Detection of Microcalcification Clusters in Digital Mammograms using

Multiresolution based Foveal Algorithm

T.Balakumaran

Dept. of Electronics and Communication Engg.

Coimbatore Institute of Technology

Coimbatore, India

e-mail: erbala12@yahoo.com

ILA.Vennila

Dept. of Electrical and Electronics Engg.

PSG college of Technology

Coimbatore, India

e-mail: iven@eee.psgtech.ac.in

Abstract— Mammography is the most used diagnostic

technique for breast cancer. Microcalcification clusters are the

early sign of breast cancer and their early detection is a key to

increase the survival rate of women. The appearance of

microcalcification clusters in mammogram as small localized

granular points, which is difficult to identify by radiologists

because of its tiny size. An efficient method to improve

diagnostic accuracy in digitized mammograms is the use of

Computer Aided Diagnosis (CAD) system. This paper presents

Multiresolution based foveal algorithm for microcalcification

detection in mammograms. The detection of

microcalcifications is achieved by decomposing the

mammogram by wavelet transform without sampling operator

into different sub-bands, suppressing the coarsest

approximation subband, and finally reconstructing the

mammogram from the subbands containing only significant

detail information. The significant details are obtained by

foveal concepts. Experimental results show that the proposed

method is better in detecting the microcalcification clusters

than other wavelet decomposition methods.

Keywords- Computer Aided Diagnosis (CAD), skewness and

kurtosis, foveal algorithm, Microcalcification detection

I. INTRODUCTION

Breast cancer is second leading cause of cancer-related mortality after lung cancer. According to statistical report, more than 9 million women suffering by breast cancer worldwide every year. Thus, early detection and treatment of breast cancer can significantly improve the survival rate of patients. Currently, X-ray mammography is the efficient imaging modality for early detection of suspicious lesions. Microcalcification clusters are one of the important early sign of breast cancer [1].

Microcalcifications are quite very tiny calcium deposits present in the breast regions, it shows up as clusters or in patterns in mammograms and it appear as nodular points with high brightness[1]. However, detection sensitivity of the microcalcification clusters in X-ray mammograms depends upon radiologist’s experience. Also some of the microcalcification clusters are not detected by radiologists due to its tiny size and nonpalpable[2]. To avoid these problems, a Computer Aided Diagnosis (CAD) system has to be developed[3]. The computer output is presented to radiologists as a “second opinion” and that improves the

accuracy in the detection progress. Most of the researchers have proposed numerous methods are based on wavelet transform, which is an efficient transform for analysis and enhancement of mammograms.

Examples for these methods include Laine et al. [4] used multiscale analysis, Yoshida et al. [5] applied a discrete wavelet transform(DWT) and Scharcanski and Jung [6] proposed a wavelet based method for enhancing microcalcifications. A. Karahaliou et al.[7] used image features and Zhang and Agyepong applied multifractal features [8] for microcalcification detection. Naveed et al. extracts DWT features from mammogram in multiresolution analysis and detects the suspicious cells[9]. In this paper, we have proposed the multiresolution based foveal algorithm for microcalcification detection and this method gives good results.

The rest of the paper is organized as follows: section II presents overview of foveal algorithm and the proposed method with multiresolution analysis in section III; Experimental results obtained by proposed method are presented in section IV and conclusion as the last section.

.

II. FOVEAL ALGORITHM

It is easier to identify an object against dark background than to identify an object against a light background[10]. Figure 1 shows part of mammograms and it is difficult to identify the suspicious cells in Fig.1(a) compare to Fig.1(b) due to denser background. The observation of tiny objects by naked eye over lighter background is even more difficult because surrounding dense makes objects almost invisible.

(a) (b)

Fig.1. Part of mammograms

657978-1-4673-0126-8/11/$26.00 c©2011 IEEE

Fig.2. Computation of mean intensity of object, neighbourhood and

background

The algorithm is following steps. The image is divided into object, object neighbourhood and rest of the image (background). Then, set of mean intensity values of object

(μ0), its neighbourhood (μN) and background(μB) are

calculated. The mean of the neighbourhood(μN) is calculated from the pixels surrounding the object excluding the object pixels according to Fig 2.

The kernel size of object has been chosen according to average size of microcalcification and neighbourhood size is twice than object size. Microcalcifications appear as subtle and bright spots between 0.6 mm to 2 mm in size in the digital mammograms. Therefore, object kernel size is a 15×15 square area, which is enough to cover the microcalcification.

Perceivable contrast is computed by

Cp =

−

0

0

N

N

μ

μμ >

Otherwise

Nμμ0

(2.1)

The areas having perceivable contrast is greater than adaptive threshold(AT) are marked as suspicious areas. The adaptive threshold is calculated by

AT = ( )

+

+

22

2

808.0

808.0

K

N

N

K

N

Cm

Cm

μ

μ

μ

μμ

>

>

KN

NK

μμ

μμ

(2.2)

Where BNK μμμ 077.0923.0 += , Cm is minimal

perceivable contrast which is used to reduce false positives.

