MAMMOGRAM IMAGE ANALYSIS

1

MAMMOGRAM IMAGE ANALYSIS

E.MALARAssociate Professor & Head

PSG Institute of Technology and Applied ResearchCoimbatore

OVERVIEW

Breast Cancer Research Need & Present State – Social Responsibility

Significance of Mammography

Need for Preprocessing & Methods

CAD & Diagnosis

I.Microcalcification Detection

II.Mass & Microcalcification Detection

III.A GUI based Comprehensive tool for Breast Cancer Analysis

2

3

SOCIAL RESPONSIBILITY

Breast Cancer - Most common cancer among women

Every year more than 500,000 women die of breast cancer

Globally, on an average one out of eight women and in India one in 22

women is predicted to develop the disease

Therapeutic actions are successful in early stage

Some of the breast lesions are missed during screening by radiologist

4

SIGNIFICANCE OF MAMMOGRAPHY Identify structural or morphological differences that indicate presence

of

cancer

Conventionally, the image recorded on film and later digital format

Currently it is the only medical imaging modality used in screening.

With about 70% sensitivity and 30% positive predictive value,

Mammography screening reduce breast cancer mortality by 25% to 30%

for women in the age group of 50 to 70.

5

MAMMOGRAPHIC BREAST ANATOMY

BREAST BOUNDARY

DENSE TISSUE

FAT TISSUE

PECTORAL MUSCLE

LABEL

BLACK BACK GROUND

Fig.2. Normal Mammogram image and its contents

6

MAMMOGRAPHIC VIEWS

CCMLO

Fig.3. Two views - Carnio Caudal (CC) and Medio Lateral Oblique (MLO)

7

MAJOR BREAST ANOMALIES

MicrocalcificationsCoarse calcium deposits

Size ranges from 0.1 - 1.0 mm, average diameter - 0.3 mm

Tight clusters of microcalcifications indicate early breast cancer - 3

microcalcifications within 1 sq.cm

Associated with 30%-50% of malignant cancers

10%-40% are missed by radiologists due to its small size and non-

palpable

8

Mass

Suspicious region denser than surrounding tissues

With irregular , spiculated or circumscribed margins

Size varies

Margins play a vital role in diagnosis.

MAJOR BREAST ANOMALIES

9

I. CAD SYSTEM FOR MICROCALCIFICATION DETECTION

10

Table 1.MINI –MIAS DatabaseS.NO. LESION TOTAL

1. Normal 207

2. Spiculated Mass 19

3. Circumscribed Mass 24

4. Microcalcification 25

5. Breast Asymmetry 15

6. Architectural distortion 19

7. Ill-defined mass 15

8. Total 322

11

Lesion Risk

No. of images in Mini-MIAS

No. of Images used in the

proposed CAD system

Images used in the proposed CAD system

Normal - 207 30

Dense Glandular - mdb 003, 004, 033, 036, 040, 054, 062, 254, 261, 286.

Fatty - mdb006, 009, 011, 027, 076, 098, 135, 272, 300, 311.

Fatty Glandular – mdb 007, 008, 014, 022, 043, 071, 086, 210, 263, 292.

Microcalcifications

Benign 13 13mdb 218, 219, 222, 223(2), 226(3), 227, 236,

240, 248, 252.

Malignant 12 12mdb 209, 211, 213, 231, 238, 239(2), 241,

249(2), 253,256.

Table 2.MINI - MIAS images used in proposed Microcalcification detection

12

Fig.7 .(a) Raw image from the database (mdb211) (b) Image after binarization (c) Image showing connected components (d) Image after label removal (e) Image after black background removal

PREPROCESSING

13

Gray Level Spatial Dependence Matrix features

(GLSDM)

Gabor filter based features

Wavelet Transform based features

FEATURE EXTRACTION

14

GLSDM BASED FEATURES

• GLSDM is a well-established statistical tool for extracting second order

texture information from images (Haralick et al (1973), Soh & Tsatsoulis

(1999) and Clausi (2002)).

• The GLSDM characterizes the spatial distribution of grey levels in an

image.

• Mapping of the mammogram image into a smaller version (based on pixel

bit depth)

15

FEATURE EXTRACTION - GLSDM

Fig.9. Creating a GLSDM

16

Table 3 GLSDM features used for microcalcification detection

17

GABOR FILTER BASED FEATURES• Gabor function is a complex exponential modulated by a Gaussian function (Gabor,1946).

