Using series of infrared data and SVM for breast normality evaluation

Breast cancer is one of the cancer types most commonly diagnosed among women worldwide. Diagnostic techniques are constantly being developed. Dynamic thermography emerges as a tool to aid in this process. Images captured under dynamic protocol were used here to obtain breast behavior on achieving thermal equilibrium. The regions of interest (ROIs) are segmented from these images and used for analysis of the temperatures during the time of the exam. Features based on statistic and clustering are used in these analyses. Time series are formed with these features using combinations of intervals constructing subsets of different cardinalities for following their evolution over time. Groups of features are classified by Support Vector Machine, using the Leave-One-Out Cross-Validation method. Achieved results for classifications on healthy or abnormal breast from a sample of 64 breasts (half healthy and half with some abnormality) are presented.