enhancement of mammographic images using morphology and

Enhancement of Mammographic Images using Morphology and Wavelet

Transform Harish Kumar.N1 Amutha. S2 Dr. Ramesh Babu .D. R3

1Lecturer, Dept. of CSE, Dayananda Sagar College of Engineering, Bangalore, India 2Assistant Professor, Dept of CSE, Dayananda Sagar College of Engineering, Bangalore, India

3Professor, Dept. of CSE, Dayananda Sagar College of Engineering, Bangalore, India

[email protected]

[email protected] [email protected]

Abstract

Mammography is the effective technology for early detection

of breast cancer and breast tumour analysis. In

mammography, low dose x-ray is used for imaging. Due to

the low dose X-ray the images obtained from mammography

are poor in contrast and are contaminated by noise. Hence it

is difficult for the radiologist to screen the mammograms for

any abnormalities like microcalcifications and masses. This

ensures the need for image enhancement to aid radiologist

for interpretation. This paper introduces a new enhancement

method for digital mammographic images based on modified

mathematical morphology and biorthogonal wavelet

transform. In the proposed method we adopted a level

dependent threshold for thresholding the detail coefficients of

wavelet transform. To evaluate the performance of the

proposed method, Contrast Improvement Index (CII) and

Edge Preservation Index (EPI) are used. Experimental results

and performance analysis indicate that the proposed method

consistently outperforms existing techniques.

1. Introduction Breast cancer is the most common cancer among

women in the United States [1]. It is the leading cause

for death of women between the ages 35 and 54. Early

detection is the most successful method of dealing with

breast cancer. Currently the best method available for

early detection of breast cancer is mammography.

Other techniques, such as computed tomography (CT),

magnetic resonance imaging (MRI), ultrasound and

transillumination have been investigated, but

mammography remains the proven technique. A

mammogram is a picture of breast taken with a safe,

low dose X-ray machine. Generally, mammograms are

poor in contrast and features that indicate the breast

cancer are very minute. Digitally enhancing the

mammograms will provide us confident interpretation

of critical cases, as well as allowing quicker diagnosis.

It is very difficult to interpret the x-ray mammograms

because of small differences in image density of

various breast tissues in particular for dense breasts [2].

When the radiologists screen the mammograms with

low dose X-ray the images obtained are poor in

contrast.

In low-contrast mammograms, it is difficult to

interpret between the normal tissue and malignant

tissue. In general while screening the mammograms,

due to imperfect machines, the obtained mammograms

are contaminated by noise. Image enhancement

techniques have been widely used in the field of

radiology where the subjective quality of images is

important for human interpretation and diagnosis.

Numerous algorithms are available in literature for

enhancement of medical images such as histogram

equalization, unsharp masking, median filter, Gaussian

filters, and morphological filters [3, 4]. Conventional

image enhancement techniques do not perform well on

mammographic images. Recently multiscale techniques

have evolved and sparked the interest of researchers for

contrast enhancement of mammographic images.

Nowadays wavelet domain has gained more popularity

in image denoising rather than conventional spatial

domain techniques such as average, median, min-max

filters. In wavelet domain each noisy coefficient is

modified according to certain threshold calculated. The

threshold is applied to each noisy coefficient to obtain

better performance. However, soft thresholding is most

widely used in the literature [5].

The organization of the rest of the paper is as

follows: Section 2 presents review of recent literature.

Section 3, describes the proposed Enhancement

method. Section 4 highlights the results of extensive

experimentation conducted on some mammographic

images. Finally conclusion is discussed.

