Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis

Abstract

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.

Imaging live cells grown on a three dimensional collagen matrix using Raman microspectroscopy

“…K-means clustering analysis is currently commonly used for the analysis of tissue sections by infrared and Raman spectroscopy, especially for the detection of the different structures present in the samples studied and the identification of tumoral regions [44][45][46] . K-means clustering can also be employed for the study of individual cells by Raman spectroscopy but only a few studies have elucidated the subcellular organisation and they are usually based on fixed cells grown on a non optimal substrate such a quartz window or equivalent 23,47,48 .…”

Chapter 10 Infrared and Raman Microscopy in Cell Biology

“…These spectral images are based solely on the similarity of the spectra in a hyperspectral data set. A detailed description of HCA has been published (Diem et al, 2004;Lasch et al, 2004;Wood et al, 2004). For HCA, the pair-wise similarity coeffcients of all spectra in a data set are collected as a matrix of correlation coeffcients C, which contains N 2 entries, where N is the total number of spectra in a data set.…”

In vivo photothermal flow cytometry: Imaging and detection of individual cells in blood and lymph flow

“…For example, AS has been successfully used to measure changes in the redox state of Cyt c in mitochondria and to identify cellular Cyt-c oxidase in vivo [Jacobs and Worwood, 1974]. Even in the IR range in the presence of a strong absorption background from water and proteins, AS has the potential to distinguish healthy from cancerous cells, as well as normal cells with different metabolic activities, or apoptotic cells having the spectral signatures of DNA, RNA, and phospholipids [Lasch et al, 2004].…”