Angular Photodiode Array for the Identification of Colorectal Carcinoma by Mie Scatter

Matthew Bills, Jeong Yeol Yoon

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


Cancer tissues differ from normal tissues in a myriad of ways including cellular changes in size, morphology, and type/density of membrane and cytosolic proteins, which will accordingly change the refractive index of cells. There are also important differences in tissue organization, perhaps most notably the increase and morphologic changes in capillary vessels and extracellular matrix organization. Our laboratory has recently developed a novel technology and early prototype that can be used to detect some of these physiologic changes by collecting the light scattering intensities over a range of scattering angles from animal and human colonic tissues. To classify tumors, we employed multivariate analysis tools including Principal Component Analysis and Support Vector Machines to help discriminate between healthy and cancerous tissue samples. The preliminary results revealed a consistent and relatively accurate classification of tissues. These findings show promise for the noninvasive, label free, low cost, and rapid (under a minute) detection of surface tumors.

Original languageEnglish (US)
Title of host publication2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116341
StatePublished - Oct 2019
Event18th IEEE Sensors, SENSORS 2019 - Montreal, Canada
Duration: Oct 27 2019Oct 30 2019

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference18th IEEE Sensors, SENSORS 2019


  • Cancer detection
  • Label-free
  • Mie scatter
  • Principal Component Analysis
  • Support Vector Machines

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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