Wavelet based thermogram analysis for breast cancer detection

In this work, we have reported a novel automatic technique to detect early breast cancer by analyzing breast thermogram. The system comprises mainly of three steps: the breast region segmentation from the original image, feature extraction, and finally classification and performance analysis. In the segmentation phase, we have first removed the background region by applying the Otsu's thresholding method followed by a reconstruction technique. Then the inframammary fold is detected to mark the lower limit of the breast. After that, the upper limit of the breast is identified by discerning the axilla. Finally, the breast region is extracted based on these two limits. In the next phase, we have extracted features from the region of interest to identify the early breast cancer, which is the most crucial and significant step. At the very beginning of the feature extraction process, we have computed the initial feature point image (IFI) for each segmented breast thermogram by applying discrete wavelet transform (DWT). After that, different types of features are extracted from the IFI for the diagnosis of breast cancer. Finally, feed-forward artificial neural network with gradient decent training rule is employed here as a classifier. The major problem in this research is the limited collection of publicly available breast thermal databases. At present, there is only one standard breast thermal database available updated by Visual Lab, Fluminense Federal University, Brazil. We are also developing a new database, which will help the researcher to do research in this field. In this work, we have used 306 breast thermograms of 102 patients collected from Visual Lab. In our proposed system, we found the accuracy of 90.48%, whereas sensitivity and specificity were 87.6% and 89.73% respectively.