enhanced skin cancer detection techniques using otsu...
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Volume 5, Issue 5, MAY 2015 ISSN: 2277 128X
International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com
Enhanced Skin Cancer Detection Techniques Using Otsu
Segmentation Method
Harpreet Kaur Aashdeep Singh
Student, Haryana Eng. College Asst. Prof. Haryana Eng. College
Haryana, India Haryana, India
Abstract— Human body suffers from many serious diseases such as lungs disease, intestinal disease, endocrine
disease, etc. One of the most dangerous diseases which are now a day’s founds in many human beings is human
cancer. Cancer is of many forms one of them is skin cancer which arises due to development of abnormal cells having
ability to spread to other body parts skin. Skin cancer is of three types named as- squamous cell cancer (SCC), basal
cell cancer (BCC), and melanoma. The treatment for basal cell and squamous cell is easy. Whereas, Melanoma is
founded as more dangerous and can be fatal if it is not treated. Therefore early finding and treatment of melanoma
skin cancer is necessary. The researches in this field are examined and the technique used to diagnose skin cancer at
its early stage is Otsu method which is a segmentation based technique. This method is used to perform automatic
clustering based image thresholding which reduce gray level image into binary image. As per this method, an image
have two classes of pixels i.e. bi-modal histogram (foreground pixels and background pixels) which calculates the
optimum threshold separates two classes for minimal combined spread or equivalent, so that their inter-class variance
is maximal . Then an obtained result is compared with other results to determine competitiveness. After experiment,
this is computed that this method gives accurate results.
Keywords—skin cancer, Basal cell, Squamous cell, melanoma, segmentation, Otsu method, thresholding.
I. INTRODUCTION
Human body suffers from many serious diseases such as lungs disease, intestinal disease, endocrine disease, etc. One of
the most dangerous diseases which are now a day’s founds in many human beings is human cancer. Human cancer
caused mainly due to accumulation of multiple molecular alterations and genetic instability.
Cancer is of many forms one of them is skin cancer. Skin cancer is a cancer which starts from skin. Skin cancer arises
due to development of abnormal cells having ability to spread to other body parts skin. A person having fair skin are
suffered from such a dangerous disease and is mainly found peoples of Europe, North America and Australia and is
correspond to one third of all cancers which is detected each year and affecting 1 in every 7 people.
Skin cancer is of three types named as-
Squamous cell cancer (SCC),
Basal cell cancer (BCC), and
Melanoma.
The squamous cell cancer (SCC) and basal cell cancer are known as non-melanoma skin cancer (NMSC). Basal cell
cancer grows slowly and can damage the tissue around it. The basal cell cancer is founded as a painless raised area of
skin or shiny with small blood vessel running over it. Whereas, the squamous cell founded as a hard lump with a scaly
top more. It is likely to be more spread cell and may also form an ulcer. Therefore, treatment for basal cell and squamous
cell is easy as compared to melanoma.
Fig-1 Non-melanoma skin cancer (NMSC)
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Melanomas are the most dangerous and aggressive. The signs of Melanoma are a mole which changes in shape, size, and
colour, has irregular edges. Melanoma is founded as more dangerous and can be fatal if it is not treated. Detection of
melanoma is highly curable in its early stages otherwise advanced melanoma is lethal. Melanoma developed due to
ultraviolet radiation i.e. long- term exposure and sun burn, this cause damage to DNA cell. One of the major contributors
to the development of melanoma is ultraviolet radiation (long-term sun exposure and sun-burn) that causes damage to the
cell DNA.
Fig- 2 Melanoma Skin Cancer
It is well-known that early finding and treatment of skin cancer is important, although the success rates of curing skin
cancer are very high. This can reduce the mortality and morbidity of patients. There are several methods of treatment of
malignant melanoma which is mainly depending on the size of the tumour.
As per the statistics that in 90–95 % of cases, if melanoma is removed surgically when its thickness is less than 1mm,
then patient will make a complete recovery. Due to widely increase of malignant melanoma, number of non-invasive
tools has been developed by researchers such as “epiluminescence microscopy (ELM)” or “skin surface microscopy” in
order to improve early diagnose. Both of these methods have different specificity, and accuracy rates in diagnosing.
Therefore, to detect skin cancer at very early stage Digital Dermoscopy is considered as one of the most effective
weapons which is used for identification and classification of skin-cancer. It is non-invasive technique which helps to
detect melanoma at its early stage. This also includes dermoscopy, total body photography, automated diagnostic system
and reflectance confocal microscopy.
Fig- 3 Lesion observations with naked eye and then comparison with dermoscopy image,
this makes local and global visible.
To detect and measure sets of features, dermoscopic images are analysed in computer which can be extremely helpful
and useful for dermatologists for their diagnosis. [7] The dermoscopic images are analysed in computer to measure and
detect sets of features from these images and this can be extremely useful and helpful for dermatologists in order to
facilitate their diagnosis. Therefore, a conclusive aim is to develop to diagnose melanoma on early stages.
