An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders
An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders
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DOI：
10.1016/j.media.2008.04.001
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发表时间：
2008-12-01
影响因子：
10.9
通讯作者：
Valenti, Cesare
中科院分区：
文献类型：
--
作者：
Ballaro, Benedetto;Florena, Ada Maria;Valenti, Cesare
本文介绍了一种在显微照片中区分正常和病理人类巨核细胞以及这些细胞的两种疾病的自动方法。一个分割程序已经开发，主要是基于数学形态学和小波变换，分离细胞。每个巨核细胞的特征（例如，细胞及其细胞核的面积、周长和曲折度，以及通过椭圆傅里叶变换的形状复杂性）被应用两次的回归树程序使用：第一次用于找到正常巨核细胞集，第二次用于区分病理。我们的分类器的输出已经与病理学家提供的解释进行了比较，结果表明，98.4%和97.1%的正常和病理细胞，分别证明了一个很好的分类。这项研究提出了一个有用的援助，支持专家在巨核细胞疾病的分类。(C)2008 Elsevier B.V.保留所有权利。
This work describes an automatic method for discrimination in microphotographs between normal and pathological human megakaryocytes and between two kinds of disorders of these cells. A segmentation procedure has been developed, mainly based on mathematical morphology and wavelet transform, to isolate the cells. The features of each megakaryocyte (e.g. area, perimeter and tortuosity of the cell and its nucleus, and shape complexity via elliptic Fourier transform) are used by a regression tree procedure applied twice: the first time to find the set of normal megakaryocytes and the second to distinguish between the pathologies. The output of our classifier has been compared to the interpretation provided by the pathologists and the results show that 98.4% and 97.1% of normal and pathological cells, respectively, have testified an excellent classification. This study proposes a useful aid in supporting the specialist in the classification of megakaryocyte disorders. (C) 2008 Elsevier B.V. All rights reserved.