Harish Kumar N et al,Int.J.Computer Techology & Applications,Vol 3 (1),192-198

IJCTA | JAN-FEB 2012 Available [email protected]

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mailto:[email protected]



2. Review of Literature Some work has been done in the past for the

enhancement of mammograms. Dhawan et al. [6]

proposed an optimal adaptive neighborhood processing

algorithm with a set of contrast enhancement functions

to enhance the mammographic features. The method

was the improvement of the earlier work developed by

Gordon and Ranagayyan [6].The method can enhance

the desired, but unseen or barely seen features of an

image with little enhancement of the noise and other

background variations. Tomklav StojiC et.al. [7]

Proposed a new algorithm for both local contrast

enhancement and background texture suppression in

digital Mammographic images. The algorithm was

based on mathematical morphology applied to gray-

scale image processing. The algorithm was not efficient

in enhancing the micro calcifications in digital

mammograms. Yajie Sun et.al. [8] developed an

adaptive-neighborhood contrast enhancement algorithm

(ANCE) for skin-line extraction. ANCE is used to

enhance the parenchyma of the breast and suppress the

background noise. Suppression of the background noise

can improve skin-line extraction and skin line

extraction is an important step in CAD. The method

can enhance only the skin-line rather than the whole

image. Heinlein et al. [9] proposed an algorithm for

enhancing microcalcifications in mammograms based

on filter banks derived from continuous wavelet

transformation, which were called integrated wavelets.

The major disadvantage of the method was that it

required an empirical selection of appropriate

thresholds for image denoising, as well as the

specification of an appropriate size range for the

structures to be enhanced [10]. Sakellaropoulos et al.

[10] developed a method to enhance contrast with

redundant dyadic wavelet transform. The method can

enhance the contrast of the mammograms and depress

the image noise. Jiang et al. [11] developed a method to

enhance possible microcalcifications combining fuzzy

logic and structure tensor. In the method, a structure

tensor operator was first produced and was then applied

to each pixel of the mammographic image, which

resulted in an eigenimage. The eigenimage was used to

combine with the fuzzy image which was obtained by a

fuzzy transform from the original image to enhance the

contrast. This method can suppress non-MCs regions

while enhancing the MC’s regions. The method is

complex in nature due to fuzziness and combination of

two different domains need expertise. Scharcanski et al.

[12] developed a wavelet transform based adaptive

method for contrast enhancement and noise reduction

in mammographic images. In this method, the images

were first pre-processed to improve the local contrast

and subtle details, then the pre-processed images were

transformed into wavelet domain for noise reduction

and edge enhancement. Although the method gave

good enhancement results the complexity of the

algorithm is more due to the wavelet domain. . Hence a

method with better results for improvement in contrast

as well as denoising of mammogram images is

necessary.

3. Proposed Method The basic enhancement needed in mammography is

the increase in contrast and denoising, especially for

dense breasts. The enhancement model takes

mammographic image I(x, y) as input. The image I(x,

y) is separated into low frequency and high frequency

components by using Gaussian low pass filter to get a

higher degree of control over dynamic range. For the

low pass filtered image L(x,y), modified mathematical

morphology is applied. The high pass filtered image

H(x,y) contains the edge information and the noise.

Edge Enhancement algorithm is applied to the high

pass filtered image to enhance the edge information and

to attenuate the noise. Then morphologically processed

image M(x,y) and Edge Enhanced image EE(x,y) are

added to get the contrast enhanced Image C(x,y). To

remove the noise wavelet transform is applied. Wavelet

transform consists of three operations: wavelet

decomposition, thresholding detail coefficients and

wavelet reconstruction. Approximation and detail

coefficients are obtained by decomposition. For the

detail coefficients level dependent threshold is applied.

Finally the decomposed image is reconstructed by the

approximation and the modified detail coefficients

E(x,y).In the following subsections the methods used

for enhancement of mammographic images are

discussed in detail.

3.1 Modified Mathematical Morphology

Mathematical morphology originated in set theory

and finds its place in any disciplines when it is

necessary to establish the relationship between the

geometry of physical system and some of its property.

As such, morphology offers a unified and powerful

approach to different image processing problems [15],

[16]. Two simple morphological operations: Erosion

and Dilation are fundamental to morphological

processing. By combining them one can derive

different image processing algorithms.

