Melanoma and skin cancers
vs Image Processing
Skin cancer and
melanoma

Skin cancer : most common of all cancers
2
1.
2.
3.
4.
5.
According to the latest statistics available from the
National Cancer Institute, skin cancer is the most
common of all cancers in the United states.
More than 1 million cases of skin cancer are
diagnosed in the US each year.
What’s shown here are some examples of skin lesion
images.
The four images shown on the left are various form
of skin lesions, cancerous or non-cancerous.
The two on the right are a specific form of skin
cancer: melanoma.
3
What is Melanoma?
1.
2.
3.
4.
A type of skin cancer
that starts from
melanocytes
6th leading cause of
cancer death in the
US
No single etiology
Some risk factors
include:
1.
2.
3.
4.
Sun exposure depleting ozone layer
Presence of many or
unusual moles
Skin types
Genetics
predisposition
skin
benign
malignant
Skin cancer and melanoma

Skin cancer : most common of all cancers
[ Image courtesy of “An Atlas of Surface Microscopy of Pigmented Skin Lesions: Dermoscopy”6]
Use of color to distinguish
malignant and benign tumors

Skin tumors can be either malignant
or benign



Classification of skin tumors using
computer imaging and pattern
recognition
1.
2.

Clinically difficult to differentiate the early
stage of malignant melanoma and benign
tumors due to the similarity in appearance
Proper identification and classification of
malignant melanoma is considered as the
top priority because of cost function
Previous texture feature algorithms
successfully differentiate the deadly
melanoma and benign tumor seborrhea
kurtosis
Relative color feature algorithm is
explored in this research for differentiate
melanoma and benign tumors, dysplastic
nevi and nevus
Successfully classify 86% of
malignant melanoma using relative
color features, compared to the
clinical accuracy by dermatologists in
detection of melanoma of
approximately 75%
Types of Melanoma

Superficial Spreading Melanoma


Nodular Melanoma


5 %, sun-exposed area, mistaken for age spot
Amelanotic Melanoma


8 %, Common in dark-skin
Lentigo Maligna Melanoma


15%, dome-shaped nodule
Acral-Lentiginous Melanoma


70%, neck, legs, pelvis
0.3%, non-pigmented
Desmoplastic

1.7%, ½ amelanotic
Benign vs Malignant
9
10
Automated Melanoma Recognition Using
Imaging Techniques
Melanoma is one of the most aggressive cancers, but it can be healed
by surgical excision successfully only if it is recognized in the early
stage.




