A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images.
A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images.
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DOI：
10.1117/12.893641
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发表时间：
2011-10-19
期刊：
影响因子：
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通讯作者：
Kovačević J
中科院分区：
文献类型：
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作者：
Massar ML;Bhagavatula R;Ozolek JA;Castro CA;Fickus M;Kovačević J
我们目前的状态，我们的工作在一个数学框架的识别和描绘的组织病理学图像的局部直方图和遮挡模型。局部直方图是在定义的空间邻域上计算的直方图，其目的是局部地表征图像。这个描述单元通过我们的描述图像形成方法的遮挡模型来增强。在这个图像形成模型的上下文中，局部直方图相对于适当的图像族的能力将通过关于预期性能的各种经证明的陈述来显示。最后，我们提出了一个初步的研究，以证明在组织病理学图像分类任务的背景下，权力的框架，而在应用程序中有很大的不同，都起源于什么被认为是一个适当的图像类这个框架。
We present the current state of our work on a mathematical framework for identification and delineation of histopathology images—local histograms and occlusion models. Local histograms are histograms computed over defined spatial neighborhoods whose purpose is to characterize an image locally. This unit of description is augmented by our occlusion models that describe a methodology for image formation. In the context of this image formation model, the power of local histograms with respect to appropriate families of images will be shown through various proved statements about expected performance. We conclude by presenting a preliminary study to demonstrate the power of the framework in the context of histopathology image classification tasks that, while differing greatly in application, both originate from what is considered an appropriate class of images for this framework.
DOI：
10.1109/isbi.2010.5490168
发表时间：
2010-04-14
期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
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作者：
Bhagavatula R;Fickus M;Kelly W;Guo C;Ozolek JA;Castro CA;Kovačević J
通讯作者：
Kovačević J