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The Use of Image Processing and Analysis in Automated Biological Dosimetry
Author(s) -
Dwi Ramadhani,
Mukh Syaifudin,
Sofiati Purnami,
Aroem Naroeni
Publication year - 2020
Publication title -
atom indonesia
Language(s) - English
Resource type - Journals
eISSN - 2356-5322
pISSN - 0126-1568
DOI - 10.17146/aij.2020.960
Subject(s) - biodosimetry , dicentric chromosome , image processing , dosimetry , radiological weapon , computer science , medical physics , computer vision , artificial intelligence , nuclear medicine , medicine , ionizing radiation , biology , radiology , image (mathematics) , chromosome , physics , irradiation , biochemistry , nuclear physics , karyotype , gene
Biological dosimetry based on cytogenetic markers such as dicentric chromosome (DC) and micronuclei (MN) is, until now, the most frequently used method to estimate the radiation dose in the radiological accident event. Another biomarker that recently gains popularity in biodosimetry is γH2AX. All these three assays are microscope-based biodosimetry techniques, and therefore need manual scoring to estimate the radiation dose. Unfortunately, the manual scoring of these assays is time-consuming and labor-intensive. In the case of a large-scale radiological accident when many people are exposed to radiation, it is very useful to use image processing and analysis in the scoring process to obtain a faster result. Several commercial systems or open-source image processing software packages already developed automated scoring of DC, MN, and γH2AX assays. This article describes how image processing and analysis were applied in automated biodosimetry based on the DC, MN, and γH2AX assays.

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