Since the melanoma emerges as a tiny dot in the topmost skin
layer, it can be examined during routine medical check up.
Although the lesions are accessible, in many cases, it is a difficult
task to make decisions whether nevi are benign or malignant.
Further, frequent use of biopsy is also not encouraged.
Hence, to assist dermatologist's diagnosis, it is useful to develop
an automated imaging-based melanoma recognition system.
11
1.
2.
3.
Uncontrolled growth of melanocytes give rise to dark and
elevated appearance of melanoma.
Neoplasm- growth of tissue, tumor
Melanoma is a type of malignant skin cancer that starts
from melanocytes. It’s caused by uncontrolled growth of
melanocytes that gives rise to tumor.
1.
Nonetheless there are risks factors that highly
attributed to its incidence. Some of the them are:
1.
2.
3.
4.
2.
amount of sun exposure – the more cumulative exposure the
higher
presence of many of unusual mole – people with many moles in
the body
Fitzpatrick’s Skin Type I and II have higher risk –1975 Thomas
Fitzpatrick, Harvard skin typing system based on skin
complexion and response to sun exposure
genetic predisposition – if there history of melanoma that runs
in the family
According to a study ,compared to general population,
people who with 2 risk factors have 3.5 times risk of
developing MM and 20 times those who have 3 or more
risk factors.
1.
2.
3.
4.
These are the types of melanona
As you see, SSM is the most prevalent one that
makes up 70% of most diagnosed melanoma
In this work, images of superficial spreading
melanoma were only explored.
The reason being, and the problem that this
work is trying to solve, Dysplastic Nevi ( a
benign mole) has properties that are highly
similar to this SSM melanoma, which makes the
diagnosis of melanoma difficult.
Melanoma Incidence
Age Adjusted
All Ages, Both Sexes
Cauc
30
Rate per 100,000
25
Af Am.
Asian
20
15
10
NCHS – national center for health
statistics
Bureau of Health Statistics
5
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
0
Incidence highest inYear
Caucasian skin
Graph one- Caucasian has the highest incidence of MM.
Having fair complexion is one of the risk factors.
Researches attribute this to low level of melanin that
absorbs harmful UV radiation in fair skin, thus UV
penetrates much deeper layer affects the surrounding
cells.
Age-Adjusted
All Ages, All Races
Male
Rate per 100,000
50
Female
40
30
20
10
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
0
Year
1.
2.
Graph two – men shows higher incidence than women.
A study of in Germany linked this trend to mutation of
genes called BRAF 4% and CDKN2A 1%.
Melanoma Incidence
Age-Adjusted
All Races, Both Sexes
<20
90
80
Rate per 100,000
70
60
20-49
50-64
65-74
Graph thee –
Incidence increases
with age. Link to
cumulative sun
exposure
>74
50
40
30
20
10
0
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Incidence increases with age
Some studies
suggested that
people who had
significant exposure
to UV at younger age
have higher risk in
later age when UV
exposure decreases.
1.
2.
3.
4.
Age-adjusted- distribution of age by percentage
It’s a way of data normalization so that you can
compare two different countries, cities and so
forth
Need standard population distribution
Who use it


5.
NCHS – national center for health statistics
Bureau of Health Statistics
What to say
1.
2.
So these are three graphs that show melanoma
incidence in different dimensions: based on race,
gender, and age.
Here, it’s evident that Melanoma has its favorites, so
to speak.
Melanoma Incidence
Percent Increase
SEER US Population Melanom a Incidence
Age-Adjusted
14
Percent (%)
12
10
8
6
4
2
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
0
Year
It is estimated that 62,480 men and
women (34,950 men and 27,530
women) will be diagnosed with and
8,420 men and women will die of
melanoma of the skin in 2008 (SEER)




Surveillance Epidemiology and End Results
What to say
This is the combination of all of the data
from the previous slides.
Average of 4.2 percent increase per year
Survival Rate by Stage
The American Joint Committee on Cancer (AJCC) TNM System
5- and 10-Year Survival Rate
40,0000 between 1988-2001
120
5 Year
100
Percent (%)
10 Year
80
60
40
20
0
I
IA
IIA
IIB
IIC
IIIA
Melanom a Stage
http://www.cancer.org
IIIB
IIIC
IV
1.
2.
3.
4.
The imaging is performed by a special CCD camera
combined with an epiluminescence microscope in
order to produce digitalized ELM images of the skin
lesions.
Once the images are captured, the lesion has to be
segmented from the background and useful
information should be extracted from the lesion
region.
Based on the extracted features, decisions have to
be made about the nature of the skin lesion.
The decisions should be supported by descriptive
justifications so that dermatologist can understand
22
the decision making process.



Contact person: Assoc. Prof.
PonnuthuraiNagaratnamSuganthan, email:
epnsugan@ntu.edu.sgTel: 6790-5404
Collaborators: Prof. C L Goh, MD, National Skin Centre,
Singapore & Dr. H Kittler, University of Vienna
This is an on-going project. We have implemented the
segmentation, feature extraction and clasifcation modules
satisfactorily, although further improvements are desirable.
The module to provide explanations supporting the
classifcation decisons is yet to be developed. siii
23
Skin cancer and melanoma
Skin cancer : most common of all cancers
 Melanoma : leading cause of mortality
1. Although represent only 4 percent of all skin cancers in the US, melanoma is
(75%)
the leading cause of mortality.

