Detection of Microcalcification in Digital Mammograms ... - IEEE Xplore

2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies

Detection of Microcalcification in Digital Mammograms by Improved-MMGW Segmentation Algorithm Shrinivas D Desai

Megha G

Avinash B

Sudhanva K

Rasiya S

Linganagouda K

Dept of IS&E BVBCET,HUbli 580031,Karnataka INDIA [email protected]

Dept of IS&E BVBCET,HUbli 580031,Karnataka INDIA megha.skyangel@gmail .com

Dept of IS&E BVBCET,HUbli 580031,Karnataka INDIA [email protected]

Dept of IS&E BVBCET,HUbli 580031,Karnataka INDIA [email protected]

Dept of IS&E BVBCET,HUbli 580031,Karnataka INDIA [email protected] om

Dept of CS&E BVBCET,HUbli 580031,Karnataka INDIA [email protected]

or more µC s in 1 cm2 area. Although about 80% of µC are typically non-cancerous, when the µC are new, clustered firmly together, and distributed in specific configurations, they are suspicious signs of breast cancer, most frequently a noninvasive ductal carcinoma in situ. Due to its high spatial resolution, mammography allows to detect µC at an early stage, a fundamental step for improving prognosis [5, 6].

Abstract—Breast cancer represents the most frequently diagnosed cancer in women. In order to reduce mortality, early detection of breast cancer is important, because diagnosis is more likely to be successful in the early stages of the disease. This paper presents an improved multi-scale morphological gradient watershed segmentation method for automatic detection of clustered microcalcification in digitized mammograms. We use adaptive median filter for preprocessing and incorporated corrections after watershed segmentation by cloned data. This correction has led to better detection and localization of microcalcifications. By comparing our results with original multiscale morphological watershed segmentation method, we proved that the proposed technique is better and performance is improved by approximately 20%. The true positive rate and false positive rate are used to evaluate the performance of the proposed technique. For experimental purpose the dataset from Mammographic Image Analysis Society database and few data collected from local diagnostic center. The result shows achievement of true positive rate of about 95.3% at the rate of 0.14 false positive per image.

A radiologist must carefully examine the mammogram with a magnifier to locate calcifications, which may be embedded in dense tissue. In the past several years there has been a considerable interest in developing methods for automatic detection of µC in mammograms [7]. Several methods have been proposed in the literature for their segmentation [8]. In this paper we present improved method for automatic segmentation of µC using multiscale morphological watershed segmentation based method (I-MMGW). We compare our results with original multiscale morphological watershed segmentation based method (O-MMGW) proposed by S. Vijaya Kumar, et.al [16]. The novelty of our approach lies in the incorporation of corrections by cloned data after watershed segmentation method and performance evaluation through sensitivity and specificity.

Keywords—microcalcification, multiscale gradient, morphological, watershed segmentation, mammography, adaptive median filter.

I.

This paper is organized as follows. Section 2 introduces existing methodologies for automatic detection of µC in digitized mammography. In Section 3, proposed methodology; improved multiscale morphological gradient watershed segmentation for detection of µC is described and also the experimental set up, performance evaluation method is discussed. In Section 4, experimental results are discussed. And Section 5, concludes the work.

INTRODUCTION

This Mammography continues to be regarded as a very useful diagnostic tool for detection and diagnosis of breast lesions. About 10% of all women develop breast cancer and about 25% of all cancers diagnosed in women are breast cancers [1]. The interpretation of a mammogram is often difficult and depends on the expertise and experience of the radiologist. A meta-analysis showed that the sensitivity of screening mammography ranged from 83% to 95% with a false positive rate of 0.9% to 6.5%, respectively [2]. Between 30% and 50% of breast carcinomas demonstrate µC (µC) on mammograms and between 60% and 80% of the carcinomas reveal µC upon histological examination [3]. µC are tiny granular deposits of calcium that appear on the mammogram as small bright spots. µC s are observed in mammograms as white spots varying in size and shape. Important characteristics are their size, shape / morphology, amount, and distribution. Their sizes vary from 0.1 mm to 1 mm [4]. µC detection is very difficult in the mammographies with overlapping breast tissues or high breast tissue density. Moreover, low contrast µC s can be perceived as noise while comparing with the nonhomogeneous background. µCs are observed in mammograms individually or in clusters. Clustered µCs are more likely to be malignant. A cluster is defined as a group, consisting of three 978-0-4799-2235-2/13 $31.00 $26.00 © 2013 IEEE DOI 10.1109/CUBE.2013.47

II.