Skin cancer detection using Otsu segmentation technique
Every month and year new skin cancer detection and identification techniques are investigated to prevent people from
such a dangerous disease. From a recent research, it is concluded that recognition of skin cancer is possible. For this
images are analysed using very advanced and supervised such as artificial neural networks and fuzzy systems, feature
extraction also k-nearest neighbours (k-NN) that also group pixels based on their similarities where each feature image
can be used to classify the normal/abnormal images.
To diagnose skin cancer every aspect of image is need to monitored and investigated carefully, such as to identify the
edges of an object in an image scene is an important aspect of the human visual system; this gives most important and
Kaur et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
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useful information on the basic topology of the object which is used to obtain an interpretative match. [1]There are many
methods that can be used to detect skin cancer by analysing an image. Therefore, one of the most supervised techniques
used for analysing an image to detect skin cancer is segmentation. Segmentation is a process of partitioning a digital
image into set of pixels. The aim of segmentation is to simplify an image into something that is easier to analyse. For
identification of an, an image is segmented into complex edges. The segmentation process affects the accuracy of the
subsequent steps. It is not easy to segment an image due to variety of sizes, lesion shapes and colours along with different
skin types and textures. [4]0
Some lesions have irregular boundaries, whereas, in some cases there is smooth transition between the lesion and the skin.
To deal with this problem many algorithms have been proposed such as thresholding and edge based or region based
methods.
Thresholding is one of the most simplest and efficient method of image segmentation. It is a process in which gray scale
image is transformed into binary images. For this each pixel of an image is replaced with a black pixel if the intensity of
an image is less than fixed constant or a white pixels if the image intensity is greater that constant. [3]
Another most effective method of image segmentation is edge based or region based method. In this process edges and
regions of an image are analysed depends upon the intensity of region boundary. It is used as a base of segmentation
technique. The desired edges are the boundaries between such objects. The results of these segmentation techniques are
less perfect. Therefore, to obtain more efficient and accurate results, Otsu segmentation technique is used.
Otsu’s method- this method is used to perform automatic clustering- based image thresholding. Or, to reduce the gray
level image into binary image. As per this method, an image have two classes of pixels i.e. bi-modal histogram
(foreground pixels and background pixels) which calculates the optimum threshold separates two classes for minimal
combined spread or equivalent, so that their inter-class variance is maximal .[4] Otsu method is based on discriminate
analysis. This method partitions the image into two classes. Suppose an image is represented in L gray levels
{0,1,2,………L}, similarly Otsu’s thresholding method partitions the image pixels into classes C0= ={0,1,2………t} &
C1={t+1,t+2……………..L-
Let the number of pixels in the gray level be and n
be the total number pixels in a given image. The probability of occurrence of gray level is defined as:
= /
Where, Co and C1 are normally corresponding to the object of intersect and the background, the probabilities of the
classes are W0 and W1,
W0= 0 and W1=
Thus, the mean of the two classes can be computed as:
µ0 (t)= 0
Otsu’s method of thresholding gray level images is efficient for separating an image into two classes where two types of
fairly distinct classes exists in the image.[4]
Fig.4 (a) Grayscale version of RGB image; (b) Segmented image after applying Otsu’s method
Basically, segmentation is categorized in to three categories, i.e. Otsu method, and other two are Gradient Vector Flow
(GVF), and Colour Based Image Segmentation Using K-mean Clustering
Gradient Vector Flow (GVF) - this is a spatial diffusion method in which gradient of an edge is derived from the image.
This is one of the most popular algorithms proposed to use in many GVF snack is well-known medical imaging problems.
The boundary of an object is approximated by an elastic contour X(s) =(X(s), Y(s)), S∈ [0, 1]. This is initialized by the
user or heuristic criteria in the image domain. After this elastic contour is modified as per the differential equation. [4]
/ = )+ )
Where, is an internal force, similar to the one used in traditional snakes tries to keep the shapes continuity and
smoothness and V= (u(x, y), v(x, y)) is the GVF field. The GVF field is a regularized version of edge gradient or image
that allows long range attraction of the contour against the object boundary. [4]
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Fig- 5 (a) Original RGB image; (b) Grayscale version of the RGB image; (c) Segmented image after using GVF method.
Colour Based Image Segmentation Using K-mean Clustering- The technique of Image segmentation techniques is
classified into following basic concepts:
Pixel oriented,
Contour-oriented,
Region-oriented,
Model-oriented, and
Colour-oriented and
Hybrid.
Fig. 6- (a) RGB image; (b) objects in cluster 1; (c) objects in cluster 2; (d) objects in cluster 3.
