Image-analysis based readout method for biochip: Automated quantification of immunomagnetic beads, micropads and patient leukemia cell


MICRON, vol.133, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 133
  • Publication Date: 2020
  • Doi Number: 10.1016/j.micron.2020.102863
  • Journal Name: MICRON
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aqualine, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Compendex, EMBASE, INSPEC, MEDLINE, Veterinary Science Database
  • Keywords: Biochip, Image processing, Leukemia cells, Micropads, Immunomagnetic beads, Bright-field optical microscope, BRIGHT, PLATFORM
  • Abdullah Gül University Affiliated: Yes


For diagnosing and monitoring the progress of cancer, detection and quantification of tumor cells is utmost important. Beside standard bench top instruments, several biochip-based methods have been developed for this purpose. Our biochip design incorporates micron size immunomagnetic beads together with micropad arrays, thus requires automated detection and quantification of not only cells but also the micropads and the immunomagnetic beads. The main purpose of the biochip is to capture target cells having different antigens simultaneously. In this proposed study, a digital image processing-based method to quantify the leukemia cells, immunomagnetic beads and micropads was developed as a readout method for the biochip. Color, size-based object detection and object segmentation methods were implemented to detect structures in the images acquired from the biochip by a bright field optical microscope. It has been shown that manual counting and flow cytometry results are in good agreement with the developed automated counting. Average precision is 85 % and average error rate is 13 % for all images of patient samples, average precision is 99 % and average error rate is 1% for cell culture images. With the optimized micropad size, proposed method can reach up to 95 % precision rate for patient samples with an execution time of 90 s per image.