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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)

http://www.ijarcsse.com/

http://en.wikipedia.org/wiki/Squamous_cell_cancer

http://en.wikipedia.org/wiki/Basal_cell_cancer



http://en.wikipedia.org/wiki/Melanoma



http://en.wikipedia.org/wiki/Melanoma




http://en.wikipedia.org/wiki/Telangiectasia

Kaur et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),

May- 2015, pp. 756-763


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


May- 2015, pp. 756-763


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]


May- 2015, pp. 756-763


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]


May- 2015, pp. 756-763


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.


May- 2015, pp. 756-763


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.


May- 2015, pp. 756-763


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.

REFRENCES

[1] Apoorva Raikar, Asst. Prof. S. P. Sangani, Asst .Prof. K D. Hanabaratti, Diagnosis of Melanomas by Check-list

Method, 4th ICCCNT – 2013 July 4 - 6, 2013.

[2] Ilias Maglogiannis, and Charalampos N. Doukas, Overview of Advanced Computer Vision Systems for Skin

Lesions Characterization, IEEE transactions on information technology in biomedicine, vol. 13, no. 5,

September 2009

[3] Margarida Silveira, Jacinto C. Nascimento, Jorge S. Marques, André R. S. Marçal, Teresa Mendonça, Syogo

Yamauchi, Junji Maeda, and Jorge Rozeira, Comparison of Segmentation Methods for Melanoma Diagnosis in

Dermoscopy Images, IEEE journal of selected topics in signal processing, vol. 3, no. 1, February 2009

[4] Md.Amran Hossen Bhuiyan, Ibrahim Azad, Md.Kamal Uddin, Image Processing for Skin Cancer Features

Extraction, International Journal of Scientific & Engineering Research Volume 4, Issue 2, February-2013

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[5] Nadia Smaoui, Souhir Bessassi, A developed system for melanoma diagnosis, International Journal of Computer

Vision and Signal Processing, 3(1), 10-17(2013).

[6] Paul Wighton, Tim K. Lee, Harvey Lui, David I. McLean, and M. Stella Atkins, Generalizing Common Tasks in

Automated Skin Lesion Diagnosis, IEEE transactions on information technology in biomedicine, VOL. 15, NO.

4, JULY 2011

[7] Rajvi Parikh, Dr Hitesh shah, A Survey on Computer Vision Based Diagnosis for Skin Lesion Detection,

International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 2, March

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[8] G. Di Leo, C. Liguori, A. Paolillo, P. Sommella, An improved procedure for the automatic detection of

dermoscopic structures in digital ELM images of skin lesions, IEEE International Conference on Virtual

Environments, Human-Computer Interfaces, and Measurement Systems Istambul, Turkey, 14-16 July 2008.

[9] Stolz W, Riemann A, Cognetta AB. Eur J Dermatol, ABCD rule of dermatoscopy: a new practical method for

early recognition of malignant melanoma, 1994, Vol. 4, pp. 521-527.

[10] Ramandeep Kaur, Gurmeen Kaur, Enhancement and Detection of Melanoma in Skin Images Using Local Binary

Pattern (LBP), Volume 5, Issue 4, 2015 ISSN: 2277 128X International Journal of Advanced Research in

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[12] H. Ganster, A. Pinz, et al, Automated melanoma recognition, IEEE transaction on Medical Imaging, 2001, Vol.

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Enhanced Skin Cancer Detection Techniques Using Otsu ...ijarcsse.com/Before_August_2017/docs/papers/Volume_5/5...Enhanced Skin Cancer Detection Techniques Using Otsu ... ... [5]