Non-Invasive ABCD Monitoring of Malignant Melanoma Using Image Processing in MATLAB

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1074
Non-Invasive ABCD Monitoring of Malignant Melanoma Using Image
Processing in MATLAB
Mrs M.R.Patil
Professor, Dept. of Electronics and Communication Engineering, DBACER, Nagpur, Maharashtra, India
Aboli Ghonge, Mansi Dixit, Vaibhavee Bobde, Akshay kumar, Deep Joshi
Student, Dept. of Electronics and Communication Engineering, DBACER, Nagpur, Maharashtra, India
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Abstract-Malignant Melanoma- skin cancer spreads
through metastasis, and thus, it's been evidenced to be
terribly fatal. applied math proof has unconcealed that the
bulk of deaths ensuing from carcinoma are as a results of
skin cancer. any investigations have shown that the survival
rates in patients rely upon the stage of the cancer; early
detection and intervention of skin cancer implicate higher
possibilities of cure. Clinical identification and prognosis of
skin cancer are difficult, since the processes are liable to
misdiagnosis and inaccuracies due to doctors' sound
judgement. Malignant melanomas are asymmetrical, have
irregular borders, notched edges, and color variations,
therefore analyzing the form, color, and texture of the skin
lesion is vital for the first detection and bar of skin cancer.
This paper proposes the 2 major elements of a noninvasive
time period automatic skin lesion analysis system for the
first detection and bar of skin cancer. the primary part could
be a time period attentive to facilitate users forestall skin
burn caused by sunlight; a completely unique equation to
work out the time for skin to burn is therebyintroduced.The
second part is an automatic image analysis module, which
contains image acquisition, hair detection and exclusion,
lesion segmentation, feature extraction, and classification.
The projected system uses PH2 Dermoscopy image
information from Pedro Hispano Hospital for the event and
testing functions.Theimageinformationcontainsa complete
of two hundred dermoscopy pictures of lesions, together
with benign, atypical, and skin cancer cases. The
experimental results show that the projected system is
economical, achieving classification of the benign, atypical,
and skin cancer pictures with accuracy of ninety six.96.3%,
95.7%, and 97.5%, severally.
Key Words: Image segmentation, skin cancer, melanoma.
1. INTRODUCTION
BACKGROUND AND MOTIVATION
Today, carcinoma has been progressively knowntogether of
the key causes of deaths. analysis has shown that there ar
various sorts of skin cancers. Recentstudieshaveshownthat
there ar roughly 3 usually famed sorts of skin cancers.These
embrace skin cancer, basal cell cancer (BCC), and epithelial
cell carcinomas (SCC). However, skin cancer has been
thought of together of the foremost risky sorts within the
sense that it's deadly, and its prevalence has slowly
accumulated with time. skin cancer could be a conditionora
disorder that affects the epidermal cell cells thereby
preventative the synthesis of animal pigment . A skin that
has inadequate animal pigment is exposed to the danger of
sunburns yet as harmful ultra-violet rays from the sun .
Researchers claim that the unwellness needs early
intervention so as to be able to establish actual symptoms
that may create it simple for the clinicians and
dermatologists to stop it. This disorder has been tried to be
unpredictable. it's characterized by development of lesions
within the skin that fluctuate in form, size, color and texture.
although the majoritydiagnosedwithcarcinoma havehigher
possibilities to be cured, skin cancer survival rates ar less
than that of non-melanoma skin For thirty years, a lot of or
less, skin cancer rates areincreasingsteady.it'stwentytimes
a lot of common for White peopletopossessskincancer than
in African-Americans. Overall,throughouttheperiodoftime,
the danger of developing skin cancer is roughly two
hundredth (1 in 50) for whites, 0.1% (1 in 1,000) for blacks,
and 0.5% (1 in 200) for Hispanics. Researchers have
instructed that the employment of non-invasivestrategiesin
identification skin cancer needs intensive coaching in
contrast to the employment of eye. In alternative words, for
a practitioner to be able to analyze andinterpretoptionsand
patterns derived from dermoscopic pictures, they need to
bear through intensive coaching. This explainswhythere'sa
large gap between trainedandprimitiveclinicians.Clinicians
ar typically discouraged to use the eye because it has
antecedently junction rectifier to wrong diagnoses of skin
cancer. In fact, students encourage them to embrace
habitually the employment of transportable automatic real
time systems since they're deemed to be terribly effective in
hindrance and early detection of skin cancer.
