assessed.

Figure 13. Processed images of patient C: (a) RGB image at

baseline, (b) melanin at baseline, (c) haemoglobin at

baseline, (d) RGB image, six weeks later, (e) melanin, six

weeks later, and (f) haemoglobin, six weeks later.

Figure 14. Patient C (a) segmented melanin image at

baseline, (b) segmented melanin image, six weeks later.


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Segmented melanin images are shown in figure 14. To

ensure the accuracy of the system, the actual size of the

pixel in each image is calibrated using a reference marker.

For example, in figure 8(a), the marker (which has an area

of 113 mm2) is represented by 45 972 pixels. Thus, in

figure 12(a), 1 pixel represents 2.4586 1072 mm2. This

calibration process is performed in every image before we

measure the vitiligo lesion area in the image.

4.2. Analysis

Table 2 shows the vitiligo areas measured by the proposed

image analysis algorithm based on two visits. Table 3

Table 2. Vitiligo area measurements.

Vitiligo areas (mm2)

Body area 1st visit 2nd visit (6-week interval)

A Head 170.35 161.1

B Head 2447.58 2227.4

C Head 66.94 54.3

Lower limb 282.3 249.9

Trunk 577.7 511.6

Upper limb 3632.5 3595

D Head 3102.596 3049.7

E Trunk 4021.67 3796.23

Upper limbs 5666.8 5236.2

Head 164.92 85.4

F Trunk 2463.4 2142.37

G Head 363.5 318.6

H Feet 155.38 151.52

Head 389.85 347.1

Upper limbs 319.79 289.9

Lower limbs 1010.24 1000.88

Trunk 35.7 34.8

I Hands 83.169 82.06

Face 486.62 459.2

J Feet 95.8 94.5

Hand 61.8 57.6

Face 272.7 223.12

Upper limb 744.6 739.5

Lower limb 813.47 809

K Face 91.9 79.52

L Lower limb 89.6 84.68


M Head 81.61 78.7

Trunk 1882.7 1805.18

N Feet 196.306 171.69

Hand 175.9 164.05

Head 98.2 77.29

Lower limb 308.79 306.08

Upper limb 302.11 265.4

O Feet 141.61 134.7

Trunk 420.2 334.1

Upper limb 336.4 312.27

P Head 41.95 38.52

Q Feet 87.9 82.9

Hand 73.72 65.53

Head 58.4 54.44

Lower limb 128.77 113.34

Trunk 591.36 580.4

R Feet 677.7 611.29

Hand 223.6 213.78

Neck 69.74 69

Lower limb 1695.14 1420


S Head 156.06 142.88

T Head 1148.1 938.46

Table 3. Comparison between PGA and the proposedalgorithm.

Body areas PGA Proposed algorithm

A Head Mild 5% (Mild)

B Head Mild 9% (Mild)

C Head Mild 19% (Mild)

Lower limb Mild 11% (Mild)

Trunk Mild 11% (Mild)

Upper limb Mild 1% (Mild)

D Head Mild 2% (Mild)

E Trunk Mild 6% (Mild)

Upper limbs Mild 8% (Mild)

Head Moderate 48% (Moderate)

F Trunk Mild 13% (Mild)

G Head Mild 12% (Mild)

H Feet Mild 2% (Mild)

Head Mild 11% (Mild)

Upper limbs Mild 9% (Mild)

Lower limbs Mild 1% (Mild)


I Hands Mild 1% (Mild)

Head Mild 6% (Mild)

J Feet Mild 1% (Mild)

Hand Mild 7% (Mild)




K Face Mild 13% (Mild)

L Lower limb Mild 5% (Mild)


M Head Mild 4% (Mild)


N Feet Mild 13% (Mild)

Hand Mild 7% (Mild)




O Feet Mild 5% (Mild)



P Head Mild 8% (Mild)

Q Feet Mild 6% (Mild)

Hand Mild 11% (Mild)

Head Mild 7% (Mild)



R Feet Mild 10% (Mild)

Hand Mild 4% (Mild)

Neck Mild 1% (Mild)



S Head Mild 8% (Mild)

T Head Mild 18% (Mild)


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compares repigmentation assessment by physician’s global

assessment and the developed algorithm. Two dermatolo-

gists estimated the physician’s global assessment scores by

comparing the digital images of vitiligo lesions before and

after six weeks of treatment.

As recorded in table 2, after six weeks of treatment, it can

be seen that vitiligo areas are somewhat reduced indicating

positive response to the treatment. The changes in vitiligo

areas allow us to determine repigmentation progression.

This information can be used in assisting physician to

evaluate the efficacy of the treatment in a shorter time

period. In comparison to physician’s global assessment

scores (table 3), the percentages obtained using the

proposed method are found to be within the physician’s

global assessment range. It is shown that the proposed

method is consistent with the physicians. Based on the 20

patients, the proposed method is very accurate as it did not

produce any errors.

5. Conclusion

Skin pigment disorders cause irregular pale patches of the

skin. The disorders occur due to the abnormality of

melanin production. The use of digital image analysis in

this research enables the objective assessment of repigmen-

tation progression and evaluation of efficacy of therapeutic

procedures. A global assessment of therapeutic response is

made by the physicians using a scoring system based on an

ordinal scale, of which physician’s global assessment (PGA)

is one example. It is shown that in order to determine

repigmentation progression objectively, the conversion of

RGB images to melanin related skin images is essential.

This is achieved by using PCA to dimensionally reduce 3D

RGB images into 2D data consisting of the principal

components. By using ICA, we are able to align the 2D

data into melanin and haemoglobin related data, resulting

in a skin image that effectively represents melanin. The

digital image analysis method is able determine vitiligo

lesion (non-melanin area) and repigmentation progression

on a shorter time frame.

Results from 20 patients show that the proposed

algorithm has the ability to objectively measure the

therapeutic response of vitiligo treatment. It is found

that all of the percentages obtained using the proposed

method are comparable to the physician’s global assess-

ment range as verified by dermatologists (two dermatol-

ogists scored by comparing the lesion). Thus, the

proposed method is highly accurate as it is consistent in

comparison to physician’s global assessment performed by

dermatologists.

Acknowledgement

This research is a collaborative work between Universiti

Teknologi PETRONAS and the Dermatology Department,

Hospital Kuala Lumpur. The authors would like to

acknowledge Ahmad Tarmidzi Zakaria from the Derma-

tology Department, Hospital Kuala Lumpur for his

assistance in data collection.

Declaration of interest: The authors report no conflicts of

interest. The authors alone are responsible for the content

and writing of the paper.

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independent component analysis for assessing therapeutic response in vitiligo skin disorder

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