Cosine histogram analysis for spectral image data classification.

Abstract

Conventional multivariate strategies for making qualitative estimates of sample composition rely chiefly on identifying subtle differences in spectral shape. In some instances, such as in biological tissues, the spectra obtained from a single sample class may consist of many shapes. Likewise, two distinctly different sample classes, such as normal and abnormal tissue, may produce similar variations in spectral shape. In our work, we employ statistical analysis of the set of cosine correlation scores obtained from multispectral visible absorption images of stained cervical Papanicolaou samples. By analyzing the cosine correlation score frequency for spectra obtained from the cell nuclei, abnormal cells can be differentiated from the background of normal cells, which vary considerably in their optical properties and morphology. Our method, called cosine histogram analysis (CHA), returns the percent likelihood of abnormality for each pixel in the field of view and is presented here for the first time.