Mammogram Analysis Mammogram Analysis – Tumor classification– Tumor classification

- Geethapriya - Geethapriya RaghavanRaghavan

BackgroundBackground Mammogram – Mammogram –

X-Ray image (of gray levels) of inner X-Ray image (of gray levels) of inner breast tissue to detect cancerbreast tissue to detect cancer

Shows the levels of contrast characterizing Shows the levels of contrast characterizing normal tissue and vesselsnormal tissue and vessels

Issues –Issues – Detect abnormalities (tumors)Detect abnormalities (tumors) Diagnosis - Classify as benign or malignantDiagnosis - Classify as benign or malignant Remove noiseRemove noise

MicrocalcificationsMicrocalcifications

Mammograms obtained from MIAS database

Methods ..Methods .. Non-linear classifiers preferred over linear classifiers Non-linear classifiers preferred over linear classifiers

given the randomness in occurrence of tumor cellsgiven the randomness in occurrence of tumor cells Contemporary methods - supervised learning Contemporary methods - supervised learning

problem (Wei problem (Wei et al., et al., 2005)2005) Support Vector Machines (SVM)Support Vector Machines (SVM) (Vapnik (Vapnik et al., et al.,

1997)1997) Kernel Fisher Discriminant (KFD)Kernel Fisher Discriminant (KFD) Relevance Vector Machines (RVM)Relevance Vector Machines (RVM)

Method I - SVMMethod I - SVM SVM was used by Chang SVM was used by Chang et al.,et al., on US images on US images Texture feature – Texture feature – microcalcification area, contrastmicrocalcification area, contrast.. Software – SVM Light (Software – SVM Light ((http://svmlight.joachims.org/)(http://svmlight.joachims.org/)

The best fitting hyperplane f(x) = wThe best fitting hyperplane f(x) = wT . T . x + b forms the x + b forms the boundaryboundary

For non-linear SVM, the ‘x’ in the above equation is For non-linear SVM, the ‘x’ in the above equation is replaced by a nonlinear function of ‘x’. replaced by a nonlinear function of ‘x’.

Method IIMethod IIUse of wavelet transform to decorrelate data Use of wavelet transform to decorrelate data

(image) (Borges (image) (Borges et al.,et al., 2001) 2001) Obtain wavelet coefficients as featuresObtain wavelet coefficients as features Normalize coefficients and feed into Nearest Normalize coefficients and feed into Nearest

Neighborhood classifierNeighborhood classifier Wavelet decomposition - Low frequency Wavelet decomposition - Low frequency

coefficients extracted at coefficients extracted at twotwo levels and NNR run levels and NNR run with with euclidean distanceeuclidean distance as metric. as metric.

ResultsResults

Classifier Microcalcification Contrast Microcalcification Area

Non-linear SVM 67.7 % 78 %

Linear SVM 42.8 % 70.4 %

NNR 72 % 76.2 %

Results - Results - ROCROC Sensitivity = Number of True Positive Sensitivity = Number of True Positive

ClassificationsClassifications Number of Malignant LesionsNumber of Malignant Lesions

Specificity = Number of True Negative Specificity = Number of True Negative ClassificationsClassifications

Number of Benign LesionsNumber of Benign Lesions

Sensitivity (y) vs. Specificity (x)Sensitivity (y) vs. Specificity (x) Dotted = lower boundDotted = lower bound Red line = Wavelets + NNRRed line = Wavelets + NNR Black curve = linear SVMBlack curve = linear SVM

specificity0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1