Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms

• Pectoral regions of two breasts are decomposed using discrete separable wavelet.
• Pectoral regions of two breasts are decomposed using dual-tree complex wavelet.
• Got the 1st and 2nd order statistical parameters with sub-band images of 2 breasts.
• Principle Component Analysis and an Adaboost classifier are applied.
• Complex wavelet performs better than separable ones for malignant vs. non-malignant.

Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts.