دانلود مقاله ISI انگلیسی شماره 152305
ترجمه فارسی عنوان مقاله

تشخیص خودکار میکروسکوپی توسط پردازش تصویر میکروسکوپی سلولی

عنوان انگلیسی
Automatic detection of micronuclei by cell microscopic image processing
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
152305 2017 28 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, Volume 806, December 2017, Pages 9-18

ترجمه کلمات کلیدی
آزمون میکروسکوپی، تجزیه و تحلیل سیتوژنتیک، تابش یونیزه، پردازش تصویر، تقسیم بندی تصویر، سیستم طراحی کامپیوتری،
کلمات کلیدی انگلیسی
Micronucleus test; Cytogenetic analysis; Ionizing radiation; Image processing; Image segmentation; Computer-aided-design system;
پیش نمایش مقاله
پیش نمایش مقاله  تشخیص خودکار میکروسکوپی توسط پردازش تصویر میکروسکوپی سلولی

چکیده انگلیسی

With the development and applications of ionizing radiation in medicine, the radiation effects on human health get more and more attention. Ionizing radiation can lead to various forms of cytogenetic damage, including increased frequencies of micronuclei (MNi) and chromosome abnormalities. The cytokinesis block micronucleus (CBMN) assay is widely used method for measuring MNi to determine chromosome mutations or genome instability in cultured human lymphocytes. The visual scoring of MNi is time-consuming and scorer fatigue can lead to inconsistency. In this work, we designed software for the scoring of in vitro CBMN assay for biomonitoring on Giemsa-stained slides that overcome many previous limitations. Automatic scoring proceeds in four stages as follows. First, overall segmentation of nuclei is done. Then, binucleated (BN) cells are detected. Next, the entire cell is estimated for each BN as it is assumed that there is no detectable cytoplasm. Finally, MNi are detected within each BN cell. The designed Software is even able to detect BN cells with vague cytoplasm and MNi in peripheral blood smear. Our system is tested on a self-provided dataset and is achieved high sensitivities of about 98% and 82% in recognizing BN cells and MNi, respectively. Moreover, in our study less than 1% false positives were observed that makes our system reliable for practical MNi scoring.