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基于显微图像分析的慢性髓性白血病细胞亚型分类

Classification of chronic myeloid leukemia cell subtypes based on microscopic image analysis.

作者信息

Ghane Narjes, Vard Alireza, Talebi Ardeshir, Nematollahy Pardis

机构信息

Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

出版信息

EXCLI J. 2019 Jun 14;18:382-404. doi: 10.17179/excli2019-1292. eCollection 2019.

DOI:10.17179/excli2019-1292
PMID:31338009
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6635720/
Abstract

This paper presents a simple and efficient computer-aided diagnosis method to classify Chronic Myeloid Leukemia (CML) cells based on microscopic image processing. In the proposed method, a novel combination of both typical and new features is introduced for classification of CML cells. Next, an effective decision tree classifier is proposed to classify CML cells into eight groups. The proposed method was evaluated on 1730 CML cell images containing 714 cells of non-cancerous bone marrow aspiration and 1016 cells of cancerous peripheral blood smears. The performance of the proposed classification method was compared to manual labels made by two experts. The average values of accuracy, specificity and sensitivity were 99.0 %, 99.4 % and 98.3 %, respectively for all groups of CML. In addition, Cohen's kappa coefficient demonstrated high conformity, 0.99, between joint diagnostic results of two experts and the obtained results of the proposed approach. According to the obtained results, the suggested method has a high capability to classify effective cells of CML and can be applied as a simple, affordable and reliable computer-aided diagnosis tool to help pathologists to diagnose CML.

摘要

本文提出了一种基于显微图像处理的简单高效的慢性粒细胞白血病（CML）细胞分类计算机辅助诊断方法。在所提出的方法中，引入了典型特征和新特征的新颖组合用于CML细胞分类。接下来，提出了一种有效的决策树分类器将CML细胞分为八组。该方法在1730张CML细胞图像上进行了评估，这些图像包含714个非癌性骨髓穿刺细胞和1016个癌性外周血涂片细胞。将所提出的分类方法的性能与两位专家制作的手动标签进行了比较。对于所有CML组，准确率、特异性和灵敏度的平均值分别为99.０%、99.4%和98.3%。此外，科恩kappa系数表明两位专家的联合诊断结果与所提出方法的所得结果之间具有高度一致性，为0.99。根据所得结果，所建议的方法具有对CML有效细胞进行分类的高能力，并且可以作为一种简单、经济且可靠的计算机辅助诊断工具应用，以帮助病理学家诊断CML。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/e0b62359e048/EXCLI-18-382-g-012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/707c10397e04/EXCLI-18-382-t-001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/5812c1eefca6/EXCLI-18-382-t-002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/81c72a0746f2/EXCLI-18-382-g-001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/758336c9c637/EXCLI-18-382-g-006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/fcb6155c88b1/EXCLI-18-382-g-007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/9402f53aec06/EXCLI-18-382-g-008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/acc1317c05f5/EXCLI-18-382-g-009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/7f124dbe9b17/EXCLI-18-382-g-010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/e0b62359e048/EXCLI-18-382-g-012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/707c10397e04/EXCLI-18-382-t-001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/5812c1eefca6/EXCLI-18-382-t-002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/81c72a0746f2/EXCLI-18-382-g-001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/42e5ddf66c3d/EXCLI-18-382-g-002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/4eecb3a3e373/EXCLI-18-382-g-003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/90b8d6855e3b/EXCLI-18-382-g-004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/0d783b7b7c8e/EXCLI-18-382-g-005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/acc1317c05f5/EXCLI-18-382-g-009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e42/6635720/e0b62359e048/EXCLI-18-382-g-012.jpg

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3
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4
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