Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection

Abstract

An efficient approach for classification of mammograms for detection of breast cancer is presented. The approach utilises the two-dimensional discrete orthonormal S-transform (DOST) to extract the coefficients from the digital mammograms. A feature selection algorithm based the on null-hypothesis test with statistical 'two-sample t-test' method has been suggested to select most significant coefficients from a large number of DOST coefficients. The selected coefficients are used as features in the classification of mammographic images as benign or malignant. This scheme utilises an AdaBoost algorithm with random forest as its base classifier. Two standard databases Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) are used for the validation of the proposed scheme. Simulation results show an optimal classification performance with respect to accuracies of 98.3 and 98.8% and AUC (receiver operating characteristic) values of 0.9985 and 0.9992 for MIAS and DDSM, respectively. Comparative analysis shows that the proposed scheme outperforms its competent schemes.

Keywords: AdaBoost algorithm; Digital Database for Screening Mammography database; Mammographic Image Analysis Society database; breast cancer detection; cancer; feature extraction; feature selection; image classification; learning (artificial intelligence); mammogram classification; mammography; medical image processing; null-hypothesis test; statistical testing; statistical two-sample t-test method; two-dimensional discrete orthonormal S-transform.

Figure 1

Extracted ROIs from different mammographic images (source: MIAS database) a–d Benign type e–h Malignant type

Figure 2

Block diagram of proposed scheme for classification of mammograms

Figure 3

ROC curves obtained by three classifiers at optimum significance level, α = 7 × 10⁻⁴ a MIAS database b DDSM database