We analyzed that Cm= 140

)( BPIσ gives good detection

of microcalcification, where )( BPIσ is standard deviation

of bandpass image of the mammogram image. The microcalcification clusters (MM) are marked

according to following decision rule

MM =

0

1

M

M >

Otherwise

AC TP (2.3)

Where M1 corresponds to presence of microcalcification cluster and M0 corresponds to microcalcifications are not present in the region.

III. PROPOSED METHOD

A. ROI Identification

Region of interest can be identified by using third order moment (skewness) and fourth order moment (kurtosis). The Original mammogram image is decomposed into four subimages by undecimated wavelet transform. The resulting bandpass subimage such as horizontal detailed image or vertical detailed image is used to detect the region encircling the clusters of microcalcifications.

An estimate of the skewness and kurtosis is given by

Sk =3

1

3

)1(

)~(

σ−

−=

N

mxN

i

i

(3.1)

Ku = 3)1(

)~(

4

1

4

−−

−=

σN

mxN

i

i

(3.2)

where xi is the input data over N observations, m~

is the

ensemble average of xi and σ with its standard deviation. The horizontal subimage is divided into overlapping 32x32 square area, the third order moment and fourth order moment are calculated at each square area. The area having skewness value greater than 0.2 and kurtosis value greater than 4 is marked as a region of interest (ROI). A 100x100 square matrix was chosen as the ROI size so that the microcalcification clusters center would be coincident with the ROI center and ROI is extracted from the Original mammogram.

B. Multresolution analysis with foveal method

Wavelet transform decomposes a signal into hierarchical set of approximations and details. The image is decomposed by wavelet tool into well localized, interpretable components that make local features easily extractable. Wavelet transform has advantages over Fourier analysis in analyzing physical situations such as singularities and discontinuities contain in image. The clusters of microcalcification appear in mammogram as group of tiny granular bright spots. These bright spots, presented as singularities, concerning localized high frequency signals such as it can be extracted efficiently by wavelet transform.

The dyadic two dimensional wavelet transform is shown in figure 3. It has downsampling operator, which removes detail information from the image. Therefore, the filter bank without downsampling & upsampling operator is used to detect tiny size objects. The wavelet transform without sampling operator maintains full resolution at each scale and it is shown in Fig. 4. The 2j is usually utilized for the order of z at scale j in order to obtain more detailed information at each scale. S0f is original image and SRf is perfectly reconstructed image, which is obtained by properly chosen filter coefficients.

658 2011 World Congress on Information and Communication Technologies

Fig.3. Dyadic Wavelet transform

Fig.4. Wavelet transform without upsampling & downsampling operator

S1f be the approximation subimage, W1HL

f & W1LH

fcontain horizontally and vertically oriented structures, and the W1

HHf contains diagonal oriented structures. Since

microcalcifications appear as high frequency components in frequency domain, it can be extracted by suppressing background information and amplifying the suspicious areas.

The S1f the lowpass filtered version of the image and is further decomposed in the same manner. The multiresolution representation is collection of subimages, which is obtained by iterating the lowpass subimage. The mammogram is decomposed by wavelet transform without sampling operator upto n scales (n value depends upon the type of microcalcification whether it’s benign or malignant). To extract high frequency microcalcification, lowest frequency is suppressed and finally high frequency subbands are reconstructed. But reconstructed image contains not only microcalcification also it contains artifacts which is high frequency in nature. In our proposed method, high frequency subbands contain microcalcification clusters are reconstructed accurately by applying foveal algorithm into the multiresolution analysis.

In section II, we analyzed foveal algorithm. The foveal algorithm is applied to approximation image(Sjf) at each scale and significant coefficients are marked according to Eq. 2.1-2.3. Microcalcification detection is achieved by thresholding to the high frequency subbands.

The thresholded high frequency coefficients are computed by

=0

),(),(

yxWyxW

M

jM

j

otherwise

yxfS Mark

j ),( (3.3)

Where M indicates LH,HL,HH subbands and Mark

j yxfS ),( is approximation image coefficient is

marked by foveal algorithm of scale j. The wavelet

coefficients of detail planes are thresholded to zero if their

corresponding approximation coefficients are not marked by

foveal algorithm and the image is reconstructed from the

remaining significant coefficients. On reconstruction,

microcalcification detection is obtained by suppressing

lowest frequency subband.

IV. EXPERIMENTAL RESULTS

The proposed approach was tested in MATLAB 10.0 and verified on the set of mammogram image with different features. The set of mammogram were obtained from DDSM database consists of 88 image with resolution of 50

m/pixel. In this set, 56 images are abnormal images and 32 images are normal. To claim our proposed method to be superior, a comparison was made with 2-D wavelet decomposition and multiscale wavelet transform.

