A Comparitive Study of Various MicroCalcification ... - IEEE Xplore

A Comparitive Study of Various MicroCalcification Cluster Detection Methods in Digitized Mammograms K.Kavitha, Dr.N.Kumaravel Department of Medical Electronics, College of Engineering, Anna University, Chennai, India. Phone : 91 – 0 – 9444757794 Email : [email protected]

Keywords: Breast Cancer, CAD, Clustered Microcalcification, Skewness, Kurtosis, Bayesian classifier, Filterbank. Abstract - The presence of microcalcification clusters in mammograms contributes evidence for the diagnosis of early stages of breast cancer. Computer aided diagnosis (CAD) can be used as a useful tool for improving the accuracy of the diagnosis process, and for helping the radiologists with film interpretation. In this paper, digitized mammograms are decomposed using filter banks at several levels in the transform space. Global nonlinear operator was applied on decomposed detail subband images using multiscale adaptive gain method to enhance the images. Skewness & kurtosis were applied as detection method of the previous modification image with a specific size of region of interest (ROI). The DCT Co-efficient taken as spectral features for classification of positive and negative region of interest. A three layered BPN employed as a classifier to evaluate classification efficiency. Distinction between microcalcification clusters

A microcalcification cluster, an early sign of breast cancer that may warrant biopsy, is commonly defined as three or more microcalcifications present in 1 cm2 on a mammogram. These clusters which are in particular clinically significant are often difficult to detect due to their small size and their similarity to other tissue structures. Approximately 10% to 30% breast cancer cases missed due to the increasing pressure to interpret large numbers of mammograms and the subtlety of many early signs. Computer-aided mammography is an important and challenging task in automated diagnosis. CAD is defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to assist the work of the radiologists in reducing false negatives and false positives as well as improving diagnostic accuracy, efficiency and consistency of the radiologist’s image interpretation. Many researchers have developed a computerized scheme based on wavelet transform, which are extremely powerful in that they provide a means for multiresolutional edge enhancement with vigorous mathematical support. Noise reduction can very easily be incorporated into the enhancement process. Furthermore, it is possible to filter the image without information loss, unlike more traditional techniques such as histogram equalization and unsharp masking which work in a single scale.

(nodular components) and normal tissues such as blood vessels and mammary ducts (linear components) made using the eigenvalue of the Hessian matrix.Bayes Discriminant function was employed for distinguishing among abnormal ROIs with a microcalcification cluster and two different types of normal ROIs without a microcalcification cluster. An integrated approach of using a filterbank, DCT and Bayesian classifier has shown to have the potential to detect microcalcification clusters with a clinically acceptable sensitivity and low false positives.The detection performance was evaluated by using 40 mammograms and showed 99% accuracy.

1. INTRODUCTION

Strickland et al. [4] used a DWT with biorthogonal spline filters to detect microcalcifications. Netch [5] proposed a detection scheme for the automatic detection of clustered microcalcifications using multiscale analysis based on the Laplacian-of-Gaussian filter and a mathematical model describing a microcalcification as a bright spot of certain size and contrast

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182 000 new cases of breast cancer are diagnosed and 46 000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime [1]. Since the cause of breast cancer remains unknown, primary prevention becomes impossible.

Chan et al. [9]–[11] investigated a computer-based method for the detection of microcalcification in digital mammograms. The method is based on a difference image technique in which a signal suppressed image is subtracted from a signal enhanced image to remove structured background in the mammogram.

Mammography associated with clinical breast examination is the only viable and effective method at present for mass screening to detect breast cancer. An early sign of the disease in 30% – 50% of mammographically detected cases is the appearance of clusters of fine, granular microcalcifications which are tiny granule like deposits of calcium of size 0.3mm to 0.7mm,more X-ray opaque than the mammary tissue (mainly fat). Although they have high inherent attenuation properties, appear with low contrast due to their small size.

Clarke et al and Qian et al [12 – 13] applied a denoising to the image and then took the high-pass scale of a DWT using spline wavelets. Most recent works provide different methods to accomplish this job using classical Orthogonal Wavelet Transform (OWT) [15].

