Composite imaging method for histological image analysis

Mizuho Imai, Akane Takei, Keita Miyamoto, Masanobu Takahashi, Masayuki Nakano

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

Abstract

A composite imaging method has been developed that enables the user to directly capture a composite image by one-image capturing. It was experimentally verified that the composite images of bright-field, dark-field, and phase-contrast images can be captured with an arbitrary composition ratio. The difference in pixel values between the captured composite image and the computer composite image was small. This imaging method is realized only by placing below the condenser a masking plate, which can easily be made using Neutral Density filters. Therefore, little additional time and cost are needed. The composite imaging method was applied for extracting Helicobacter pylori in microscopic images of HE-stained gastric histological sections. H pylori is difficult to extract because the colors in H pylori are similar to those in other areas. It is experimentally shown that a composite image of phase-contrast and dark-field images captured using the proposed method improves the accuracy for extracting H pylori.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages3387-3390
Number of pages4
DOIs
Publication statusPublished - 2013 Oct 31
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 2013 Jul 32013 Jul 7

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Country/TerritoryJapan
CityOsaka
Period13/7/313/7/7

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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