Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images

Uslu F., İÇÖZ K., TAŞDEMİR K., YILMAZ B.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL, cilt.49, ss.473-482, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 49
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.bspc.2019.01.002
  • Dergi Adı: BIOMEDICAL SIGNAL PROCESSING AND CONTROL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.473-482
  • Anahtar Kelimeler: Leukemia cells, Image-processing, Bright-field optical microscopy, Machine learning, Immunomagnetic beads, Support vector machines, SUPPORT VECTOR MACHINE, FEATURE-SELECTION, CANCER-PATIENTS, CLASSIFICATION, BIOSENSOR, PARTICLE
  • Abdullah Gül Üniversitesi Adresli: Evet

Özet

Quantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40x or a 20x objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40x objective and 79.7% for the 20x objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection. (C) 2019 Elsevier Ltd. All rights reserved.