Dermatologist will take pleasure in a transportable system
for carcinoma interference and early detection.unnecessary
to mention, one ought to note that at the instant, the work
bestowed during this paper is that the solely planned
moveable sensible phone-based system that may accurately
discover malignant melanoma. Moreover, the planned

system also can discoveratypical moles.Mostoftheprevious
work don't reach high accuracy, ordon'tseemto be enforced
on a transportable sensible phone device, and in the main
don't have any interference feature. this is often wherever
the necessity for a system together withsuchoptionsisseen.
2. PROPOSED SYSTEM
The flow chart of the proposed dermoscopy image analysis
system.
FIGURE 1. Flowchart for the proposed dermoscopy image analysis
system.
2.1 IMAGE ACQUISITION
The first stage of our machine-controlledskinlesionanalysis
system is image acquisition. This stage is important for the
remainder of the system; thence, if the image isn't non
heritable satisfactorily, then the remaining elements of the
system (i.e. hair detection and exclusion, lesion
segmentation, feature extraction and classification) might
not be doable, or the results won't be cheap, even with the
help of some style of image sweetening.
FIGURE 2. The dermoscopy device attached to the iPhone and sample of
images captured using the device.
In order to capture top quality pictures, the Phone camera
is employed, equipped with eight megapixels and one.5
pixels. mistreatment the iPhone camera solitary has some
disadvantages since first, the scale of the captured lesions
can vary supported the space between the camera and
therefore the skin, second, capturing the pictures in several
lightweight environments are goingtobeanotherchallenge,
and third, the small print of the lesion won't be clearly
visible. to beat these challenges, a dermoscope is connected
to the Phone camera. Figure a pair of showsthedermoscope
device connected to the Phone. The dermoscope provides
the best quality views of skin lesions. it's a exactness
designed optical system with manylenses.Thisprovidesthe
correct standardized zoom with

FIGURE 3. Illustration of two samples for hair detection, exclusion and
reconstruction, (a) the original image, (b) the gray image before hair
detection and exclusion, (c) the hair mask (d) the gray image after hair
detection, exclusion and reconstruction applied.
auto-focus and optical magnification of up to twenty on to
the camera of the iPhone device. Its form ensures sharp
imaging with a fixed distance to the skin and consistent
image quality. Also, it's a singular twin lightweight system
with six polarized and 6 white LEDs. This dermoscope com-
bines the benefits of cross-polarized and immersion fluid
dermoscopy. Figure twoshowssamplesofpicturescaptured
exploitation the dermoscope connected to iPhone camera.
2.2. HAIR DETECTION AND EXCLUSION
In dermoscopy pictures, if hair exists on the skin, it'll seem
clearly within the dermoscopy pictures. Consequently,
lesions is part lined by hair. Thus, hair will impede reliable
lesion detection and have extraction, leading to unsatisfying
classification results. This section introduces a picture
process technique to notice and exclude hair from the
dermoscopy pictures as a necessary step conjointly seen in .
The result's a clean hair mask which may be accustomed
phase and take away the hair within the image, making
ready it for any segmentation and analysis.
To notice and exclude the hair from the lesion, first, the hair
is segmental kind the lesion. Toaccomplishthistask,a group
of eighty four directional filters area unit used. These filters
area unit created by subtracting a directional Gaussianfilter
(in one axis alphabetic character of Gaussian is high
associated in alternative axis alphabetic character is low)
from an isotropous filter (sigma is higher in each axes).
Later, these filters area unit applied to the dermoscopy
pictures. once segmenting the hair mask, the image is
reconstructed to fill the hair gap with actual pixels. To
reconstruct the image, the system scans for the closest edge
pixels in eight directions, considering the present pixel is
within the region to ll. These eight edge pixels of hair region
area unit found and therefore the
price|mean|average|norm} of thoseeightpixelsisholdonas
pixel value of hair pixel. Figure three illustrates the method
of hair segmentation and exclusion.
