Signal processing for feature extraction and pattern recognition

A new feature extraction technique designed specifically for the mass screening problem is presented. It is based on the hypothesis that feature values from the normal class tend to form a cluster while those from the abnormal class fall outside this cluster, or "decluster". While some feature extraction techniques attempt to retain the fidelity of the data from which the features arose, the new technique is designed to enhance the discrimination between the normal and abnormal classes. Results from applying the new algorithm to the mass screening of breast thermograms to detect breast cancer are discussed.