A new segmentation framework for infrared spectroscopic imaging using frequent pattern mining

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Histologic analysis of a stained tissue sample by a trained pathologist forms the definitive diagnosis of prostate cancer. Rapid and objective second opinions are highly desirable to make more accurate diagnostic decisions. One alternate method is to use Fourier transform infrared (FT-IR) spectroscopic imaging, which is an emerging technique that combines the molecular selectivity of spectroscopy with the spatial specificity of optical microscopy. While instrumentation is well-developed for FT-IR imaging, information extraction from the data could benefit greatly from improved approaches. Here we propose a new approach to segment histologic classes in a tissue for FT-IR imaging using frequent pattern mining. Prior to applying frequent pattern mining, FT-IR images are discretized, and subsequent pruning method and feature selection method result in a classifier for the segmentation. The method is evaluated using two different datasets. Results indicate that accurate histologic segmentation is achievable by this approach.

Original languageEnglish (US)
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages452-455
Number of pages4
DOIs
StatePublished - Nov 2 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: Mar 30 2011Apr 2 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
CountryUnited States
CityChicago, IL
Period3/30/114/2/11

Keywords

  • discretization
  • feature selection
  • frequent pattern mining
  • histological segmentation
  • Infrared spectroscopic imaging

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Kwak, J. T., Sinha, S., & Bhargava, R. (2011). A new segmentation framework for infrared spectroscopic imaging using frequent pattern mining. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 (pp. 452-455). [5872443] (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2011.5872443