Automated mitosis detection in histopathology using morphological and multi-channel statistics features.
Automated mitosis detection in histopathology using morphological and multi-channel statistics features.
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DOI：
10.4103/2153-3539.112695
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发表时间：
2013
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通讯作者：
Irshad H
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文献类型：
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作者：
Irshad H
根据诺丁汉分级系统，有丝分裂计数在癌症诊断和分级中起着关键作用。有丝分裂的人工计数是繁琐的，并且受到相当大的读者间和读者内差异的影响。目的是通过选择能够更好地捕捉统计和形态特征的颜色通道来提高有丝分裂检测的准确性，从而将有丝分裂与其他对象区分开来。我们提出了一个框架，它包括对不同颜色空间中选定通道的统计和形态特征的全面分析，以帮助病理学家进行有丝分裂检测。在候选检测阶段，对蓝比图像进行高斯拉普拉斯、阈值、形态和活动轮廓模型等方法进行候选检测和分割。在候选分类阶段，我们对每个候选通道提取了143个特征，包括形态特征、一阶统计特征和二阶(纹理)统计特征，最后使用决策树分类器进行分类。在为2012年国际模式识别大会提供的数据集上对该方法进行了评估，在Aperio和Hamamatsu图像上分别获得了74%和71%的检测率、70%和56%的准确率和72%和63%的F-MEASURE。所提出的多通道特征计算方法使用固定的图像尺度，在不同颜色空间的选定通道中提取核特征。在Mitos国际基准测试期间，这个简单但健壮的模型在捕捉用于有丝分裂检测的多通道统计特征方面已被证明是高效的。事实上，在癌症诊断中至关重要的有丝分裂检测是一项非常具有挑战性的视觉任务。在未来的工作中，我们计划使用颜色去卷积作为预处理和Hough变换或基于局部极值的候选检测，以减少有丝分裂和非有丝分裂类的候选数量。
According to Nottingham grading system, mitosis count plays a critical role in cancer diagnosis and grading. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. The aim is to improve the accuracy of mitosis detection by selecting the color channels that better capture the statistical and morphological features, which classify mitosis from other objects. We propose a framework that includes comprehensive analysis of statistics and morphological features in selected channels of various color spaces that assist pathologists in mitosis detection. In candidate detection phase, we perform Laplacian of Gaussian, thresholding, morphology and active contour model on blue-ratio image to detect and segment candidates. In candidate classification phase, we extract a total of 143 features including morphological, first order and second order (texture) statistics features for each candidate in selected channels and finally classify using decision tree classifier. The proposed method has been evaluated on Mitosis Detection in Breast Cancer Histological Images (MITOS) dataset provided for an International Conference on Pattern Recognition 2012 contest and achieved 74% and 71% detection rate, 70% and 56% precision and 72% and 63% F-Measure on Aperio and Hamamatsu images, respectively. The proposed multi-channel features computation scheme uses fixed image scale and extracts nuclei features in selected channels of various color spaces. This simple but robust model has proven to be highly efficient in capturing multi-channels statistical features for mitosis detection, during the MITOS international benchmark. Indeed, the mitosis detection of critical importance in cancer diagnosis is a very challenging visual task. In future work, we plan to use color deconvolution as preprocessing and Hough transform or local extrema based candidate detection in order to reduce the number of candidates in mitosis and non-mitosis classes.