EXISTING METHODOLOGIES

The detection of µC in digitized mammography is an inverse problem with many alternate solutions. The reported techniques for the µC detection shall be broadly classified as segmentation based, region based, wavelet based, soft computing based, contour based, cluster based and model based. The Table 1 below presents the survey of recently presented literature and their outcomes. From the Table it is learnt that the data set from Mammographic Image Analysis Society (MIAS) database is widely used. Some approaches have claimed True Positive Rate up to 98% and more, however numbers of dataset cases used are less. Evaluation of performance by confusion matrix and ROC curve could be better.

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TABLE I. S.No

Authors

Methods used

1

Irfan Karagoz,et.al 2012 [9]

Unsharp masking, and image statistics

2

Oliver et al. 2012 [10]

3

Fatima Eddaoudi, et al 2011 [11]

4

Jing et al. 2011 [12]

5

Dheeba and Tamil 2011 [13]

6

Mohanalin et al. 2010 [14]

7

Arnau Oliver, et.al 2010 [15]

8

S. Vijaya Kumar, et.al 2010 [16]

9

Dheeba.J, et.al 2010 [17]

10

Yu and Huang 2010 [18]

11

Sankar and Thomas 2010 [19]

12

Rizzi et al. 2009 [20]

Knowledge-based approach Microcalcification Detection in Mammographic Images using Texture Coding: Texture Coding using gray levels. Spatial point process Gray level distribution modelling (µCs are modelled with Gamma, background is modelled by Gaussian), New classification approach using Support Vector Machines (SVM) T sallis entropy, Type II fuzzy index for thresholding. Focal features & boosting classifier: A Boosting Based Approach for Automatic Micro-calcification Detection Watershed segmentation: A novel method for detection of micro calcification based on multi-scale morphological gradient watershed segmentation algorithm Detection of µC clusters in Mammogram using Neural Network: Backpropagation Neural Network Combined model-based and statistical textural features

New Fast Fractal Modeling Approach

Wavelet Decomposition

COMPARATIVE STUDY OF MICRO CALCIFICATION DETECTION TECHNIQUES Number of cases, Source 57

Other methods used

TP (%)

FP (%)

Remarks

MIAS database

1. Laplace filters for contrast enhancement. 2. Low frequency elimination. 3. Gaussian distribution for feature extraction.

94

0.06

No user interaction involved. Selecting the threshold or obtaining image dependent parameters is not encountered in this method.

3 database, 2 of them digitized afterwards

Local feature extraction via filtering.

80

1

Experiments are validated using either digitized or digital mammograms, obtaining slightly better results when testing the digital database.

120 --

Feature extraction by Haralick method, SVM classifier algorithm.

95.6

0.9

Processing is done on the coded images instead of original, this is time consuming.

141 --

The spatial distribution and amplitude parameter extraction of clustered µCs, maximum a posteriori estimation.

90

0.5

The performance of the proposed approach was also demonstrated to be more stable over different compositions of the test images.

322 MIAS

Feature extraction using Law’s texture energy measures, pixel classification via support vector machine.

86.1

-

type II fuzzy index

96.5 5

0.4

247 UCSF and MIAS, 112 MIAS

Proposed approach is superior when compared with several other classification approach discussed in the literature Proposed Tsallis entropy approach outperforms the two-dimensional nonfuzzy approach and the conventional Shannon entropy partition approach

1. Building dictionary using Gaussian derivatives, Laplace filter, a corner detector, 2Sobel operator filters. 2. Convolution of bank filters for extraction.

80

0.9

1. Focus is more on building the dictionary than detecting the tumor. 2. Unable to detect clusters. 3. The algorithm returns false positives.

1. Adaptive mean filter for preprocessing. 2. Morphological operations for image reconstruction. 3. Multi-scale Gradient Image generation.

--

--

Optimal technique to detect micro calcification. Involves segmentation. Problem is to differentiate between benign and malignant tumor

322 MIAS.

Integer Wavelet Transform, Gabor Features Extraction.

85.2

--

Extraction of Gabor features is a tedious task and difficult to implement.

20 MIAS,

Wavelet filter and thresholding, model-based and statistical texture features.