The dilation of a gray-scale digital image I(x,y) by a

structural element S(i,j) is defined as:

I⨁S m, n = max I m − i, n − j + s i, j (1) (3.1)

The gray-scale erosion is given by Eqn. (3.2):



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I⨂S m, n = min I m + i, n + j − S i, j (2) (3.2)

The opening of image I(x,y) by structuring element

S is defined as erosion followed by dilation and is

expressed as Eqn. (3.3). The closing of image I(x,y) is

defined as dilation followed by erosion and is given by

Eqn. 3 and Eqn. 4:

I ∘ S = I⨂S ⨁S (3) (3.3)

I S = I⨁S ⨂S (4)

Gray-scale opening can remove light details smaller

than the structuring element. Similarly gray-scale

closing removes dark details smaller than structuring

element.

The top-hat by opening is defined as the difference

between the original image and its gray scale opening

using structuring element S and it is defined as Eqn. 5:

TO = I − I ∘ S (5)

Similarly dual bottom-hat by closing is the

difference between the gray-scale closing image and

original image is represented by Eqn. 6:

BC = I S − I (6) (3.6)

Top-hat by opening yields an image that contains all

residual features removed by opening. Adding these

features back to original image has the effect of

attenuating the high intensity structures. The dual

residual obtained by using bottom-hat by closing is

then subtracted from resulting image to attenuate low

intensity structures:

C x, y = I x, y + TO − BC (7)

The mathematical morphological approach is shown

in Figure 2.

3.2 Edge Enhancement Algorithm

High-pass filtered image H(x,y) is mainly composed

of edge information and noise. The high-pass filtered

image is obtained by subtracting the low-pass filtered

image L(x,y) of the given input image from the original

input image I(x,y). Usually the edge information pixels

have small values where as the noisy pixels have high

values. The edge information has to be enhanced mean

while the noisy pixels are to be attenuated. To

accomplish this we adopt two gain parameters namely

g1 and g2. The first gain parameter is used to enhance

the edge information while the other gain parameter is

used to attenuate the noise. These gain parameters are

applied based on selecting an arbitrary pixel value such

that, the pixel values below the arbitrary pixel selected

are assumed to be the edge pixels and the pixels above

the arbitrary pixel value are noisy pixels. The arbitrary

pixel in this context is selected by taking min and max

pixel value in the high-pass filtered image and taking

the average of both. The Edge Enhancement Algorithm

is given below:

Step1: Select an arbitrary pixel a(x, y) from the high-

pass filtered image and gains G1 and G2.

Step 2: If the pixel in the image is less than a(x, y), go

to step 3. Else go to step 4.

Step 3: Multiply the pixel in image with gain G1.

Step 4: Multiply the pixel in image with gain G2,

where G2<G1.

Step 5: Add the resulting images.

3.3 Wavelet Denoising

Wavelets are mathematical functions that cut up data

into different frequency components and then study

each component with a resolution matched to its scale.

They have advantages over traditional Fourier methods

in analyzing physical situations where the signal

contains discontinuities and sharp spikes. The wavelet

denoising is accomplished in the following three steps

namely Wavelet Decomposition, Threshold Detail

Coefficients, Wavelet Reconstruction.

In this proposed method bi-orthogonal wavelet is

used for decomposition. The input image I(x,y) is

decomposed at two levels. After decomposition the

given image is realized by one approximation

coefficient and 6 detail coefficients. Bi-orthogonal

wavelet representation has many advantages compared

to orthogonal. The subband images are invariant under

translation and do not have aliasing. Smooth

symmetrical or anti symmetrical wavelet functions are

used for alleviation of boundary effects via mirror

extension of the signal. The detail coefficients obtained

after decomposition are horizontal, vertical, and

diagonal coefficients. These detail coefficients are

mainly composed of noisy details so they have to be

denoised using appropriate threshold value.

In the proposed method soft-thresholding is

employed. The level dependent threshold is calculated

at each level. The threshold is given by equation as

shown below.

T = j/2 max(dj) (8)

Where, j is level at which threshold T is computed. In

this step we perform wavelet reconstruction using the

last approximation coefficients and the modified detail

coefficients after thresholding from level N to 1. The

resulting image E(x,y) will be a contrast enhanced

denoised image which is clearer than the original image



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and it will aid the radiologist to mark any abnormalities

like lesions, microcalcifications, and masses.

