Neural Network Classifier for Segmentation and
Classification of Skin Lesions from Digital Images
Jain Elza Jose
Anina John
Department of Computer Science
Department of Computer Science
4th Semester MTech Student
Assistant Professor
Caarmel Engineering College, Perunad
Caarmel Engineering College, Perunad
Abstract- Melanoma is a cancer that make melanin in the
melanocytes. Melanoma tumors are brown or black. There is a
need for an automated system to assess a patient’s risk of
melanoma using images of their skin lesions captured using a
standards digital camera. Locating the skin lesion in the digital
image is a challenging one. The segmentation accuracy is also
less. In the existing system, it segments the skin lesions and
classify the skin as lesion and normal skin. In the proposed
system, texture distictiveness lesion segmentation algorithm is
used to segment the skin lesions. The TDLS algorithm consists
of two steps. First, a set of sparse texture distributions of
normal skin and lesion skin are learned. A TD metric is
calculated to measure the dissimilarity of a texture distribution
from all other texture distributions. Second, the TD metric is
used to classify regions in the image as part of the skin class or
lesion class. Finally a neural network classifier is used to
classify the lesion as benign or malignant. The proposed
framework has higher segmentation accuracy compared to all
other tested algorithms.
superpixels). The goal of segmentation is to simplify and/or
change the representation of an image into something that is
more meaningful and easier to analyze. Image segmentation
is typically used to locate objects and boundaries (lines,
curves, etc.) in images.
Index Terms- Melanoma, neural network, segmentation, texture.
I. INTRODUCTION
Image processing [17] is a signal processing for which the
input is an image, such as a photograph. The output may be
either an image or a set of characteristics or parameters
related to the image. Most image-processing techniques
involve treating the image as a two-dimensional signal and
applying standard signal-processing techniques to it. Image
processing usually refers to digital image processing, but
optical and analog image processing also are possible. This
article is about general techniques that apply to all of them.
The acquisition of images (producing the input image in the
first place) is referred to as imaging. Image segmentation
[5][16] is the process of partitioning a digital image into
multiple segments (sets of pixels, also known as
The result of image segmentation is a set of segments that
collectively cover the entire image, or a set of contours
extracted from the image (see edge detection). Each of the
pixels in a region are similar with respect to some
characteristic or computed property, such as color, intensity,
or texture. Adjacent regions are significantly different with
respect to the same characteristics.
Melanoma [1][2] is a cancer that begins in the melanocytes.
Most of these cells still make melanin, so melanoma tumors
are often brown or black. Melanoma most often starts on the
trunk (chest or back) in men and on the legs of women, but
it can start in other places, too. Having dark skin lowers the
risk of melanoma, but a person with dark skin can still get
melanoma. Melanoma can almost always be cured in its
early stages. But it is likely to spread to other parts of the
body if it is not caught early. Due to the increase in
incidence rates, early detection of melanoma is essential. To
reduce the cost of screening melanoma an automated
melanoma screening [2] algorithms have been proposed.
A dermatoscopes [3] is a handheld device that optically
magnifies, illuminates and enhances skin lesions, allowing
the dermatologist to better view the lesion features. Use of
the dermatoscope has been found to improve diagnosis,
compared to the naked eye. Dermatoscope was used for
screening melanoma by the dermatologists. The main
reasons against using the dermatoscope include a lack of
training or interest. Recent work includes screening the
melanoma from digital images. It includes segmentation
algorithms for locating the lesion border of the skin. This is
important while classifying the lesion based on the features.
The main features include the ABCD scale: asymmetry,
border irregularity, color variegation, and diameter. The
examples of digital images of melanoma are shown in fig.
1(a) and (c).
(a)
(b)
Fig. 1. (a) and (b) are examples of digital images of melanoma.
II. RELATED WORKS
Existing illumination correction algorithms [6] adjust pixel
intensities in an image based on an estimated illumination
map. The goal of these algorithms is to remove any external
illumination, so that the resulting image is independent of
any illumination effects. Some of these effects that should
be removed include shadows and bright areas caused by
illumination variation. The motivation of this preprocessing
step is to improve the performance of subsequent steps,
including lesion segmentation and classiffication. Existing
general illumination correction algorithms focus on
correcting for illumination variation in standard digital
images. These algorithms are general and can be applied to
any image.
Recent work by cavalcanti et al. [6] proposes a correction
algorithm specific for skin lesion images. The algorithm fits
pixel intensities from the four corners of the photograph to a
parametric surface. The disadvantage with this algorithm is
that only a very small subset of pixels is used to fit the
parametric surface. This results in the estimated illumination
map being over-or underestimated. The purpose of image
segmentation algorithms is to find and outline distinct
objects of importance in an image. For example, for images
of skin lesions, the border of the skin lesion should be
identified. Segmentation in general is a very well-researched
area and many different algorithms have been proposed.
Image segmentation [5][16] is perhaps the most studied area
in computer vision, with numerous methods reported. A
segmentation method is usually designed taking into
consideration the properties of a particular class of images.
The majority of algorithms only use features derived from
pixel color to drive the segmentation. Segmentation is
difficult due to the illumination variation. Thresholding
algorithms are used for bright areas where there is a
reflection of camera flash. In preprocessing, a color image is
first transformed into an intensity image in such a way that
the intensity at a pixel shows the color distance of that pixel
with the color of the background. The color of the
background is taken to be the median color of pixels in
small windows in the four corners of the image.
Preprocessing step is to correct shadows and bright spots
caused by illumination variation.
