Different Image Pre-Processing and Feature Extraction Techniques for Breast Cancer Detection in LabV

IJSTE - International Journal of Science Technology & Engineering | Volume 4 | Issue 7 | January 2018 ISSN (online): 2349-784X

Different Image Pre-Processing and Feature Extraction Techniques for Breast Cancer Detection in Labview Bhagyashri K. Yadav M. Tech Student Department of Electrical Engineering Veermata Jijabai Technological Institute, Mumbai, India

Dr. Prof. M. S. Panse Professor Department of Electrical Engineering Veermata Jijabai Technological Institute, Mumbai, India

Abstract The Image pre-processing plays an important role in Breast cancer detection. Mammography images are usually preferred for breast cancer detection analysis. Images should be pre-processed to remove the noise, preserve the edges of an image, and enhance the image quality for carrying out further image analysis. The filtering techniques available for image pre-processing are an average filter, adaptive median filter, median filter. This paper provides various pre-processing techniques for breast cancer detection using noisy MIAS images. The pre-processed results are evaluated by feature extraction.

Keywords: Breast cancer, Pre-processing, Mammography, MIAS Data, Median Filter ________________________________________________________________________________________________________ I.

INTRODUCTION

Breast Cancer cells are characterized by unrestrained division principal to abnormal growth & the aptitude of these cells to arrive in normal tissue locally or to spread through the body this evolution called metastasis. Breast cancer is a type of cancer originating from breast tissue, commonly from. Hence early detection of breast cancer is essential in effective treatment and in reducing the number of deaths caused by breast cancer. There are mainly two types of breast cancer, a ductal carcinoma is the most common cancer where cancer begins in the milk duct and the second is cancer that begins in the lobules are called lobular carcinoma[1].The Image processing plays an important role in cancer detection.

The image pre-processing is carried out by various filtering techniques to enhance the image quality, preserves the edges and remove the noise present in the image database .The pre-processing produce better quality image which is used for feature extraction for further analysis.

Images and Database The Mammography Image Analysis Society (MIAS) is an organization of UK research groups which have produced a digital mammography database Images are in gray scale file format (PGM – Portable Gray Map) .The original MIAS Database is digitized at 50 micron pixel edge and every image is 1024 pixels x 1024 pixels known as the mini-MIAS database and .The Selected mini-MIAS database as it contains abnormalities in each mammographic image .

II. PRE-PROCESSING The purpose of the pre-processing stage is to improve quality of the image, remove background noise and pictorial muscles present in the image. The pre-processed image is making ready for feature extraction process. The accuracy of cancer detection is improved after pre-processing of the image. The different filtering techniques which are used for pre-processing are as below:Median Filter It is a non- linear filter which is effective in removing the salt and pepper noise. The median inclines to keep the sharpness of an image.

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Different Image Pre-Processing and Feature Extraction Techniques for Breast Cancer Detection in Labview (IJSTE/ Volume 4 / Issue 7 / 009)

Fig. 1: Original Image

Fig. 2: Median Filterd Image

Gaussian Smoothing/Gaussian Filter The result of smoothing is to blur an image and it is same as like the mean filter.The standard deviation is used to determine the degree of smoothing. A Gaussian is better than the mean filter because it provides gentler smoothing, protect edges and improve quality of an image.It is well defined as, 2

X Y

G ( X ,Y ) 

1 2 

2

e

2

2

2

σ, indicate the degree of standard deviation

Fig. 3: original Image

Fig. 4: Image After Gaussian Smoothing

Fig. 5: Image After Gaussian Filter

III. FEATURE EXTRACTION Let Q be present the number of possible concentrations in an image X × Y pixels, Si, i= 0,1……,Q-1, their concentration values, and ni the absolute frequency that Si arises in the image. P is the probability of Si occurs in the image.

Mean: It is the ordinary value of concentration of the image and it is well-defined as, [1] Q 1

 



Si P (Si )

i0

Standard Deviation: It is the square root of the variance and it is the evaluation of mean square deviation of gray pixel value P (Si) from its mean value. It is well-defined as, [2]



Q 1 2





(Si  

2

)P (Si )

i0

Entropy: Entropy is a arithmetic measure of randomness that can be used to describe the quality of the input image and it is well-defined as, [3] Q 1

E    P ( S i ) log 2[P(Si )] i0

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Different Image Pre-Processing and Feature Extraction Techniques for Breast Cancer Detection in Labview (IJSTE/ Volume 4 / Issue 7 / 009)

Skewness: Sk describes the degree of asymmetry of a pixel spreading in the indicated window around its mean and it is defined as, [4] S k = E (S - μ)3 σ3 Kurtosis: K measures the Peak-ness or flat-ness of a spreading relative to a normal distribution. It is defined as, [5] Q 1

K 

 (S

  )P (Si ) 4

i

i0

Variance: Variance is the square root of standard deviation and it is the average of squared differences from the mean. It is defined as, [6] Q 1



2





(Si  

2

)P (Si )

i0

Table - 1 Comparison of filtering technique Method PSNR MSE Median Filter 31.43db 34.78db Gaussian Filter 35.02db 15.32db

IV. CONCLUSION AND FUTURE SCOPE It is concluded that the Mean Square error is least and Peak signal to noise ratio is maximum in the Gaussian filter. So this method proves that the pre-processing using Gaussian smoothing provides the better quality of image. It removes the salt and pepper noise and enhances the image for further analysis. This method plays an important role in Breast cancer detection and classification in LabVIEW. Our future work is to extend the various different pre-processing and features extraction techniques.

REFERENCES [1] [2] [3] [4] [5]

R. Ramani, “The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images” I.J. Image, Graphics and Signal Processing, pp 46-55, 2013.

T.C.Wang, N.B. Karayiannis, “Detection of micro-calcifications in digital mammograms using wavelets, Medical Imaging,” IEEE Transactions, 17, pp 497 -510, 1998.

S.Lai, X.Li W.Bischof,”On techniques for detecting circumscribed masses in mammograms”, IEEE Trans. Med. Imag., vol. 8, pp. 375-3871989.

Peng F, Yuan K, Feng S, Chen W,”Pre-processing of CT Brain Images for Content-Based Image Retrieva”. In: Proceedings of International Conference on Bio-Medical, Engineering and Informatics, 2008, 208-212.

R. Subash Chandra Boss, K. Thangavel, D. Arul Pon Daniel “ Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle”.

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