Fig.5(a) shows an Original mammogram image of size 1024x1024. Fig.5(b) shows region of interest is identified by using third and fourth order moments and Fig.5(c) shows ROI of size 100x100 extracted from original mammogram.

2011 World Congress on Information and Communication Technologies 659

(a) (b)

(c) (d)

(e) (f)

Fig. 5 Steps of microcalcification detection (a)Original mammogram

image (b) Region of Interest (ROI)identified by using skewness & kurtosis (c)ROI image (d)Detection by 2-D wavelet decomposition (e) Detection

by multiscale method (f) Detection by Proposed algorithm

The microcalcification clusters are detected by 2D wavelet and multiscale wavelet transform are shown in Fig.5(d) and Fig 5(e). Finally Fig.5(f) shows detected image by proposed method. The free-response receiver operating characteristic (FROC) curve is used to evaluate the microcalcification detection performance[11]. It is a plot of the true-positive detection ratio (TP) versus the average number of false positives (FPs) per image.

Fig.6. Comparative of FROC curves for microcalcification detection

Figure 6 shows that the proposed method has the TP ratio of 91.1% ,multiscale wavelet has TP ratio of 90.6%

and the 2D wavelet method has the TP ratio is 90.2% for a 1.5 FP/image. The detection capability of the proposed method is much higher than the 2D wavelet method and multiscale wavelet.

V. CONCLUSION

In this paper, the development of a CAD system for the

accurate and automatic microcalcification clusters detection

in mammogram was presented.In many traditional wavelet

decomposition methods, detection of microcalcifications is

obtained by suppressing lowest subband and reconstructing

all high frequency subbands. But reconstructed image

contains not only calcifications, also it contains unwanted

artifacts and noise. Therefore, diagnostic accuracy can be

improved by reconstructing only high frequency

calcifications. The computational complexity and storage

space of the proposed algorithm is high compared to 2D

wavelet transform. But the results were promising that this

method could detect the microcalcifications accurately than

2D wavelet transform. We plan to compute the size of each

microcalcification in future.

REFERENCES

[1] Nakayama, R. Uchiyama, Y. Yamamoto, K. Watanabe, R. Namba, K, “Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms”, IEEE Transactions on Biomedical Engineering, vol 53. No.2,p.273-283,Feb 2006

[2] R. G. Bird, T. W. Wallace, and B. C. Yankaskas, “Analysis of cancers missed at screening mammography,” Radiology, vol. 184, pp. 613–617,1992

[3] L. Wei, Y. Yang, R. M. Nishikawa, M. N. Wernick and A. Edwards, “Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications,” IEEE Trans. Medical Imaging, vol. 24, no.10, pp. 1278-1285, 2005

[4] A.F. Laine, S. Schuler, J. Fan, and W. Huda, “Mammographic feature enhancement by multiscale analysis,” IEEE Trans. Med. Imag., vol. 13, no. 4, pp.725–740, Dec. 1994.

[5] H. Yoshida, K. Doi, R. M. Nishikawa, M. L. Giger, and R. A. Schmidt, “An improved computer- Assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms,” Acad. Radiol., vol. 3, pp. 621–627,1996

[6] J. Scharcanski and C. R. Jung, ‘‘Denoising and enhancing digital mammographic images for visual screening,’’ Comput. Med. Imaging Graph., vol. 30,no. 4, pp. 243–254, 2006

[7] Karahaliou, S. Skiadopoulos, I. Boniatis, P. Sakellaropoulos, E. Likaki,G. Panayiotakis, and L. Costaridou, “Texture analysis of tissue Surrounding microcalcifications on mammograms for breast cancer diagnosis,” Br.J. Radiol., vol. 80, no. 956, pp. 648–656, 2007.

[8] Ping Zhang, Kwabena Agyepong , “Wavelet-based Fractal Feature

Extraction for Microcalcification Detection in Mammograms”, Proceedings of the IEEE SoutheastCon 2010, March 2010.

[9] Nawazish Naveed, Tae-Sun Choi M and ArfanJaffar,” Malignancy

and abnormality detection of mammograms using DWT features and ensembling of classifiers”, International Journal of the Physical

Sciences, Vol. 6(8), pp. 2107-2116, April 2011

[10] Heucke, L.,M.Knaak, andR.Orglmeister, “A Newimage segmentation method based on human brightness perception and foveal adaptation”, IEEE Signal Processing Letters 7 (6), p. 129-131,2000

[11] C.E.Metz, “Some practical issues of experimental design and data

analysis in radiological ROC studies,” Invest. Radiol., vol. 24, no. 3,

pp. 234–245, Mar. 1989

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