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I JSRD - I nternational Journal for Scientifi c Research & Development| Vol. 3, I ssue 12, 2016 | ISSN (onli ne): 2321-0613

All rights reserved by www.ijsrd.com  1039

Enhancement and Segmentation of Tumor in Mammograms for Breast

Cancer DetectionS.Savitha1 S.S.Thamilselvi2 

1PG Student 2Assistant Professor1,2Department of Electronics & Communication Engineering

1,2

K.S.Rangasamy College of Technology, Tiruchengode, India Abstract  —  The well-known method used for the detection of

 breast cancer is mammography. The mammogram images

are of poor contrast and noisy. To enhance the quality of the

mammograms the Robust polynomial filter is used in this

 paper. It is the combination of Type-0 and Type-II

 polynomial filter. The segmentation technique is then applied

for the separation of tumor region. The segmentation is

 performed using Marker controlled watershed segmentation.

The pectoral muscles are removed from the image, the tumor

region is segmented. It helps to detect the breast cancereffectively.

Key words:   Mammogram, Contrast Enhancement,

Polynomial Filter, Marker Controlled WatershedSegmentation

I.  I NTRODUCTION 

The main cause of death among women is due to breast

cancer. It is the second largest cancer in the world. It occursdue to either microcalcifications or masses. The

microcalcifications are tiny specks of calcium in the breast.

They are referred to as a cluster and may indicate a small

cancer. A mass is the group of cells clustered together more

densely than the surrounding tissue. The early detection of

the cancerous region helps in early diagnosis of a diseased person which will reduce mortality rates. For breast cancer

detection various techniques are used in which themammography is the mostly used technique by radiologist.

In mammography low dose X-ray is used. Due to this it is

difficult to interpret between normal and cancerous tissues.

The quality of the mammogram is improved using the imageenhancement technique. In some mammograms the

malignant tissues and pectoral muscles both are present. The

 pectoral muscles are to be removed from the mammograms

for the effective detection of breast cancer. Since, the

 pectoral muscles have same intensity and texture as that of

the malignant tissues, it is necessary to segment the pectoral

muscle before tumor detection. In this paper, for the

enhancement of mammograms Robust polynomial

filtering[4]  technique is used. To segment the tumor regionthe Marker controlled watershed transform is applied [2].

II.  METHODOLOGY 

The input mammogram images are taken from MIAS and

DDSM databases. The images are normalized beforefurther processing. The Robust polynomial filter is applied

for the enhancement of mammograms. It is the combination

of Type-0 and Type-II polynomial filter. The Marker

controlled watershed segmentation is applied to the

enhanced results. The thresholding is then applied to remove

the pectoral muscles. The block diagram of the method is

shown in the Fig 1.

Fig. 1: Block diagram.

 A.   Robust Polynomial Filter

The Robust Polynomial Filtering[4]  (RPF) is a linear

combination of Type-0 and Type-II polynomial filters. The

Type-0 filter provides contrast enhancement and noisefiltering and the Type-II filter provides edge sharpening.

The characteristic equation of the Robust Polynomial Filter

can be stated as:

y(n) = 0() + ()  (1)

Where:0()  represents the Type-0 polynomial

filter and ()  represents the Type-II polynomial filter

respectively. The y(n) can be expanded as:

y(n) = + 0 +

  (2)

Where:

= 052 + 2(1

2 + 32 + 7

2 + 92) +

2(22 + 4

2 + 62 + 8

2)  (3)

0

= ∅052

+ ∅2(12

+ 32

+ 72

+ 92)

+∅2(22 + 4

2 + 62 + 8

2)  (4)

= ∅7(1

3 + 1

7 + 3

9 + 7

9) +

∅8(19

+ 37

) + ∅9(28 + 4

6) + ∅10(1

6 +

18

+ 27 + 2

9 + 3

4 + 3

8 + 4

9 +

67)  (5)

The terms a,b,c are the powers on the pixels raisedinside a 3×3 kernel: a for center pixel b for pixels at odd

locations and c for pixels at even locations.

 B.   Marker Controlled Watershed Segmentation

A marker is a connected component belonging to an image.

It possess the same intensity values and they are treated as

regional minima. Markers can be classified as foreground or background markers depending on its location of region of

interest. This gives a priori knowledge about segmentation.