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The main goal of this paper is to device a better CAD technique for Micro calcification cluster detection in mammograms and to provide the comparative performance of various techniques.

HL(z) Input

HH(z)

The rest of the paper is organized as follows. Section 2 explains proposed system. Section 3 & 4 discusses the results and conclusion.

L2

K2

L2

K2

FL(z) Output FH(z)

Fig. 2. Two-channel filter bank.

In order to improve the contrast enhancement, global nonlinear operator was applied on decomposed detail subband images In this technique, highpass components will be suppressed if it is value less than the threshold and will be increased if it is greater than threshold given by [17]:

2. MICROCALCIFICATION DETECTION SYSTEM Digitized mammogram

f(y) = a[sigm(c(y-b)) – sigm(-c(y+b))]

Decomposition using Filter Bank & Enhancement

(1)

1 Selection of ROI Skewness and Kurtosis Measurement

where a =

DCT Feature Extraction

sigm(c(1-b)) – sigm(-c(1+b)) 1

Extraction of 8 Features

Bayes Discriminant function

Fisher criterion

0 < b < 1 ; sigm(y) = 1 + e-y where b and c are defined as threshold and rate of enhancement respectively.

Three layer BPN

2.2 Scheme for Detection of Microcalcification Detection of micro calcification

2.2.1 Detection using statistical methods: A statistical test based on skew ness and kurtosis is effective in finding regions with asymmetrical and heavier tailed distributions [18].

Fig. 1. Proposed block diagram

2.1 Preprocessing

If a region contains microcalcification the symmetry of the distribution of detail-image coefficients is destroyed and the kurtosis assumes a high value. The detail-image is first divided into overlapping square regions (ie) N*N pixels in which statistical parameters such as skewness and kurtosis are estimated. A region with high positive skewness and kurtosis is marked as a region of interest (ROI).

Enhancement methods based on wavelet transform proved to be very useful because of their multi-resolution properties wavelet transform is basically a filtering technique that represents images hierarchically on the basis of scale or resolution, analyzing high-spatialfrequency phenomena localized in space, and, thus, can effectively extract information derived from localized high-frequency signals, such as those emitted by microcalcifications.

Skewness (γ1) and kurtosis (γ2) parameters are defined as : E[(x – E[x])3] γ1 = (2) (E[(x – E[x])2])3/2

In fact, in mammograms different features, such as microcalcifications, masses, background, and noise appear at different scales, and so they can be selectively enhanced, detected or reduced within different resolution levels.

E[(x – E[x])4] The proposed enhancement technique employs to increase the contrast of microcalcifications while maintaining their shapes, by use of a filter bank that satisfies the requirement for perfect reconstruction [16]. Fig. 2 shows a two-channel filter bank.

γ2 =

-3

(3)

(E[(x – E[x])2])2

where E[(x)k] is kth moment of variable x. The detection problem is posed as a hypothesis given by 0

γ1 < T1 or

1

γ1 > T1 and γ2 > T2

Γ (x) =

406

γ2 < T2 (4)

ni and χi are the number of patterns and the pattern set in class i respectively. The region connecting the ROIs that were classified as abnormal was considered to be region of potential microcalcification clusters.

where T1 and T2 are skewness and kurtosis thresholds value, respectively. Value”0” signs there are no microcalcification in the region, and “1” signs there is microcalcification in the region. 2.2.2 Detection using DCT & BPN Network: DCT coefficient taken as spectral features for classification of positive and negative region of interest. For ROIs containing clustered microcalcifications, the co-efficients spread to lower right corner of the matrix. A three layer BPN [19] network employed as a classifier to evaluate classification efficiency.

3. SIMULATION The simulation is carried out by using the following conditions a) In our experiments we mainly used images from the MIAS digital mammography database [22]. This database contain 322 images. In this simulation, 40 mammogram images were used. Some samples of images are shown in the results.

