ABSTRACT

Image
segmentation techniques is broadly utilized as a part of  medical image currently. In medical field the
segmentation plays an vital part helping doctors to take appropriate decisions.
Breast cancer is  the most widely
recognized sort of cancer in ladies worldwide and all the more so in India Mammography
plays an important role in the detection of breast cancer.Segmentation of image
plays an key role in practical applications such as medical science. Thresholding
is an important technique for image segmentation.Among all segmentation method,Otsu
method is one of the most standout methods for image thresholding.Clustering is
one of the methods used for segmentation. Clustering approach is widely used in biomedical image
segmentation and it is application are used for  breast cancer
detection to find out the tumor on the breast.The k-means is one of the
basic clustering algorithm which is commonly used in several applications. This
paper compares two methods for image segmentation Otsu method and k means
method to detect breast cancer.The comparisons of both techniques are based on
segmentation parameters such as mean square error, peak signal-to-noise ratio
to find the best technique to detect breast cancer.

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Keywords:Breast
Cancer,Otsu Thresholding,K-Means clustering,PSNR,MSE,Time Accuracy

 

I.INTRODUCTION

Due
to advent computer technology image processing techniques have been
increasingly important in a wide variety of applications.Image segmentation is
a classic subject in the field of image processing and also is a hotspot and
focus of image processing techniques.Segmentation of the varied components
among the particles is extremely vital to medical call.The complete objective
of this segmentation is known as computer aided diagnosis which is used by
doctors in evaluating medical images or in recognizing abnormalities in a
medical image.Image segmentation technique is used to separate the foreground
from the background.image segmentation is one of the hardest research problems
in computer vision industry. Breast cancer is the most common type of cancer in
women worldwide and more so in india.About 10% of women are confronted with
breast cancer in their lives.Early detection and diagnosis of breast cancer
using digital mammography and image processing can increase survival rate and
medication can be given in proper time for complete recovery.Detecting breast cancer can be quite a challenging job.
Specially, as cancer is not a single disease but is a collection of multiple
diseases. Thus, every cancer is different from every other cancer that exist.
Also, the same drug may have different reaction on similar type of cancer.
Thus, cancer vary from person to person. Depending on only one technique or one
algorithm to detect breast cancer may not provide us with the best possible
result. As one cancer differ from another, similarly every breast appears
differently from another. The mammography image can also be compromised if the
patient has undergone some breast surgery. Breast Cancer has been a big topic
in research field for the last two decades. It has been well funded medical
research topic across the globe. Many people have been cured of it, due to
early detection. Still the progress in diagnosis and treatment for it remains
expensive and time consuming. Automated detection of mass still remains a
difficult task, this might be due to the fact that every cancer is different
like it’s host and each requires customized medication to be cured. So, a lot
of work is still left to be done.The only current means of early
detection of breast cancer is through regular mammography screening.Through
mammogram analysis radiologists have a detection rate of 76%-94%,which is
considerably higher than the 57%-70% detection rate for a clinical breast
examination.Mammography has been considered as the most important technique to
investigate the breast cancer and it is the currently best method for detecting
breast cancer at its early stage.Thresholding is often used to separate objects
from the background.One of the commonly used method,the otsu method improves
the image segmentation effect considerably.The segmentation process was
performed by using  threshoding method,Otsu  which generates the threshold value which was
automatically implemented on the mammogram images with the aim to separate the
breast area form its background. Clustering is a method of grouping data
objects into different groups,such that similar data objects belong to the same
group and dissimilar data objects to different clusters. Current research
increasing interest in digital image searching,classification,identification,management
and storage.An
efficient clustering algorithm is used for the medical image for the k-means
algorithm in image segmentation. Medical image segmentation had been a vital organ  of research as it inherited
complex problems for the proper diagnosis of incomplete developed mind. K-means
clustering is a key technique in pixel-based methods. Because pixel-based
methods based on K-means clustering are simple and the computational complexity
is relatively low compared with other region-based or edge-based methods, the
application is more practicable. Furthermore, K-means clustering is suitable
for biomedical image segmentation as the number of clusters is usually known
for images of particular regions of the human anatomy

The performance analysis has been carried out
for two segmentation techniques Otsu Thresholding and K-means clustering.By
comparing both the techniques the k means clustering provides the best result
for mammogram images.

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