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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
IR Spectrometers01:25

IR Spectrometers

There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...

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High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
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Artificial neural networks as supervised techniques for FT-IR microspectroscopic imaging.

Peter Lasch1, Max Diem, Wolfgang Hänsch

  • 1P25 "Biomedical Spectroscopy", 13353 Berlin, Nordufer 20, Germany.

Journal of Chemometrics
|December 5, 2009
PubMed
Summary

This study demonstrates an improved method for segmenting infrared microspectroscopy data from colorectal cancer tissues using artificial neural networks (ANNs). The technique enhances diagnostic accuracy by optimizing classification models for histological specimens.

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Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Computational Biology

Background:

  • Fourier transform infrared (FT-IR) microspectroscopy generates hyperspectral data crucial for analyzing histological specimens.
  • Accurate image segmentation is vital for interpreting complex spectral data from tissue samples.
  • Existing methods may require optimization for enhanced diagnostic sensitivity and specificity.

Purpose of the Study:

  • To demonstrate an improved method for image segmentation of FT-IR microspectroscopic data from histological specimens.
  • To develop and validate artificial neural network (ANN) models for classifying colorectal adenocarcinoma tissues.
  • To optimize diagnostic sensitivity and specificity through tailored ANN model parameters.

Main Methods:

  • Acquisition of hyperspectral FT-IR microspectroscopic data from human colorectal adenocarcinomas.
  • Creation of a database comprising 4120 FT-IR point spectra from 28 patient samples.
  • Training and validation of multilayer perceptron artificial neural network (MLP-ANN) models, including hierarchical classification schemes.
  • Utilizing agglomerative hierarchical clustering (AHC) for initial model generation and class definition.

Main Results:

  • Successful application of an improved image segmentation method for FT-IR microspectroscopic data.
  • Development of MLP-ANN models capable of classifying tissue spectra with high accuracy.
  • Demonstration that diagnostic sensitivity and specificity can be optimized by adjusting teaching patterns and ANN topology.
  • Hierarchical ANN classification and AHC-informed class definitions yielded superior classification results.

Conclusions:

  • The developed FT-IR microspectroscopy and ANN-based image segmentation method offers a powerful tool for analyzing histological specimens.
  • Optimized ANN models, particularly hierarchical approaches informed by clustering, significantly improve the reliability of tissue classification.
  • This approach holds promise for enhancing diagnostic capabilities in histopathology.