Initially, the gradient magnitude of the input image is

computed.Next the morphological operations are applied to

mark the foreground. Following the opening with a closing

can remove the dark spots. The dark pixel in the image

 belongs to the background. The gradient magnitude image is

modified so that it has only regional minima occur at

foreground and background. The watershed is then applied

to get the segmented result. The application of the watershed

transform results in over segmentation of the image due to

the presence of artifacts and noise. To avoid this, the

watersheds are applied to images with markers. For the

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 Enhancement and Segmentation of Tumor in Mammograms for Breast Cancer Detection

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visualization purpose the watershed output with markers is

converted to coloured images.

III.  EXPERIMENTAL RESULTS AND DISCUSSION 

The Fig 2.(a) and 2.(b) shows the mammogram images

taken from the two standard databases MIAS andDDSM.The normalizaton is performed before applying the

enhancement technique The optimal values of linear andquadratic filter coefficients determined for the Robust

Polynomial Filter [4]  are: 0 = 0.2, 1 = 2 = 0.1, ∅0 = 8€,∅1 

= ∅2= ∅6 = −€, ∅3 = −0.5€,∅4 = ∅5 = ∅10=€, ∅7= −2€, ∅8 =

−4€, ∅9  = 4,€ =0.15. The values of the weight indices are

taken as: a=8 μ, b=c=μ; the value of μ varies between 0.5 to

0.7 irrespective of the nature of the breast tissues.

The Fig. 3(a) and 3(b) shows the enhanced result

obtained using RPF technique. By using Robust PolynomialFiltering technique background tissues are suppressed and

the noises are reduced. It is shown that the tumor region and

 pectoral muscles are present at the enhanced output.

Fig. 2(a): mdb184

Fig. 2(b): C_0080

Fig. 2: Original mammograms

Fig. 3(a): mdb184

Fig. 3(b): C_0080

Fig. 3: Enhanced results using RPFThe marker controlled watershed segmentation is

applied to the enhanced mammograms to segment the tumor

region. The Fig. 4 shows the results obtained using Marker

controlled watershed segmentation. The pectoral muscles,

tumor region and the background tissues are represented

with different colors.

The thresholding is then applied to the marker

controlled segmented output to remove the pectoral muscles.The Fig 5.(a) and 5.(b) shows the tumor region segmented

from the mammograms. The results shows only the tumor

region and the other regions are eliminated.

Fig. 4(a): mdb184

Fig. 4(b): C_0080

Fig. 4: Results obtained using the marker controlledwatershed segmentation.

Fig. 5(a): mdb184

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 Enhancement and Segmentation of Tumor in Mammograms for Breast Cancer Detection

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Fig. 5(b): C_0080

Fig. 5: Results obtained for segmentation of tumor from

mammogram images.

Fig. 6(a): mdb241

Fig. 6(b): mdb315

Fig. 6: Results obtained for segmentation of tumor frommammogram images.

IV.  CONCLUSION 

The enhancement using Robust polynomial filter removes

the background noise and ill effects effectively. From the

segmented results it is proved that the presented work

effectively removes the pectoral muscles and segment out

only the tumor region. This helps to do feature extraction

and better classification of tumor region for the successful

detection of breast cancer.

R EFERENCES

[1]  Yasmeen Mourice George, et al., “Remote computer -

aided breast cancer detection and diagnosis system

 based on cytological images”, IEEE Systems Journal,

vol. 8, no. 3, pp.949-964, Sep. 2014.

[2]  Ravi S and A M Khan, “Bio-medical imagesegmentation using marker controlled watershed

algorithm: a case study” International Journal of

Research in Engineering and Technology, vol.03,

 pp.26-30, May 2014.

[3]  Xiaoming Liu and Jinshan Tang, “Mass classification in

mammograms using selected geometry and texturefeatures, and a new SVM-based feature selection

method”, IEEE systems journal, pp.1-11, Nov. 2012.

[4]  V.Bhateja,et al., “A Robust polynomial filtering

framework for mammographic image enhancement

from biomedical sensors,” IEEE Sensors Journal vol.

13,no.11, pp. 4147 – 4156, Nov. 2013.[5]  Pandey,et al., “Design of new volterra filter for

mammogram enhancement,” in Proc. Int. Conf.

FICTA, vol. 199.Dec. 2012, pp. 143 – 151.

[6]  S.Singh and K.Bovis, "An Evaluation of Contrast

Enhancement Techniques for Mammographic Breast

Masses," IEEE Transactions on Information

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