Most of the segmentations algorithms use color variation to
identify the skin lesions. Textures are also used to identify
the skin lesion and normal skin. Since normal skin and
lesion skin has different textures. It includes, smoothness,
roughness, bumps or ridges etc. Stoecker et al. [7] analyzed
texture in skin images using basic statistical approaches,
such as the gray-level cooccurrence matrix. They found that
texture analysis could accurately find regions with a smooth
texture and that texture analysis is applicable to
segmentation and classification of dermatological images.
Existing texture analysis extracts features and measurements
of a texture, allowing textures from different regions to be
compared. Texture analysis is useful for image
segmentation because different parts of the same object will
usually match in texture. Algorithms include using first
level statistics, gray-level co-occurrence matrix or haralick
statistics. Model-based algorithms use probability models,
such as the autoregressive model or markov random field
model [4][8l, to characterize textures. Structural algorithms
deconstruct and characterize the texture as a number of
texture elements. The algorithm proposed by xu et al. [9]
learns a model of the normal skin texture using pixels in the
four corners of the image, which is later used to find the
lesion. Hwang and celebi [10] use gabor filters to extract
texture features and use a g-means clustering approach for
segmenting the lesion.
Fig. 2. Architecture of the System
In this paper, it proposes a texture distinctiveness lesion
segmentation algorithm (TDLS) to locate skin lesion in the
digital images. The TD metric measures the dissimilarity of
texture distributions with all other distributions. Then
classify it as normal skin or lesion skin. A probabilistic
neural network classifier is used to classfy the skin as
benign or malignant. In section III system design, the
process of learning the sparse texture model and calculating
a metric to measure td is described. Finally the lesion is
separated. Then the neural network classifier is used to
classify the lesion as benign or malignant. In section IV
experimental results and discussion about future are
explained.
III. SYSTEM DESIGN
Each row in the neighbourhood is concatenated
sequentially. An unsupervised algorithm is used to learn the
textures. The k-means clustering algorithm is used here. It is
used to find the k clusters of texture data. It is also used to
increase the robustness and to speed up the number of
iterations.
The mean and standard deviations are calculated to find the
TD metric. TD means the texture distinctiveness metric. The
TD metric is used to classify the image as normal skin and
lesion skin. For the normal skin, the dissimilarity of the
textures from other is very small. So the TD metric is also
small for the normal skin. But for the lesion skin, the TD
metric will be large. Because of its dissimilarity from other
textures.
The proposed system includes a texture distinctiveness
lesion segmentation algorithm (TDLS) to learn the texture
distributions and to calculate the TD metric. It is used to
classify the image as normal skin and lesion skin. Then
finally a neural network classifier is used to the lesion as
benign or malignant.
A.
Texture distributions
In texture disributions, the input image will transform the
RGB image to XYZ image. It will convert the pixel values
into XYZ. For each pixel in the image a local texture vector
is obtained. The texture vector contains pixels in the
neighbourhood of size n centered on the pixel of interest.
Fig.3. Map of the texture distinctive metric. In (a) and (c), the original
images are shown. In (b) and (d), maps of the texture distinctive metric
In Fig. 3, it will show the map of texture distinctiveness
metric. Fig. 3 (b) and (d) displays the textural
distinctiveness metric for each pixel in the image. In both
figures, the lesion is white, which means that it has highest
textural distinctiveness metric.
lesion as malignant. The resultant graph is shown in fig.4.
This will shows the better analysis with the existing
classification algorithms.
B. Statistical region merging
In this, first of all the image is oversegmented, that image is
divided into number of regions. For that SRM algorithm is
used. It includes mainly two steps that is a sorting step and a
merging step. In the sorting step , a four connected graph is
constructed. The horizontal and vertical pixels are sorted
based on their similarity. In the merging step, it will merge
the regions based on the pixel intensities. Here the normal
skin and lesion skin are classified.
Fig.4. Performance Analysis Graph
C. Segmentation refinement
In the segmentation refinement, the lesion is refined here.
To refine the lesion border postprocessing are applied. It
includes two steps: morphological dilation and region
selection. The morphological dilation is used to fill the holes
and smooth the border of the lesion. The region selection is
used to select the lesion region and eliminate the unwanted
region. The region which touches the edge of the
photographs can be eliminated. Since the lesion will not
touch the edge.
D. Neural network classifier
In this research, as a future enhancement probabilistic feed
forward neural network classifier is used. In this, the lesion
is segmented. The features such as the area of the lesion,
orientation, major axis length, minor axis length,
eccentricity, centroid, momentum, red average value are
extracted. The training towards the benign and melanoma
are tested. Finally, it is used to classify the lesion as benign
or malignant.
IV. EXPERIMENTAL RESULTS AND DISCUSSIONS
The objective of this experiment is to measure the
classification accuracy of the lesion that is classified as
benign or malignant. This experiment is done with 50
benign images and 50 malignant images. The experiment
shows about 98% accuracy for classifying the lesion as
benign and 99% accuracy for classifying the lesion as
malignant. In the existing systems it is only 80% for
classifying the lesion as benign and 81% for classifying the
V.
CONCLUSION AND SCOPE OF THE WORK
In summary, a novel lesion segmentation algorithm using
the concept of learning is proposed. Texture distinctiveness
lesion segmentation algorithm is used. It captures
dissimilarity between the texture distributions. Then image
is divided into smaller regions and classified as lesion or
skin based on TD map. In future enhancement, it will work
with neural network classifier. The classifier will extract
features and finally classify the lesion as benign or
malignant. Experience and training based learning is an
important characteristic of neural networks that makes it
ideal for diagnosis applications. From the segmented images
neural network learns skin and lesion pixel values. The
proposed framework achieves higher segmentation and
classification accuracy. As a future work, it is expected to
work with border accuracy of benign or malignant image.
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