• Its impulse response in the 2D plane has the following general form

u - radial frequency of the Gabor function, - Spread of Gabor envelop along x and y

axes

• With the above mother Gabor wavelet, the self-similar filter bank can be obtained by

appropriate scaling and rotation of the following function (Manjunath & Ma, 1996)

Index for scale (dilation) p=0,1,2,…,S-1, orientation (rotation) q=1,2,…,L-1.

S - total number of scales, L - the total number of orientations.

18

WAVELET BASED FEATURES

The wavelet transform is used to analyze different frequencies of an

image

using different scales.

The filter bank of two dimensional wavelet transform •S is the original Input image.

•S1 is the smoothed version of original image (approximate component)

•W1H is the horizontal detail component

•W1V is the vertical detail component

•W1D is the diagonal detail component.

•H (ω) is low pass filter and G (ω) is high pass filter.

Fig. 10 Two dimensional undecimated wavelet transform structure

19

BAYES CLASSIFIER

• Bayesian decision theory is a fundamental statistical approach to the

problem of pattern classification.

• Decision problem is posed in probabilistic terms

• Bayes formula calculates posterior probability as

Where,

20

NAÏVE BAYES CLASSIFIER

• Probabilistic classifier based on Bayes theorem.

• It assumes that the effect of value of each attribute on a given class is

independent of value of other attributes - conditional independency.

• As variables are independent - only variances need to be determined and not

the entire covariance matrix.

• It requires only a small amount of training data to estimate the parameters

(means and variances of the variables)

21

SUPPORT VECTOR MACHINE• SVM is a learning tool based on modern statistical learning theory (Vapnik

1995).

• It constructs a separating hyper surface in the input space

• Maps the input space into a high dimensional features space through non

linear mapping

• Chooses a priori (kernel) or constructs in this features space the Maximal

Margin Hyper Plane (Cortes & Vapnik 1995).

Figure 11 Support vector machine based classification

22

EXTREME LEARNING MACHINEELM proposed by Guang et al (2006)

It comes under the class of Single Layer Feed

Forward Network

Learning speed is thousand times faster than

conventional feed forward networks.

Better generalization - as the input weights

and

hidden layer biases can be randomly assigned.

No parameters need to be manually tuned

except

predefined network architecture

Figure 12 Structure of ELM network

23

ELM STRUCTUREFor a sample of N data -

Hidden neurons -

Activation function g(x) is modeled as

Weight vector connecting the ith hidden and input neurons -

Weight vector connecting the ith hidden and output neurons -

Output vector of SLFN -

Threshold of ith hidden neuron -

These N equations can be written compactly as given by

The resulting training error is minimized by solving

Its least square solution with minimum norm is given by

24

(a) GLSDM (b) Gabor (c) Wavelet Figure 13 Efficiency vs. No. of hidden neurons

PERFORMANCE EVALUATION AND RESULTS

0 10 20 30 40 50 60 70 80 900

10

20

30

40

50

60

70

80

90

100

Effi

cien

cy(%

)

Number of Hidden Neurons

Unipolar Gaussian Bipolar

0 10 20 30 40 50 60 70 800

10

20

30

40

50

60

70

80

90

Effi

cien

cy (%

)



0 10 20 30 40 50 60 70 800

10

20

30

40

50

60

70

80

90

100

Effi

cien

cy (%

)



25

PERFORMANCE METRICS

• FP is the number of False Positives, TP is the number of True Positives,

• P is the total number of Positives, and N is the total number of Negatives.

26

PERFORMANCE ANALYSIS

ClassifierTrue Positive Rate

GLSDM Gabor WaveletBayes Classifier 0.70 0.74 0.87Naïve Bayes Classifier 0.72 0.61 0.90Support Vector Machine 0.72 0.80 0.90Extreme Learning Machine 0.90 0.95 0.98