94 90

1 0.65

200 MIAS

For searching; three methods based on mean and variance, dynamic range of the image blocks, and mass center features are used.

87.6 90.5 87.6

-

200 MIAS

Biorthogonal and orthogonal wavelet filter, thresholding.

98 95

1 0.5

280 digitized database

36 MIAS database

214 215

Combined model-based and statistical textural features are suitable for characterizing µC and capable of supporting reliable and effective µCs detection. Proposed method reduced the encoding time by a factor of 3, 89, and 13, respectively, with respect to the conventional fractal image coding method with quad tree partitioning Results shows very good performance compared to traditional methods

13

Halkiotis et al. 2007 [21]

14

Peng et al. 2006 [22]

15

Yu and Guan 2000 [23]

III.

Mathematical morphology, artificial neural network. Knowledge-discovery incorporated evolutionary search

CAD system for the automatic detection

23 MIAS

Morphological filters

94.7

0.27

Results shows improved performance compared to reported methods

100 DDSM

Knowledge-discovery mechanism in the genetic algorithm.

96.8 98.9

0.20 0.4

Incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FµCs and produce improved performance comparing with the conventional GA methods

40 Nijmegen,

Wavelet transform, segmentation using statistical features, 31 feature extractions, feature selection by using neural network.

90

0.5

Proposed system gives quite satisfactory detection performance.

is subjected to watershed segmentation, in which the concept of catchments basin (i.e. watershed) and dam boundaries (i.e. watershed lines or divide lines) is involved. The watershed transform is applied on the final gradient marker extracted image and original image. The resulting image is corrected by cloned data. The processes followed are almost similar to OMMGW only with an exception that corrections by cloned data after watershed segmentation method yields well detected and highlighted µC.

PROPOSED METHODOLOGY

Before The proposed methodology is an improved version of O-MMGW, where we have incorporated corrections to watershed segmentation result by using cloned image to yield better detection and highlighting the µC. the below Fig. shows the system model for proposed I-MMGW method

The pseudo code of correction by cloning is as given below Create the copy of the original_image Convert the output _watershed_segmented image from BGR to RGB If pixel_value (output _watershed_segmented)>threshold Mark it red Else No change Add the result to copy of original_image to display final image

A. Experimental set up and results The experimental results were performed using two different sets of mammograms. The first one was the full (digitized) MIAS database [24], from which we used 200 mammograms, 150 mammograms with µC, and 50 mammograms containing other types of abnormalities (masses, speculations, architectural distortions, and asymmetries) which are non µC . The spatial resolution of the images was 50 μm × 50 μm and the optical density was linear in the range 0 − 3.2 and quantized to 8 bits. The second set of mammograms was a set of 100 full-field digital mammograms collected from local NMR diagnostic center, Hubli, 50 of them containing µC and 50 mammograms without abnormalities. The mammograms were acquired using a MAMMOMAT® Inspiration of SEIMENS company, with resolution 75 micronpixel, size 4096 × 3328, and 12-bit depth.

Fig. 1. Proposed I-MMGW methodology

The pre-processing phase is very essential in mammogram image analysis as it is observed that mammography images exhibit Gaussian type of noise. Adaptive Median filtering has been found to be very effective in removing noise from mammography without degrading the quality of image. This makes it particularly suitable for enhancing mammogram images. The morphological operations such as dilation and erosion are carried out on the pre-processed images. In order to smoothen the micro calcifications morphological closing is performed, which is achieved by using partial reconstruction operator, dilated image and reference image. Further to obtain gradient image, the eroded image has to be subtracted from the dilated image. Multiscale gradient image is obtained by taking the average of morphological gradients taken depending upon the structure element. This multiscale gradient image is further processed to obtain marker extracted image in order to avoid over segmentation problem. In this process of marker extraction the homogeneous regions in the image are detected and are eliminated using markers. The marker extracted image

B. Performance evaluation To perform the quantitative evaluation of the micro calcification detection algorithm we used Receiver Operating Characteristic (ROC) analysis. In this analysis, a graphical curve represents the true positive rate (number of detected mammograms with µC divided by the total number of mammograms with µC) as a function of the false positives rate (number of normal mammograms incorrectly detected as containing µC divided by the total number of normal mammograms).

