A New Feature Extraction Approach Using Contourlet Transform and T-Test Statistics for Mammogram Classification

Year 2020, , 16 - 20, 31.01.2020

Nebi Gedik

https://doi.org/10.17694/bajece.557693

Abstract

In this study, a CAD
system is recommended for the classification of mammography images as
normal-abnormal and benign malignant. The proposed system consists of the
feature extraction, determination of the distinguishing capabilities of the
features and selection of the features using by dynamic thresholding according
to the determined distinguishing capabilities. It uses the contourlet transform
to extract features. The distinguishing capabilities of the features are
determined by using t-test statistics, and the thresholds are applied to those
values to select effective ones. Classification is performed using support
vector machine algorithm for every iteration with each thresholding step. Among
the results of the iteration performed, the optimum data that have the best
performance, which is they have maximum accuracy result with the minimum number
of features, is selected as the optimum value. To evaluate the optimal feature
set, classification carries out using the feature set applying 5-fold
cross-validation. According to the results, the proposed method can be accepted
as a successful CAD system. 

Keywords

Classification, CAD, Mammogram, Contourlet transform

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