2. They account for more than 75 percent of all skin cancer deaths.
24]
[ Image courtesy of “An Atlas of Surface Microscopy of Pigmented Skin Lesions: Dermoscopy”
Skin cancer
and melanoma



The time line shown here is the 10 year survival rate of melanoma.
1. If caught in its early stage, as seen here, melanoma can often be
cured with a simple excision, so the patient have a high chance to
recover. Hence, early detection of malignant melanoma
significantly reduces mortality.
Skin cancer : most common of all cancers
Melanoma : leading cause of mortality (75%)
Early detection significantly reduces mortality
25]
[ Image courtesy of “An Atlas of Surface Microscopy of Pigmented Skin Lesions: Dermoscopy”
Dermoscopy view
Clinical View
26]
[ Image courtesy of “An Atlas of Surface Microscopy of Pigmented Skin Lesions: Dermoscopy”
Dermoscopy
1.
2.
3.
4.
5.
Dermoscopy is a noninvasive imaging technique, and it is
just the right technique for this task.
It has been shown effective for early detection of
melanoma.
The procedure involves using an incident light
magnification system, i.e. a dermatoscope, to examine
skin lesions.
Often oil is applied at the skin-microscope interface.
This allows the incident light to penetrate the top layer of
the skin tissue and reveal the pigmented structures
beyond what would be visible by naked eyes.
27
Dermoscopy



Dermoscopy improves diagnostic accuracy by 30%
in the hands of trained physicians
May require as much as 5 year experience to have
the necessary training
Motivation for Computer-aided diagnosis (CAD) of
pigmented skin lesion from these dermoscopy
images.
Clinical view
Dermoscopy view
28

In the future, with the
development of new algorithms
and techniques, these computer
procedures may aid the
dermatologists to bring medical
break through in early detection of
melanoma.
29







40,000 people between 1988-2001
Cancer stage is categorized into TNM level
T – tumor ( localized)
N – regional lymph-nodes
M -Metastasis
The key point is the earlier the better of survival
5- and 10- year survival mean percentage of
people who live at least 5 and 10 years
respectively after being diagnosed
Diagnosis
- ABCDE
System
1. E= evolution/elevation
2. What to say
3. ABCDE system is the tool for detecting melanoma. This is
a list of criteria that can be used for distinguishing
between benign and malignant melanocytic skin lesions.
4. A- if you draw a line across the center of MM, you’ll see
that is not symmetric compared to regular mole
5. B- the border is uneven or ragged is a sign of melanoma
6. C-if there are multiple shades of pigment is presence
7. D- diameter > 6mm
8. Dermatologist adds E for either evolution if lesion
changes upon observation or E for elevation.
9. Suspicious lesion is followed by histological confirmation.
Where the problems lie
Atypical nevi acquire several properties similar to
melanoma, their recognition posed high difficulties even
to experts. The classical ABCD guidance is not reliable
therefore cannot be used as sole indicator for detection
of melanoma for both clinical and public examination.
In clinical setting, recognition and discrimination are
highly subjective with rate of success based on experts’
years of experience. As was found, inexperienced
dermatologists showed decrease sensitivity in the
detection of melanoma in both live and photo
examinations.




General practitioner – 62% sensitivity and 63% specificity
Dermatologist – 80% sensitivity and 60% specificity








OK, so we have the ABCD diagnosis tool plus the experts.
So anyone with sort of skin lesion can step in a clinic get the
ABCD tool and experts examination undertaken then there
you have the results.
You either have benign mole or malignant melanoma at the
end of the consultation. Everything just goes as plan.
Unfortunately it is not always the case.
Sensitivity – TP/TP+FN
Specificity – TN/TN+FP
Read the bullet
The objective of the this work is to address these problems
Here you have
some samples
of MM on the
top row and DN
on the bottom
row
Atypical Nevi
(mole) – shares
some
sometimes all
characteristics
of MM.
MM and DN
ABCD Rules
Malignant Melanoma
Dysplastic Nevi
This actually
what makes
melanoma
detection
difficult.
Objectives
To construct an automated, image-based system for
classification of Malignant Melanoma and Dysplastic Nevi
using solely the visual texture information of the lesion.
The system will be based on methodologies that emanate
and/or correlated with human vision therefore will closely
emulates human experts only with greater extent of
accuracy, reliability and reproducibility
 Investigate new segmentation methods that will be
effective on both lesions
 Extract most relevant texture information from the
image
 Construct a classification system of the lesion
1.
2.
Ultimate goal is the construction
of the classification system
The uniqueness of the system is the fact
that :


only texture information is used – robust in
color variability
Methodologies used through out the whole
process emanate from the human vision thus
emulate human expert
Systems, Materials and Tools