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C. Confusion Matrix In predictive analytics, a Table of confusion, also known as a confusion matrix, is very useful for evaluating classifiers, as they provide an efficient snapshot of its performance displaying the distribution of correct and incorrect instances TABLE II.

Method Data Set I-MMGW -MIAS O-MMGWMIAS I-MMGWNMR Scan center O-MMGWNMR Scan center

CONFUSION MATRIX Actual value Condition Condition Positive Negative

Test outcome positive Test outcome negative

Test outcome

True Positive

False Negative

 TruePositive *100  ConditionP ositive  TrueNegative *100 SP   ConditionN egative

FN

TN

FP

TP RATE

FP RATE

SE

SP

143

7

43

7

0.953

0.14

95.3

86

112

38

34

16

0.746

0.32

74.6

68

47

3

42

8

0.94

0.16

94

84

38

12

36

14

0.76

0.28

76

72

False Positive

Fig. 2 shows the ROC curve for proposed I-MMGW and original MMGW approaches for different datasets. From Fig. 2, we can clearly record that points of I-MMGW dominates the points of other approaches. It’s worth observing that TP rate remains constant in case of I-MMGW while that of O-MMGW doesn’t. From the graph we shall conclude that I-MMGW is better technique for µC detestation. This is because of addition of corrections by cloned data yields in better highlighted data.

True Negative

The performance of all algorithms is evaluated by computing the percentages of Sensitivity (SE), and Specificity (SP), the respective definitions are as follows: SE 

TP

(1)

(2)

D. The ROC Method The most suitable evaluation method for µC detection method is the Receiver Operating Characteristic method. In ROC analysis, instead of a single value of accuracy, a pair of values is recorded for different class and cost conditions a classifier is learned. The values recorded are the False Positive rate (FP) and the True Positive rate (TP), defined in terms of the confusion matrix as: FP 

fp ( fp  tn )

(3)

TP 

tp (tp  fn )

(4)

Fig. 2. The ROC curve for I-MMGW and O-MMGW

One more way of comparing proposed method is by sensitivity and specificity. The Fig. 3 presents the comparison. It is evidenced that both sensitivity and specificity of IMMGW is better than O-MMGW, but worth noting is OMMGW performs better with lesser number of cases, this is also reported in the literature by S. Vijaya Kumar, et.al [16].

In this formula, fp is the number of false positives; tp is the number of true positives. Each (FP, TP) pair is plotted as a point in the ROC space

IV.

RESULTS AND DISCUSSION

The proposed technique is tested once by using mammography images from MIAS database and once with data collected locally. The following Table 3 shows the records. In the Table Fig. 3. The sensitivity & specificity of I-MMGW and O-MMGW

True positive: presence of µC is correctly detected False positive: absence of µC incorrectly identified as present True negative: absence of µC is correctly detected as not present False negative: presence of µC is incorrectly identified as not present TABLE III.

The step by step results of O-MMGW and I-MMGW are mentioned in the below Table 4. The result of I-MMGW is very clearly highlighted at the last stage.

SENSITIVITY AND SPECIFICITY OF O-MMGW AND I-MMGW TABLE IV.

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STEP BY STEP COMPARISION OF O-MMGW AND I-MMGW

Details Original mammogram image

O-MMGW

Details Original mammogra m image

I-MMGW

External Marker image

Watershed segmentatio n

Filtered Mammogram Image Using adaptive median filter

Filter

Marker modified gradient image

Correction by cloned data

Morphological ly reconstructed

Dialate

Detection of µc

Final detected and highlighted µc

Multi-scale Gradient Image

Erode

V.

Final gradient image generation

Multi-scale gradient image

Oversegmentation problem

Gradient

Regional minimas of Final Gradient image.

Final Gradient Image Generation

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Internal Marker image of mammogram.

CONCLUSION

In this article, we developed a improved multiscale morphological watershed segmentation method for the detection of micro calcifications in mammographic images. This method uses the cloned image after watershed segmentation method. Through this method, we found that the clusters as well as minute µC are highlighted by red colored boundaries. The experimental results showed that the true positive and false positive rate are better compared to original multiscale morphological watershed segmentation method. The true positive rate is observed to be 0.953 and 0.94 for MIAS and NMR datasets respectively which better than O-MMGW by approximately 20%. The future work shall be to overcome highlighted portion on the boundaries of mammography, and also experimenting using datasets from few more public medical database..

[2]

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[4] [5]

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