The segmentation of image on the basis is a difficult operation in image analysis and also in many computer vision,
image interpolation, and pattern recognition system. The performance of colour segmentation significantly affects the
quality of an image understanding system. The segmentation process is categorized into two stages named as-
1. Enhancing colour separation of medical image using decorrelation stretching is carried out
2. After that the regions are grouped into three classes with the help of k-mean clustering algorithm.
II. RELATED WORK
A. Analysing Skin Cancer Using Automatic image analyses method
Human Cancer is a complex disease which occurs mainly due to genetic instability and accumulation of multiple
molecular alterations. Many techniques are used to investigate skin cancer at its early stage. In such a case, use of image
processing for analysing skin cancer is founded as non-invasive technique. Image processing is an automatic image
analysis method that provides valuable information about lesion. This is a process in which skin cancer is identified by
analysing digital images that can reduce unnecessary skin biopsies. To achieve this goal, a method called feature
extraction is used that analyse images appropriately. In this, number of digital images are analysed on the basis of
segmentation technique. Then feature extraction technique is applied on segmented images. A comprehensive discussion
has been explored depending upon the obtained result. [4]
B. Recognition Of Malignant Melanoma Using Advanced Computer Vision System
From last many years, huge changes and advancements has been noticed in medical treatment. Similarly, to diagnose one
of the most dangerous diseases at its early stage computer vision based system is used by various dermatologists. In this
process, first review the installation process, then visual features that used for skin lesion classification and methods used
for defining them. After this, description about extraction of features of digital images using various processing methods
i.e. segmentation method, colour, border and texture processing. To determine effectiveness of methods, an author
compares various methods used for examine features of digital images. [1]
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C. Generalizing Model to Segment Skin Lesion
To perform number of tasks for automatic skin lesion diagnosis, an author purposed a generalized model. A model is
applied on skin lesion segmentation, identification of dermoscopic structure and occluding hairs. Then an obtained result
is compared with other results to determine the competitiveness of method. The model is also hoped to use in many other
tasks such as black frames detection, oil bubbles, etc also to other problem domains. [6]
D. Diagnosing Melanoma Using Computer Aided Developed System
A rapid increase in melanoma patients has been observed from recent years. Thus, effective treatment is needed to
control such a harmful diseases at its early stages. For early detection of melanoma a computer aided system has been
developed. The aim of this paper is to improve performance by developing an interface between two methods i.e.
segmentation method and analytical method. this process, proceeds in three steps, first, removal of noise and unwanted
structures from image by implementing pre-processing, next, locating skin lesion using automatic segmentation approach
and finally, extraction of features using ABCD rules. For this process, 40 images contains suspicious melanoma skin
cancer are required. , the author report an experiment in which he were able to achieve 92% accuracy, reflects viability.
[5]
E. Evaluation of Segmentation Methods For Skin Lesion In Demoscopic Images
A paper is about diagnosing skin lesion by evaluating six segmentation methods named as- adaptive thresholding (AT),
EM level set (EM-LS), Fuzzy based split and merge algorithm (FBSM) and Adaptive snake (AS). These methods are
then applied to more than 80 images and evaluated with four different metrics and computed that EM-LS and AS
methods gives better result which are semi supervised methods whereas, best fully automated method named as FBSM
Gives worst result as compared to AS and EM-LS methods. [3]
III. PURPOSED WORK
Pre-processing - Removal of noise before focal area identification.
- Segmentation by Otsu method.
Features Extraction
- Asymmetry, border irregularity, diameter & colour variation features.
Classification
-In this step region of interest of lesion image is assigned to one of the classes of healthy or cancerous.
IV. RESULTS
As we discussed above that among many forms of human cancer, skin cancer is one of the most dangerous problem and
it need to diagnose at early stages. Therefore to diagnose skin cancer at its early stage we are using feature extraction and
segmentation techniques. Both of these techniques are very effective and fast. In segmentation we are using Otsu method
which clusters the gray image into binary image. Whereas, in feature extraction method ABCD rule is used which
analyze the image on the bases of image symmetry border, color, and dimension. To calculate the effectiveness of
methods, various images of cancer defected skin are investigated using Mat lab tool. Images analyses is passed through 4
modules, named as:-
Open Image
Load mask
Redraw mask
Save mask
Run evaluation.
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Open Image- this is the first module which is used to open the images that we already have. As we click on this module,
it displays some option of images that we want to select.
Selected images is then displayed on the screen
Load mask- After slecting an image that we want to analyze, next option is to load mask. In this module two more
modules can be used in case if we don’t have preloaded mak of an image. After selecting this module, if we have
preloaded mask of particular image then we select the mask as per image, otherwise, select next option i.e. redreaw mask
in which new mask is created and then saved using save mask option.
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As masking of an image is done, image is then run for evaluation to determine the stage of cancer as well as the level of
risk.
V. CONCLUSION
A continue increase in melanoma skin cancer has been observed from last two or three decades. Thus early and effective
detection of skin cancer become necessary. If skin cancer is detected at its early stage, then its treatment becomes easy.
For detection of melanoma skin cancer at its early stages many techniques were proposed by researchers. Some
techniques were not being able to give appropriate and accurate result. In this work, one of the oldest and simplest
methods of segmentation has been discussed, called Otsu method. The result shown by this method are much better then
remaining two segmentation methods. This method requires no changes to the parameters for different skin lesions. This
method performs automatic clustering based image thresholding. The execution speed and accuracy of result of this
method is much better than other two methods of segmentation.
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