Image database

Original tumor images



Border images


512x512 24-bit color images digitized from 35mm color
photographic slides and photographs
160 melanoma, 42 dysplastic, and 80 nevus skin tumor images
Binary images drawn manually and reviewed by the
dermatologist for accuracy
Software

CVIPtools


Computer vision and image processing tools developed at our
research lab
Partek

Statistical analysis tools
CVIPtools
Other approach Texture
System for Melanoma
Detection
Outline of the Process
1. Here you have the outline
of the process
2. Each of the subsequent
step is dependent of the
of the preceding steps. In
other terms, the results of
subsequent step is only as
good as the results of
preceding steps.
3. Therefore, since
segmentation is the top
most of the hierarchy, its
important to make sure
the method is robust.
Hypotheses



Due to observable pattern disruption in the skin tissue
driven by the MM, It is hypothesize that measuring
magnitude of pattern disruption provides discriminative
features for diagnosing MM.
Since visual texture is highly length-scale dependent, It
is hypothesized that the detection and analysis methods
that explore texture at different scales such as the
wavelet is the most appropriate approach.
It is hypothesized that texture descriptors that emanate
from and highly correlated with human vision system
provide the utmost representation, and thus yield a more
contextual system—a system that closely emulate
human expert







Item one – skin has distinct uniform pattern (glyphic
pattern).
MM disrupts texture.
Quantifying texture differences between MM and NV is
more reliable method than color-based ( color-based
in prone to variability in imaging system)
Item two – texture come in different sizes.
Detection method that explore texture image at
different possible scale is more sensitive than methods
that are using one scale.
Example of this snake-based ( gradient-based),
Normalized Cut, histogram threshold



Item three – there are many texture descriptors
that are purely algorithmic that may not
necessary correlate with human vision.
One example is first-order statistics of texture (
variance ,mean), structure-based approach,
laplacain of Gaussian.
Texture classifiers that emanate from or highly
correlated with human visual system provides a
closer approximation of experts perception of
texture.
Visual
Texture
Texture
Technical Definition






a.
Texture is regarded as what constitutes a macroscopic region. Its structure is simply
attributed to the repetitive patterns in which elements or primitives are arranged according
to a placement rule(Tamura et al, 1978).
Texture is both the number and types of its (tonal) primitive and their spatial arrangement
(Haralick ,1979).
The term texture generally refers to repetition of basic texture elements called texels. The
texel contains several pixels, whose placement could be periodic, quasi-periodic, or random.
Natural textures are generally random, whereas artificial textures are often deterministic or
periodic. Texture may be course, fine, smooth, granulated, rippled, regular, irregular, or
linear (Jain, 1989).
Texture is intuitively viewed as descriptor in providing a measure of properties such as
smoothness, coarseness, and regularity (Gonzales and Woods, 1990).
Texture is an attribute representing the spatial arrangement of the gray levels of the pixels
in a region (IEEE, 1990).
Texture is both grey level of a single pixel and its surrounding pixels, which was coined as a
unit texture, texels. These texels conformed repetitive patterns that dictated the effective
texture analysis approach (Karu et al, 1996).
Patterns which characterize objects are called texture in image processing (Jähne, 2005).
1.
2.
3.
4.
5.
6.
7.
Texture has no single definition.
Definitions from previous literature dedicated in
studying texture
The first three definitions, tells us texture is composed
of a building block that is spatially arranged based on
the placement rule (periodic, quasi periodic, or
random): like a brick a single brick is the building block,
the arrangement of the bricks that gives rise to a
texture of a brick wall
Texture is descriptors for smoothness, coarseness, and
regularity
In computer vision
Spatial arrangement of gray levels of the pixel
Pattern
Texture and Human Vision System
Pre-attentive visual system-1962-1981
 Dr. Julesz



