Skip to main content

Cosine Histogram Analysis for Spectral Image Data Classification

Buy Article:

$29.00 plus tax (Refund Policy)


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.

Keywords: Carcinoma; Chemical imaging; Chemometric analysis; Cosine correlation analysis; Multivariate analysis; Spectral imaging

Document Type: Research Article


Affiliations: Department of Chemistry, Cleveland State University, Cleveland, Ohio 44115

Publication date: November 1, 2004

More about this publication?

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more