Figure 1. Enhancement Model

Figure 2. Modified Mathematical Morphology

Figure 3. Edge Enhancement Algorithm Figure3. Shows the flow chart of Edge Enhancement

algorithm. Finally the resulting image will be an edge

Low pass filtered Image

Morphologically Contrast

Enhanced Image

Tophat by opening

Erosion

Dilation

Bottom hat by

closing

Dilation

Erosion

C(x,y)

I(x,y)

G(x,y)

L(x,y)

H(x,y)

EE(x,y) M(x,y)

W(x,y)

E(x,y)

Apply Gaussian Low Pass

Filter

Modified

Mathematical

Morphology

Input mammographic

image

Low frequency components

High frequency

components

Edge Enhancement

Algorithm

Contrast Enhanced

Image

Wavelet Denoising

Enhanced Image

High frequency

component image f(x, y)

Select an Arbitrary pixel a(x, y)

a(x,y)=(min f(x,y)+max f(x,y))/2

If a(x, y)

< f(x, y)

These are edge

pixels and multiply by

gain G1

These are noisy

pixels and multiply by

gain G2 (G2<G1)

Edge Enhanced

image



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enhanced image. The edge enhanced image and the

morphologically enhanced image of the both high-pass

and low-pass filtered images respectively, are added to

give contrast enhanced edge preserved image. Mean

while the noise content may also be enhanced due to

morphological operations and edge enhancement.

4. Experimental Results

The proposed method has been applied to more than

40 mammographic images from the standard Database,

Mammographic Image Analysis Society (MIAS) [17].

To measure the quantitative performance analysis of

the proposed method, parameters such as Contrast

Improvement Index (CII) and Edge Preservation Index

(EPI) are employed.

4.1 Contrast Improvement Index (CII)

A quantitative measure of contrast improvement can

be defined by a contrast improvement index. The

contrast improvement index is defined as follows [13]:

CII =Cprocessed

Coriginal (10)

Where, CProcessed and COriginal are the contrasts for the

processed and original images, respectively. The

contrast C of an image is defined by the following

form:

C =f−b

f+b (11)

Where, f and b denote the mean gray-level value of

the foreground and the background, respectively. The

local contrast at each pixel is measured within its 5x5

pixel neighbourhood. More the value of CII, better

improvement in contrast.

4.2 Edge Preservation Index (EPI)

The edge preservation index [14] is defined as

follows:

EPI = Ip i,j −Ip i+1,j +|Ip i,j −Ip i,j+1 |

Io i,j −Io i+1,j +|I0 i,j −I0 i,j+1 | (12) (4.2)

Where Io(i,j) is an original image pixel intensity

value for the pixel location (x,y), Ip(i, j) is the processed

image pixel intensity value for the pixel location (x,y).

The greater value of EPI gives a much better indication

of image quality.

Table 1. CII Values of enhanced mammograms at the second wavelet decomposition levels

Table 2. EPI Values of enhanced mammograms at the second wavelet decomposition level

The proposed method achieved highest CII and EPI

values as shown in Table 1and Table 2 respectively.

Figure 4 shows the resulting output for some existing

methods took for comparison with the proposed

approach.

5. CONCLUSION

In this paper a new method for enhancement of

mammograms for early detection and diagnosis of

breast cancer has been introduced. It is based on

modified mathematical morphology and Bi-orthogonal

wavelet transform. The algorithm has been applied to

more than 40 mammographic images from the standard

Database MIAS. For performance evaluation of the

proposed method, Contrast Improvement Index (CII)

and Edge Preservation Index (EPI) are adopted.

Experimental results show that the proposed method

yields significantly better image quality when

compared with other contemporary methods.