Statistical approach


Neuroscientist
Texture perception
Disproved conjecture that second-order is processed
in the vision system
Textons



Contrast
Terminator-end of lines, corners
Elongated blobs of different sizes - granularity
1.
2.
3.
4.
5.
6.
As one of the hypothesis. Texture characterization emanate from
visual system closely emulates experts
Neuroscientist, studied perception of texture
Before disproving, he conjectured that second-order statistics is
processed in the vision system, and He claimed that two textures
with similar second-order statistic is not pre-attentively recognizable.
In other words without close inspections, two different texture with
same sec stat would seem to look similar.
After series of experiments, he finally suggested that textons are the
major player for texture discrimination.
And the textons are contrast, terminators. granularity
Texture discrimination
Textons instead of second-order statistics that cause the
texture discrimination
Second-order statistics
Textons
The image on left is an example of two different textures
with the same SO that is not pre-attentively detectable.
The right image is two different textures with the same SO
but pre-attentively detectable.
Among others this leads to the final statement texture
discrimination is made possible through the textons.
Here in this one is the difference termination of the two
texture elements .
In this work, the second-order statistics CoM and contrast
of edge elements will be explored for extracting visual
texture properties of skin lesion.
Texture and Human Vision System
Frequency and Orientation
 Multi-frequency and orientation analysis
decomposition (1968) –Campbell and Robson
 Simple cells of the visual cortex respond to narrow
ranges of frequency and orientation, cells act as 2D
spatial filter-(1982) De valois et al.
 Orientation-based texture segregation involves the
generation of a neural representation of the surface
boundary whose strength is nearly independent of the
magnitude of orientation contrast - Motoyoshi and
Nishida (2001)





More studies had been conducted in part to understand human vision.
This Campbell and Robson found that when signal received by the eye is
decomposed into multiple frequencies and orientation
Another work in the subsequent year that further support the previous
finding that simple cells are highly selective/tuned to narrow frequency
and orientation.
Another work found that neural representation of texture boundary is
formed that is independent of magnitude and orientation of the contrast
In this work in wavelet analysis will be used for segmentation. Frequency
and contrast
Texture
Method Design





Creation of relative color
images
Segmentation and
morphological filtering
Relative color feature
extraction
Design of tumor feature
space and object feature
space
Establishing statistical
models from relative color
features
COLOR
Create Relative Color Skin
Tumor Images

Purpose




Algorithm




to equalize any variations caused by lighting,
photography/printing or digitization process
to equalize variations in normal skin color
between individuals
the human visual system works on a relative
color system
Mask out non-skin part in the image to calculate
the normal skin color
Separate tumor from the image
Remove the skin color from the tumor to get a
relative color skin tumor image
CVIPtools functions were used to create
relative color skin tumor images
Calculate Skin Color
Original
Noisy Skin
Tumor Image
Non-skin Algorithm
Skin Tumor
Image W/O
Noise
Mask
out
tumor
Average
R, G, B Value
of Skin
Calculate
Skin-Only
Image
Tumor Image
Original
Noisy Skin
Tumor Image
AND
Border
Image
Tumor
Image
Relative Color Tumor Image
Tumor
Image
SUBTRACT
Average
R, G, B Value
of Skin
Relative
Color Image
of the Tumor
Segmentation and
Morphological Filtering



Image segmentation was used to find regions that represent
objects or meaningful parts of objects
Morphological filtering was used to reduce the number of
objects in the segmented image
Easy to use CVIPtools for experimenting and analysis
Feature
Extraction
Relative Color Feature Extraction



Necessary to simplify the raw image data into higher
level, meaningful information
Feature vectors are a standard technique for classifying
objects, where each object is defined by a set of
attributes in a feature space.
Totally 17 color features and binary features were
extracted using CVIPtools