2.3 IMAGE SEGMENTATION
Pigmented skin lesion segmentation to separate the lesion
from the background is an important method before
beginning with the feature extraction so as to classify the 3
differing types of lesion (i.e. benign, atypical andmelanoma)
. The segmentation step follow as: 1st, RGB dermoscopy
image is scan (See Figure four, Step 1) and regenerate to a
grey scale image. it's done by forming a weighted total ofthe
R, G, and B elements as 0:2989 RC0:5870 GC0:1140 B. Then,
a 2 dimensional mathematician low-pass filter is generated
by Equations a pair of and three.
where h could be a 2-D filter of size n1, n2 9 9, and
alphabetic character is zero.5. The filtered image is given in
Figure four, Step 2. once the mathematician filter is applied,
a worldwide threshold is computed by Otsu's technique to
be wont to convert associate degree intensity image to a
binary image. Otsu's technique chooses the edge to reduce
the intra-class variance of the background and foreground
pixels. This directly deals with the matter of evaluating the
goodness of thresholds. associatedegreeoptimumthreshold
is chosen by the discriminant criterion. Theensuingimage is
given in Figure four, Step 3. Step four removes the white
corners within the dermoscopy image. so as to try to to this,
the ensuing image within the previous step is disguised by
Mask1 that's outlined in Figure five. All white pixels within
the corners area unit replaced with black pixels.
After applying the edge, the perimeters of the output
image become irregular. To smoothen the perimeters,
morphological operation is employed. A disk-shaped
structure part is formed by employing a technique referred
to as radial decomposition mistreatment periodic lines[44],
[45]. The disk structure part is formed to preserve the
circular nature of the lesion. The radius is specified as
eleven pixels so the massive gaps may be crammed. Then,
the disk structure part is employed to perform a

morphological closing operation on the image. Step five in
Figure four shows the ensuing image. Next, the
morphological open operationisappliedtothebinaryimage.
The morphological open operation is erosion followed by
dilation; an equivalent disk structure part that was created
within the previous step is employed for each operations.
See Figure four, step 6.
In the next step, associate degree formula is employed to ll
the holes within the binary image. A hole could be a set of
background pixels that can't be reached by filling within the
background from the sting of the image. Figure 4, step seven
shows the result image.
In the next step, associate degree formula is applied
supported active contour [25] to phase the grey scaleimage,
that is shown in Figure four, step 4. The active contour
formula segments the 2-D grey scale image into foreground
(lesion) and back-ground regions mistreatment active
contour primarily based segmentation. The active contour
operate uses the image shown in Figure four, step seven asa
mask to specify the initial location of the activecontour.This
formula uses the Sparse-Field level-set technique for
implementing active contour evolution.
FIGURE 4. Steps of the proposed dermoscopy image segmentation
algorithm applied to two images (a) and (b).
FIGURE 5. Mask 1 and Mask 2, used in the segmentation algorithm to
prepare the image for the initial state of the active contour and to remove
the corners.

It additionally stops the evolution of the active contour ifthe
contour position within the currentiterationisthatthesame
mutually of the contour positions from the foremost recent
five iterations, or if the most range of iterations (i.e.400) has
been reached. The output image may be a binary image
wherever the foreground is white and therefore the
background is black, shown in Figure four, step8.
The next step is to get rid of the tiny objects. To do that, first,
the connected elements square measure determined.
Second, the realm of every part is computed. Third, all little
objects that have fewer than fifty pixels square measure
removed. This operation is thought as space gap. Figure 4,
step nine shows the end result image. Finally the disk
structure part that was created within the previous step is
employed to perform a morphological shut and open
operation. After that,the ensuingimageiscovert withMask2
to preserve the corners (Figure five, Mask2). Figure 4, step
ten shows the final binary mask that accustomed mask the
pictures.
