Hybrid Discrete Wavelet Transform and Histogram of Oriented Gradients for Feature Extraction and Classification of Breast Dynamic Thermogram Sequences

Breast cancer is responsible for the death of thousands of women around the world. A cancerous tumour increases temperature in the region near to the tumour, such heating is then transferred to the skin surface. Early breast cancer detection can save many lives and reduce the cost of treatment. There are many imaging techniques to do the screening such as thermography. To achieve an early detection, Computer-Aided Detection (CAD) systems are needed to classify masses in thermogram images. In this research, we propose a hybrid methodology based on Histogram of Oriented Gradients (HOG) and Discrete Wavelet Transform (DWT) to classify temporal-based thermogram images into either normal or cancerous. In this study we propose a technique to reduce the HOG coefficient vector and extract features from this vector. Two-dimensional Discrete Wavelet Transform (DWT) is applied on the original images to obtain HH (high-high) sub band. Then, HOG is applied using the HH sub band. Support Vector Machine (SVM) binary classifier is used to classify images to either normal or abnormal using the extracted features. The obtained results showed comparable results with state-of-the-art related research.