2.2.3 Detection using Hessian matrix & Bayesian classifier: For the distinction between Microcalcification clusters and normal tissues in mammograms, it may be important to detect both nodular components, such as microcalcifications, and linear components, such as blood vessels and mammary ducts. [20].This is achieved by using the value of the second derivative or the eigenvalue of the Hessian matrix. The condition that the two eigenvalues must satisfy for a nodular structure and a linear structure, is given by

b) The chosen wavelet basis function is the Daubechies 5/3 tap filter with four coefficients as filter banks [23] without 2-factor down sampling from wavelet transforms coefficients inorder to reduce lost information and maintain size of images. - x[2n – 2] + 2x [2n – 1] + 6x[2n] + 2x[2n + 1] – x[2n + 2] + 2

for a nodular structure : λ1 ≅ λ2 < 0 for a linear structure : λ1 < 0 ; λ2 ≅ 0

L[2n] =

Therefore, the filters based on the second derivatives can be used for the detection or enhancement of the nodular structure and the linear structure.The second derivative of the function ƒ(x,y) in an arbitrary direction θ is given by ∂z

-x[2n] + 2x[2n + 1] – x[2n +2] H[2n + 1] =

= ƒƎ = xT H x

(8) 2

c) Global image enhancement procedure was applied only on 4-level decomposed detail sub band image. The detection of micro calcification algorithm will be done as described above.

2

∂2r

(7) 4

(5)

where xT = (cosθ sinθ ), H is a Hessian matrix.

4. RESULT & DISCUSSION

Many region of interest (ROIs) of size 3 mm x 3 mm (50x50 pixels) were then selected from the mammogram image. In each ROI, eight features were determined from the subimages. Bayes discriminant function employed for distinguishing among three classes, class 1, 2, 3 corresponding to abnormal ROI with a microcalcification cluster, the normal ROI with blood vessels, and the normal ROI without blood vessels, respectively.

Fig. 3. shows the simulation results of two sample test images which gave a suitable results based on detection and location of microcalcification. Skewness and kurtosis analysis of various suspected regions are shown in Fig. 4.

Bayes discriminant function for distinguishing among the three classes [21] given by gi (x) = - ½(x – mi)t Σi-1 (x – mi) - ½ log | Σi |

(6)

(a)

where,

Fig. 3 (a) Test pattern with microcalcification, (b) Test pattern without microcalcification

1 Σ x

mi = ni

x∈

χi

1 Σi =

Σ (x – mi) (x – mi)t ni

(b)

x

∈

χi

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5. CONCLUSION A new approach to the problem of micro calcifications detection in digital mammograms is introduced. Using the proposed criterion with a integrated approach using filter bank, DCT and bayesian classifier, experimental results showed that a higher classification accuracy than ordinary techniques is achieved. The proposed technique also reduces the complexity of the detection process.

(a)

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(b) Fig. 4. Skewness and Kurtosis measurement for (a) Abnormal ROI and (b) Normal ROI

[3]

The Table 1 shows a remarkable difference in skewness and kurtosis values between normal and malignant images with which classifying those becomes relatively easier. Detection efficiency improved using eigen values as another classification parameter shown in Table 2. A comparison of the performance of the proposed methods is shown in Fig.5. The integrated approach of using all the three methods showed low false positive fraction. Sl.No 1 2 3 4 5

Skewness Normal Malignant 0.7628 4.3951 0.77281 4.2863 1.0979 2.5483 2.4834 2.4242 0.91275 3.3172

[4]

[5]

Kurtosis Normal Malignant 11 49 9 69 10 30 13 31 6 32

[6]

Table. 1. Comparison of skew ness and kurtosis value for test images.

Sl.No 1 2 3 4

[7]

Eigen Values Normal Malignant 6.1626 23.3072 2.0508 16.0235 5.1878 18.7423 4.5023 25.5664

[8]

Table. 2. Eigen values for normal and abnormal ROI’s

[9]

[10]

[11] Fig. 5. Performance analysis of various methods

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