2.4 FEATURE EXTRACTION
Feature extraction is that the method of conniving
parameters that represent the characteristics of the input
image, whose output can have an immediate and powerful
influence on the performance of the classification systems.
during this study, 5 totally different feature sets square
measure calculated.Thesesquaremeasure2-Dquick Fourier
rework (4 parameters), 2-D distinct trigonometric function
rework (4 parameters), complexness Feature Set (3
parameters), ColorFeatureSet(64parameters)andPigment
Network Feature Set (5 parameters). additionally to the 5
feature sets, the subsequent four options are calculated:
Lesion form Feature, Lesion Orientation Feature, Lesion
Margin Feature and Lesion Intensity Pattern Feature.
a) 2-D FAST FOURIER TRANSFORM
The 2-D quick Fourier remodel (FFT) feature set is
calculated. The 2-D FFT feature set includestheprimarycoef
ficient of FFT2, the primary constant of thecross-correlation
[51] of the primary twenty rows and columns of FFT2, the
mean of the primary twenty rows and columns of FFT2, and
also the variance of the primary twenty rowsandcolumnsof
FFT2.
b) 2-D DISCRETE COSINE TRANSFORM
A 2-D distinct circular function rework (DCT) expresses a
finite sequence of knowledge points in terms of a total of
circular function functions periodic at totally different
frequencies. The 2-D DCT feature set includes the primary
constant of DCT2, the primary constant of the cross-
correlation of the primary twenty rows and columns of
DCT2, the mean of the primary twenty rows and columns of
DCT2 and therefore the variance of theprimarytwentyrows
and columns of DCT2.
c) COMPLEXITY FEATURE SET
The quality feature set includes the mean (Equation 4),
variance (Equation 5), and mode supported the intensity
worth of the Region of Interest (ROI).
where M is the mean, _ is the standard deviation, Ii is the
intensity value of pixel i and n is the pixels count.
d) COLOR FEATURE SET
Color options in dermoscopy are vital. Typical pictures
contains three-color channels that are red blue and
inexperienced. Use of color is another technique to assess
malignant melanoma risks. Usually, malignant melanoma
lesions have the tendency to alter color intensely creating
the affected region to be irregular. For the colour feature set
the 3-D bar graph of the elements of the science lab color
model is calculated. so as to urge the 2-D color bar graph
from the 3-D color bar graph, all values within the
illumination axis are accumulated. As a result, eight eight D
sixty four color bins are generated, every thought of in
concert feature.
e) PIGMENT NETWORK FEATURE SET
Pigment network is created by animal pigment or
melanocytes in basal keratinocytes. The pigment network is
that the most vital structure in dermoscopy. It seems as a
network of skinny brown lines over a diffuse brown
background. Dense pigment rings (the network) square
measure because of projectionsofcomplexbodypartpegsor
ridges. The holes square measure because of projections of
dermal papillae. The pigment network is found in some
atypical and skin cancer lesions. In some sitesthenetwork is
widened. It doesn't need to occupy the total lesion .

2.5 CLASSIFICATION
Lesion classification is that the final step. There are many
existing systems that applyvariedclassificationways.during
this framework, 3 kinds of classifiers are planned, i.e. one
level classifier (classifier A) and two-level classifiers
(classifier B and C). the primary stage of this framework isto
perform image process to observe and exclude the hair, at
that time the ROI of the skin lesion is segmental. Then, the
image options are extracted. Next, the extracted options ar
fed to the classifiers.
2.5.1)CLASSIFIER A
This classifier could be a one level classifier; one classifier is
planned to classify the image into 3 classes, benign, atypical
or skin cancer. All extracted options are fed into this
classifier so as to classify the input image.
2.5.2) CLASSIFIER B
This classifier may be a 2 level classifier, 2 classifiers area
unit projected, i.e. classifier I and classifier II. Classifier I
classifies the image into benign or abnormal, andclassifierII
classifies the abnormal image into atypical or skin cancer.
2.5.3) CLASSIFIER C
This classifier could be a 2 level classifier, 2 classifiers area
unit projected, i.e. classifier I and classifier II. Classifier I
detects skin cancer skin cancer} and classifiestheimageinto
melanoma or (benign and atypical),andclassifierIIclassifies
the photographs into benign or atypical. The two-level
classifiers approach offers higher results compared to the
one level classifier, as explained within the experimental
results section. Support Vector Machines (SVM) classifier is
employed altogether classifiers. TheSVMhasbecomea well-
liked classifier algorithmic program recently due to its
promising performance on totally different kind of studies.
