A Computer Vision Algorithm for the Digitalization of Colorimetric Lateral Flow Assay Readouts

Luca Pezzarossa*, Susan Ibi Preus, Winnie Edith Svendsen, Jan Madsen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Lateral flow assays (LFAs) are low-cost testing tools widely used for home, point-of-care, or laboratory medical diagnostics. These tests typically use colorimetry to report the presence and the concentration of a certain physical/ biological quantity, showing the result as a color marker. This work presents a computer vision algorithm for the digitalization of LFA readouts, enabling precise and reliable results at low-cost. The algorithm receives as input an image of a sample, identifies the color marker, and computes its average color intensity. In contrast to existing algorithms, the proposed one can detect color markers that are not characterized by a predetermined precise shape, size, and position, since the topology is identified and analyzed by the algorithm itself. The evaluation of the proposed algorithm on a set of LFA strips shows correct functionality and execution time of less than a second.
Original languageEnglish
Title of host publication2020 Symposium on Design, Test, Integration & Packaging of MEMS and MOEMS (DTIP)
Number of pages6
PublisherIEEE
Publication date2020
ISBN (Electronic)978-1-7281-8901-7
DOIs
Publication statusPublished - 2020
Event2020 Symposium on Design, Test, Integration & Packaging of MEMS and MOEMS - Lyon, France
Duration: 15 Jun 202026 Jun 2020

Conference

Conference2020 Symposium on Design, Test, Integration & Packaging of MEMS and MOEMS
CountryFrance
CityLyon
Period15/06/202026/06/2020

Keywords

  • Lateral flow assay
  • Sample digitalization
  • Computer-vision algorithm
  • Arbitrary marker topology
  • OpenCV

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