Image ID VisuShrink Bayes shrink

SURE Shrink

Proposed

Mdb 057 0.8571 0.7963 1.0793 1.49

Mdb 147 0.8566 0.8192 1.1421 1.4977

Mdb 148 0.8579 0.8322 1.1258 1.5639

Mdb 186 0.8387 0.8038 1.1342 1.660

Image ID VisuShrink Bayes shrink

SURE Shrink

Proposed

Mdb 057

0.8596 0.8618 1.1641 2.5615

Mdb 147

0.8419 0.8247 1.0093 2.2926

Mdb 148

0.8328 0.8087 1.2617 2.3573

Mdb 186

0.9537 0.9970 1.1941 2.1739



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6. References

1] Canadian Cancer Society, Facts on Breast Cancer,

Apr.1989.

[2] P.C. Johns, M.J. Yaffe,“X-ray characterization of normal

and neoplastic breast tissues,” Physics Medical and Biology, Vol.32, no. 6, 1987, pp. 675-695.

[3]K.Thangavel, M.Karan, R.Sivakum-ar, A. Kaja Mohideen,

“Automatic detection of microcalcification in

mammograms: a review,” ICGST-GVIP Journal, Volume (5), Issue (5), May 2005.

[4]Issac N. Bankman, “Handbook of medical imaging,”

Academic Press, 2000.

[5]D. L. Donoho, “Denoising by soft-thresholding,” IEEE Transaction on Information Theory, vol. 41, May 1995.

[6] A. P. Dhawan, G. Buellon, and R. Gordon, “Enhancement

of mammographic feature by optimal adaptive neighborhood

image processing,” IEEE Trans. Med. Imag., vol. MI-6, no. 1, 1986, pp. 82–83.

[7]Tomklav StojiC, Irini Reljin, Branimir Reljin, “Local

contrast enhancement in digital mammography by using

mathematical morphology,” IEEE Transactions, 2005. [8]Yajie Sun, Jasjit Suri, Zhen Ye, Rangaraj M. Rangayyan,

Roman Janer, “Effect of adaptive neighborhood contrast

enhancement on the extraction of the breast skin line in

mammograms,” Proceedings of the IEEE , Engineering in Medicine and Biology 27th Annual Conference , Shanghai,

China, September 1-4, 2005.

[9] P. Heinlein et al., “Integrated wavelets for enhancement

of microcalcifications in digital mammography,” IEEE Transactions on Medical Imaging, vol. 22, no. 3, 2003, pp.

402–413.

[10] P. Sakellaropoulos etal,“A wavelet-based spatially

adaptive method for mammographic contrast enhancement,” Phys. Med. Biol., vol. 48, no. 6, 2003, pp. 787–803.

[11] J. Jiang et al., “Integration of fuzzy logic and structure

tensor towards mammogram contrast enhancement,”

Computer Medical Imaging and Graphics, vol. 29, no. 1, 2005, pp. 83–90.

[12] J. Scharcanski and C. R. Jung, “Denoising and

enhancing digital mammographic images for visual

screening,” Computer Medical Imaging and Graphics, vol. 30, no. 4, 2006, pp. 243–254.

[13] W. M. Morrow, R. B. Paranjape, R. M. Rangayyan, and

J. E. L. Desautels, “Region-based contrast enhancement of

mammograms,” IEEE Trans. Med. Imag., vol. 11, no. 3, pp. 392–406, 1992.

[14] M H Xie and Z M Wang, “The partial Differential

Equation Method for Image resolution Enhancement”,

Journal of Remote Sensing, Vol. 9, No. 6, 2005, pp. 673-679. [15] Gonzalez R. C and Woods R. B., Digital Image

Processing, Pearson Education, Asia, 2001.

[16] J Sara, “Image analysis and mathematical morphology,”

Academic Press, London (UK), 1982. [17]http://www.wiau.man.ac.uk/services/MIASIMIA-

mini.htm: The Mammographic Image Analysis Society: Mini

Mammography Database, 2008.

a b

c d

e

Mdb148

b

c d

e

a

Mdb147

Mdb057



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http://www.wiau.man.ac.uk/services/MIASIMIA-

Figure 4. (a) Original image (b) VisuShrink

(c) Bayesshrink (d) Sureshrink (e) Proposed

a b

c d

e

Mdb 186



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enhancement of mammographic images using morphology and

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