Label-Free Normal and Cancer Cells Classification Combining Prony's Method and Optical Techniques

Aysha F. Abdulgani, Mahmoud Al Ahmad

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Label-free methods neither cause cell damage nor contribute to any change in cell composition and intrinsic characteristics. Indeed, there is much interest in the scientific community to learn more from existing methods and to develop new label-free based methods for detection and classification of cells. Cell classification using optical measurements has been frequently utilized. When cells interact with light, due to differences in the composition of different types of cells, changes in the optical absorption and transmission response result. This work combined the advancement in optical measurements and Prony techniques to enhance the classification of cells based on their measured optical profiles. In this work, six types of cells, HeLa, 293T, lung- cancer and normal, and liver- cancer and normal, were suspended in their corresponding medium and their transmission characteristics were assessed. After media de-embedding, the transmission profiles were fitted with a sum of exponentially decaying signals using the Prony algorithm. After that, the optical response of each cell was modeled with a set of extracted parameters: amplitude, frequency, phase, and damping factor. The four parameters extracted via the Prony method are related to the coefficients and locations of the poles for each fitted model. A figure of merit (FOM) has been introduced, whose distribution in the complex z-plane plays a major role in the classification of cell type. The changes in the values of FOM are due to the changes in cell composition and intrinsic characteristics of different cells.

Original languageEnglish
Article number8995512
Pages (from-to)32882-32890
Number of pages9
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Cancer
  • Prony estimation
  • cells
  • classification
  • detection
  • figure of merit
  • label-free
  • optical
  • sensors

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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