Classification of ex-vivo breast cancer positive margins measured by hyperspectral imaging

This paper presents our recent development of a classification algorithm for identification of breast cancer margins measured by hyperspectral imaging for the purpose of lowering the number of missed positive margins in breast cancer lumpectomy. After extracting Fourier coefficient selection features and reducing the dimensionality of hyperspectral image data via the Minimum Redundancy Maximum Relevance method, an SVM classifier involving a radial basis kernel function is deployed to separate cancerous tissues from normal tissues. By examining exvivo breast cancer hyperspectral images tagged by a pathologist, the developed classification approach is shown to achieve a sensitivity of about 98% and a specificity of about 99%.