Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis.
In this paper, three different clustering algorithms were applied to assemble infrared (IR) spectral maps from IR microspectra of tissues. Using spectra from a colorectal adenocarcinoma section, we show how IR images can be assembled by agglomerative hierarchical (AH) clustering (Ward's technique), fuzzy C-means (FCM) clustering, and k-means (KM) clustering. We discuss practical problems of IR imaging on tissues such as the influence of spectral quality and data pretreatment on image quality. Furthermore, the applicability of cluster algorithms to the spatially resolved microspectroscopic data and the degree of correlation between distinct cluster images and histopathology are compared. The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to univariate methods of IR imaging (functional group mapping). Among the cluster imaging methods, AH clustering (Ward's algorithm) proved to be the best method in terms of tissue structure differentiation.
Lasch
Peter
P
Department of Chemistry and Biochemistry, City University of New York, Hunter College, 695 Park Avenue, New York, NY 10021, USA. LaschP@rki.de
Haensch
Wolfgang
W
Naumann
Dieter
D
Diem
Max
M
D000230:Adenocarcinoma / Q000737:chemistry* / Q000473:pathology*; D000465:Algorithms; D016000:Cluster Analysis; D015179:Colorectal Neoplasms / Q000737:chemistry* / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*; D013194:Staining and Labeling