Automatic counting cancer cell colonies using fuzzy inference system

Sheng-Fuu Lin*, Hsien Tse Chen, Yi Hsien Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper examines the efficacy of liver cancer treatment using HA22T cancer cells specific to the Taiwanese population. The Clonogenic Assay is the current standard method for detecting liver cancer. This paper uses image processing technology and a fuzzy inference system to identify in-vitro colonies of cancer cells. A scanner was first used to capture an image of the culture dish. This image was then analyzed using image processing techniques and the Hough transform to establish the relative position of the dish. Image segmentation was accomplished by image differencing, while feature extraction was based on the features specific to the image. Decision-making was then carried out using a fuzzy inference system to calculate the number of colonies within the image. In summary, this paper proposes a fuzzy cancer cell colony identification system based on a fuzzy inference system (FIS) that successfully identifies cancer cell colonies that are indiscernible to the naked eye.

Original languageEnglish
Pages (from-to)749-760
Number of pages12
JournalJournal of Information Science and Engineering
Volume27
Issue number2
DOIs
StatePublished - 1 Mar 2011

Keywords

  • Cancer
  • Clonogenic assays
  • Feature extraction
  • Fuzzy inference system (FIS)
  • Image processing

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