The SVM relies on structural risk diminution wherever the
aim is to search out a classifier that minimizes the boundary
of the expected error . In different words, it seeks a most
margin separating the hyperplane and also the nighest
purpose of the coaching set between 2 categories of
information . within the experiments the publically
accessible implementation Lib SVM is employed with radial
basis perform (RBF) kernel sinceit yieldedhigheraccuracies
within the cross-validation compared to different kernels.
The grid search procedure is employed to see the worth of C
and gamma for the SVM kernel.
3. EXPERIMENTAL RESULTS
In the planned system, The dermoscopic pictures were
obtained underneath identical conditions employing a
magnification of twenty. This image info contains of a
complete of two hundred dermoscopic pictures of lesions,
together with eighty benign moles, eighty atypical and forty
melanomas. they're 8-bit RGB color pictures with a
resolution of 768 * 560 pixels. as a result of the info is
anonymous and is employed for coaching functions, no IRB
approval was needed for this study. the pictures during this
info square measure kind of like the picturescaptured bythe
planned system. we have a tendency to determined to use
this info for implementation and take a look at set up since
it's verified and established by a bunch of dermatologists.
Figure ten shows
an example of pictures from the PH2 info and pictures
captured by the planned system. within the experiments,
seventy fifth of the info pictures square measure used for
coaching and twenty fifth square measure used for testing.
The planned framework compared 3 styles of classifiers.
Consequently, Classifier B vanquish classifiers A and C.
Classifier A was able to classify the benign, atypical and skin
cancer pictures with accuracy of ninetythree.5%,90.4%and
94.3% severally. On the opposite hand, the two-level
Classifier B was able to classify the dermoscopy pictures
with accuracy of ninety six.3%, 95.7% and 97.5% severally.
this is often whereas the two-level Classifier C was able to
classify the dermoscopy pictures with accuracy of eighty
eight.6%, 83.1% and one hundred severally. Table four
shows the confusion matrix resultsforClassifierA.Tablefive
shows the confusion matrix for Classifier B (classifier I and
classifier II). Table six shows the confusionmatrixresultsfor
Classifier C (classifier I and classifier II).
FIGURE 6. Sample of images from PH2 database (first column), and
images captured by the proposed device (second column).

TABLE 1. Confusion matrix for classifier A.
The smart-phone application fortheplannedmodel hasbeen
developed and is absolutely useful.additionally,a pilotstudy
on a hundred subjects has been conductedtocapturelesions
that seem on the subjects' skin. This study contains of a
complete of one hundred sixty dermoscopic pictures of
lesions, together with one hundred forty benign moles,
fifteen atypical and five melanomas. they're 8-bit RGB color
pictures with a resolution of 768*560 pixels. as a result of
the info is anonymous and is employed for coaching
functions, no IRB approval was needed for this study. The
results are valid by a medical man from the health sciences
department at the University of metropolis, adding to the
effectiveness and feasibleness of the planned integrated
system. during this experiment we tend to were ready to
classify the benign, atypical and malignant melanoma
pictures with accuracy of ninety six.3%, 95.7% and 97.5%
severally. The experimental results show that the planned
system is economical, achieving terribly high classification
accuracies.
TABLE 2. Confusion matrix for classifier B (classifier I and classifier II).
TABLE 3. Confusion matrix for classifier C (classifier I and classifier II).
4. CONCLUSION AND FUTURE WORK
The incidence of skin cancers has reached an outsized range
of people inside a given population, particularly among
whites, and also the trend remains rising. Early detection is
important, particularly regarding skin cancer, as a result of
surgical excision presently is that the solely life-saving
technique for carcinoma. This paper given the parts of a
system to help within the melanoma interference and early
detection. The planned system has 2 parts. the primary part
could be a period awake to facilitate the users to forestall
skin burn caused by daylight. The part is an automaticimage
analysis module wherever the user are going to be able to
capture the photographs of skin moles and this image
process module classifies underneath that class the moles
fall into; benign, atypical, or skin cancer. associate alert are
going to be provided to the user to hunt medical facilitate if
the mole belongs to the atypical or skin cancer class. The
plannedmachine-controlledimageanalysismethod enclosed
image acquisition, hair detection and exclusion, lesion
segmentation, feature extraction, and classification.