ClassifierFalse Positive Rate


Table 4 True Positive Rate for different classifiers and feature vectors

Table 5 False Positive Rate for different classifiers and feature vectors

27

ClassifierArea under the curve


ClassifierPrecision


ClassifierF-measure


Table 6 Precision for different classifiers and feature vectors

Table 7 F-measure for different classifiers and feature vectors

Table 8 Area under the curve for different classifiers and feature vectors

28

ROC ANALYSIS

Figure 14 ROC curves for GLSDM based feature vector

29

Figure 14 ROC curves for Gabor Filter based feature vector

ROC ANALYSIS

30

Figure 14 ROC curves for Wavelet based feature vector

ROC ANALYSIS

31

III. A GUI BASED COMPREHENSIVE TOOL FOR BREAST CANCER ANALYSIS

32

PECTORAL MUSCLE REMOVAL

REGION OF INTEREST

SOBEL GRADIENT

HOUGH TRANSFORM

Figure 26 The flow diagram for pectoral muscle removal

33

REGION OF INTEREST

• Pectoral muscle would be present in the top half of the image.

Figure 27 The ROI selection for pectoral line detection

34

SOBEL GRADIENTROI

Figure 28 (a) ROI and (b) output of Sobel gradient

35

FEATURE EXTRACTION

• The features of digital images can be extracted directly from the spatial data or

from a different space.

• Using a different space by a transform can be helpful to separate a special data.

Three types of features were extracted

Wavelet coefficients

Curvelet coefficients

Contourlet coefficients

36

CLASSIFICATIONFactors to be considered

Computational time

Accuracy of classification

Sensitivity of classification

Unsusceptible to local minima and longer training epochs.

Three classifiers are used :

Support vector machine

Extreme Learning Machine

Phase encoded complex extreme learning machine

37

ABNORMALITY ANALYSIS

Cancer Normal

ABNORMALITY DETECTION

38

EFFICIENCY

SVM ELM BAYES 0

10

20

30

40

50

60

70

80

90

100

43.1

73.3376.67

100

66.67 66.67

77

91.3

85.47

WAVELETS CURVELETSCONTOURLETS

CLASSIFIER

EFFI

CIEN

CY (

%)

Figure 33 Comparison of testing efficiency of various combinations of features and classifiers for Abnormality analysis

39

CANCER CHARACTERIZATION

Cancer

Micro calcification

Spiculated

Asymmetry

Architectural distortion

Circumcised

Miscellaneous

40

EFFICIENCY

WAVELETS CURVELETS CONTOURLETS0

10

20

30

40

50

60

70

80

90

100

20.68

96.55

16

46.6743.75

50

36.38

44.96

33.33

SVMELM BAYES

CLASSIFIER

EFFI

CIEN

CY (%

)

Figure 34 Comparison of testing efficiency of various combinations of features and classifiers for cancer characterization analysis

41

RISK ANALYSIS

RISK ANALYSIS

Benign Malignant

42

EFFICIENCY


10

20

30

40

50

60

70

80

90

100

62.06

100

48.27

80.31 82.31

75.8678.39 78.36

71.87

SVMELM BAYES

CLASSIFIER

EFFI

CIEN

CY (%

)

Figure 35 Comparison of testing efficiency of various combinations of features and classifiers for Risk analysis

43

TISSUE DENSITY ANALYSISTISSUE DENSITY

Fatty Fatty glandular

Dense glandular

44

EFFICIENCY


10

20

30

40

50

60

70

80

90

100

30.76

98.22

42.59

66.08

51.8

60.3764.81

58.14

52.24

SVMELM BAYES

CLASSIFIER

EFFI

CIEN

CY (%

)

Figure 36 Comparison of testing efficiency of various combinations of features and classifiers for tissue density analysis

45

DENOISING – SPECKLE NOISE

Figure 37 Denoising of Random noise

46

SNR COMPARISON

NoisesWavelet

SNR/dB

Contourlet

SNR/dB

Curvelet

SNR/dB

Random 21.59 23.65 16.51

Salt & pepper 6.76 11.65 32.94

Poisson 25.71 25.55 23.14

Gaussian 16.96 17.84 57.51

Speckle 16.69 19.13 22.13

Table 16 Comparison Of Wavelet, Contourlet And Curvelet Based Denoising Methods

SCOPE FOR FUTURE WORK

The clinical information may be added to help in epidemiological studies.

Mammograms can be combined with different imaging modalities like

MRI and Ultrasound.

Dense breast tissue difficult in classifying begin and malignant breast

disease.

Categorize the different types of mass.

48

THANK YOU

MAMMOGRAM IMAGE ANALYSIS

Engineering

Transcript of MAMMOGRAM IMAGE ANALYSIS