The three largest objects, based on the binary feature ‘area’,
were used in feature extraction
Histogram features, that is, color features, were extracted in
each color band from relative color image objects
17 Features

Binary features

Area
Area   I (r , c)
r


Histogram features in R, G, B bands

Mean  
c
Thinness
Area 

Thinness  4 
2 
 Perimeter 
Mean
r

c
I (r , c)
M
Standard deviation
   ( g  g ) P( g )
Skewness
L 1
2
g

g 0
Skewness 

Energy
1
 g3
L 1
L 1
 (g  g )
3
P( g )
g 0
Energy   P( g )
2
g 0

Entropy
L 1
Entropy   P( g ) log 2 P( g )
g 0
17 Features (Cont.)
Design Two Feature Spaces

Tumor feature space



consists of 277 feature vectors correspond to 277 skin tumor
images.
each feature vector has 51 feature elements, which are the total
of 17 features of each three largest objects within the same
tumor.
Object feature space


had 842 feature vectors corresponding to 842 image objects
each feature vector has 17 feature elements, which were the
binary features and color features stated as above
Establishing Statistical Models

Two feature spaces serve as two data models in order to maximize
the possibility of success

Two classification models, Discriminant Analysis and Multi-layer
Perceptron, were developed for both data models

The training and test paradigm is used in statistical analysis to
report unbiased results of a particular algorithm


due to small size of data set, 282 images, we used the leave x out
method, with both one and ten for x
Partek software was used


to analyze the data representing the features
to develop a model or rules for classifying the tumors
Quadratic Discriminant
Analysis
1.
2.
A statistical pattern recognition technique based on Bayesian
theory, which classifies data based on the distribution of
measurement data into predefined classes
Normalization the feature data as preprocessing
1.
3.
performed to maximize the potential of the features to separate classes
and satisfy the requirement of the modeling tool such as Quadratic
discriminant analysis for a Bayesian distribution of the input data
Variable selection was used to choose dominant features.
07/13/2005
Computer Vision and Image
Processing Research Lab @ ECE
Dept., SIUE
Multi-Layer Perceptron

A feed forward neural network



neural networks modeled after the nervous system in biological
systems, based on the processing element the neuron
widely used for pattern classification, since they learn how to
transform a given data into a desired output.
Principal Component Analysis (PCA) as preprocessing


a popular multivariate technique, is to reduce dimensionality
by extracting the smallest number components that account
for most of the variation in the original multivariate data and
to summarize the data with little loss of information
the dispersion matrix selected for PCA in this project is
correlation
Multi-Layer Perceptron (Cont.)

Creation, training and testing of neural networks

Creation a neural network involves selection of hidden and output
neuron types and a random number generation.



Scaled Conjugate Gradient algorithm is used for learning in this project.





Four output neuron types – Softmax, Gaussian, Linear and sigmoid
Three hidden neuron types – Sigmoid, Gaussian and Linear
Automated and independent of user parameters
Avoids time consuming
Stopping criteria, sum-squared error, is selected to determine after how
many iterations the training should be stopped
The trained data is then tested on itself first to examine how far the
neural network is able to classify the objects correctly.
Leave x partition out method is used for testing the algorithm
Experiments and Analysis in
Object Feature Space

Discriminant Analysis

Number of
Histogram
Features
Area
8
X
9
8, 9, 11 and 12 significant features were selected
respectively for leave one out method
Mean
R
G
STD
Skewness
G
B
R
G
Entropy
B
R
G
B
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
11
X
X
X
X
X
X
X
X
X
X
X
12
X
X
X
X
X
X
X
X
X
X
X
X
R
Energy
B
R
G
B
X
Experiments and Analysis in
Tumor Feature Space

Histogram
Features
Object 1
Discriminant Analysis

R
X
24
features
selected
for leave
ten outEntropy
Mean
STD
Skewness
Energy
method
G
B
R
G
B
R
G
B
R
G
B
R
G
X
Object 2
X
Object 3
X

R
G
Object1
X
X
Object 2
X
07/13/2005
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
10 features selected for leave one out method
Histogram
Features
Object 3
X
B
Mean
STD
B
R
B
R
G
Energy
B
R
G
Entropy
B
R
G
B
X
X
X
G
Skewness
X
X
Computer Vision and Image
Processing Research Lab @ ECE
Dept., SIUE
X
X
Experiments and Analysis in
Tumor Feature Space (Cont.)