The state of the art is employed within the planned system
for the dermoscopy image acquisition, that ensures
capturing sharp dermoscopy pictures with a hard and fast
distance to the skin and consistent image quality. The image
process technique is introduced to observe and exclude the
hair from the dermoscopy pictures, getting ready itfor more
segmentation and analysis, leading to satisfactory
classification results. additionally, this work proposes an
automatic segmentation algorithmic rule and novel options.
This novel framework is in a position to classify the
dermoscopy pictures into benign, atypical and skin cancer
with high accuracy.specifically,theframework comparesthe
performance of 3 planned classifiers and concludes that the
two-level
classifier outperforms the one level classifier. Future work
would target clinical trials of the planned system with many
subjects over an extended amount of your time to beat the
potential glitches and more optimize the performance.
Another fascinating analysis direction is to analyze the
correlation between skin burncaused bydaylightandneural
activity within the brain.
5. REFERENCES
[1] S. Suer, S. Kockara, and M. Mete, ``An improved border
detection in dermoscopy images for density based
clustering,'' BMC Bio in format., vol. 12, no. 10, p. S12,
2011.
[2] M. Rademaker and A. Oakley, ``Digital monitoring by
whole body photography and sequential digital
dermoscopy detects thinner melanomas,'' J. Primary
Health Care, vol. 2, no. 4, pp. 268 272, 2010.
[3] O. Abuzaghleh, B. D. Barkana, and M. Faezipour,
``SKINcure: A real time image analysis system to aid in
the malignant melanoma prevention and early
detection,'' in Proc. IEEE Southwest Symp. Image Anal.
Interpretation (SSIAI), Apr. 2014, pp. 85 88.
[4] O. Abuzaghleh, B. D. Barkana, and M. Faezipour,
``Automated skin lesion analysis based on color and
shape geometry feature set for melanoma early

detection and prevention,'' in Proc. IEEE Long Island
Syst., Appl. Technol. Conf. (LISAT), May 2014, pp. 1 6.
[5] R. P. Braun, H. Rabinovitz, J. E. Tzu, and A. A. Marghoob,
``Dermoscopy researchAnupdate,'' SeminarsCutaneous
Med. Surgery, vol. 28, no. 3, pp. 165 171, 2009.
[6] A. Karargyris, O. Karargyris, and A. Pantelopoulos,
``DERMA/Care: An advanced image-processing mobile
application for monitoring skin cancer,'' in Proc. IEEE
24th Int. Conf. Tools Artif. Intell. (ICTAI), Nov. 2012, pp.
1 7.
[7] C. Doukas, P. Stagkopoulos, C. T. Kiranoudis, and I.
Maglogiannis, ``Automated skinlesionassessmentusing
mobile technologiesandcloudplatforms,''inProc.Annu.
Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Aug./Sep.
2012, pp. 2444 2447.
[8] C. Massone, A. M. Brunasso, T. M. Campbell, and H. P.
Soyer, ``Mobile teledermoscopy Melanoma diagnosisby
one click?'' Seminars Cutaneous Med. Surgery, vol. 28,
no. 3, pp. 203 205, 2009.
[9] T. Wadhawan, N. Situ, K. Lancaster, X. Yuan, and G.
Zouridakis, ``SkinScan: A portable library for melanoma
detection on handheld devices,'' in Proc. IEEE Int.Symp.
Biomed. Imag., Nano Macro, Mar./Apr. 2011, pp. 133
136.
[10] K. Ramlakhan and Y. Shang, ``A mobile automated
skin lesion classification system,'' in Proc. 23rdIEEEInt.
Conf. Tools Artif. Intell. (ICTAI), Nov. 2011, pp. 138 141.
[11] D. Whiteman and A. Green, ``Melanoma and
sunburn,'' Cancer Causes Control, vol. 5, no. 6, pp. 564
572, 1994.
[12] M. Poulsen et al., ``High-risk Merkel cell carcinoma
of the skin treated with synchronous
carboplatin/etoposide and radiation: A Trans-Tasman
Radiation Oncology Group study TROG 96:07,'' J. Clin.
Oncol., vol. 21, no. 23, pp. 4371 4376, 2003.

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