07/13/2005
Discriminant Analysis (Cont.)
Computer Vision and Image
Processing Research Lab @ ECE
Dept., SIUE
Experiments and Analysis in
Tumor Feature Space (Cont.)



Multi-layer Perceptron
Best features,
being in the first
three components
of the PCA
projection data,
were used
Success
percentages of
melanoma as high
as 77% and nevus
is as high as 68%
07/13/2005
Computer Vision and Image
Processing Research Lab @ ECE
Dept., SIUE
Experiments and Analysis in
Object Feature Space (Cont.)


Discriminant Analysis (Cont.)
Yield consistent
results in
classifying
melanoma from
other skin tumor
with above 80%
success rate
Experiments and Analysis in
Object Feature Space (Cont.)





Multi-layer Perceptron (MLP)
5 out of 12 hiddenoutput layer neuron
combinations gave
better classification
results
Leave one out method
Yield success
percentage as high as
86% for classifying
melanoma.
MLP is more consistent
in classifying melanoma
as well as nevus
07/13/2005
Conclusion

Multi-Layer perceptron (MLP) with feature data
preprocessed by Principal Component Analysis (PCA) gave
better classification results for melonoma than
Discriminant Analysis (DA)


The best overall successful rate of 78%, of which percentage
correct of melanoma is 86%, nevus is 62% and dysplastic is
56%.
The best classification results are achieved with sigmoid used
as the hidden and output layer neuron type for the MLP with
PCA on Object Feature Space.

The three largest tumor objects are representative for the
whole skin tumor.
Conclusion (Cont.)


However the small percentage of melanoma misclassification as well
as the relatively low success rate for nevus and dysplastic nevi
suggests that we may not have the complete data set for the
experiments.
In order to achieve better classification results, future experiments



Needs more complete skin tumor image database.
Should combine texture and color methods to get better results
Will include dermoscopy images
Acknowledgement





Dr. Scott E Umbaugh, SIUE
Mr. Ragavendar Swamisai
Ms. Subhashini K. Srinivasan
Ms. Saritha Teegala
Dr. William V. Stoecker, Dermatologist,
UMR
07/13/2005
Computer Vision and Image
Processing Research Lab @ ECE
Dept., SIUE
Thank You!
Yue (Iris) Cheng
Graduate Student
@
Computer Vision and Image Processing Research Lab
Electrical and Computer Engineering Department
Southern Illinois University Edwardsville
E-mail: cheng@westar.com
https://www.ee.siue.edu/CVIPtools
07/13/2005
Computer Vision and Image
Processing Research Lab @ ECE
Dept., SIUE
CLASSIFICATION OF MALIGNANT
MELANOMA AND DYSPLASTIC NEVI
USING IMAGE ANALYSIS: A VISUAL
TEXTURE APPROACH
Dr. Dinesh Mital
University of Medicine and Dentistry of New Jersey
School of Health Related Profession
Biomedical Informatics
March 2009
Color-based Diagnosis:
Research
Project Funded
In Part by NIH
Clinical
Images
Yue (Iris) Cheng, Dr. Scott E Umbaugh
@
Computer Vision and Image Processing Research Lab
Electrical and Computer Engineering Department
Southern Illinois University Edwardsville
E-mail: cheng@westar.com
https://www.ee.siue.edu/CVIPtools
Spatially Constrained Segmentation of
Dermoscopy Images
Howard Zhou1, Mei Chen2, Le Zou2, Richard Gass2,
Laura Ferris3, Laura Drogowski3, James M. Rehg1
1School
of Interactive Computing, Georgia Tech
2Intel Research Pittsburgh
3Department of Dermatology, University of Pittsburgh
91