HotSwap Engineering Consultants, S-17263 Stockholm, Sweden.
IEEE Trans Med Imaging. 2013 Jun;32(6):983-94. doi: 10.1109/TMI.2013.2239655. Epub 2013 Jan 11.
Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy. We extend current linear decomposition methods to include stained tissues where one spectral signature cannot be separated from all combinations of the other tissues' spectral signatures. We demonstrate both qualitatively and quantitatively that our method results in more accurate decompositions than methods based on non-negative matrix factorization and independent component analysis. The result is one density map for each stained tissue type that classifies portions of pixels into the correct stained tissue allowing accurate identification of morphological features that may be linked to cancer.
癌症诊断基于对活检组织切片进行显微镜下的目视检查。然而，虽然病理学家依赖于组织染色来识别形态特征，但使用颜色进行自动组织识别存在许多问题，这些问题源于由于组织准备的变化、染色组织的光谱特征的变化、采集过程中的光谱重叠和空间混叠以及图像采集时的噪声而导致的图像强度变化。我们提出了一种用于组织学图像颜色分解的盲法。该方法将强度与颜色信息解耦，并仅基于每种染色剂的组织吸收特性进行分解。通过对电荷耦合器件传感器噪声进行建模，我们提高了方法的准确性。我们将当前的线性分解方法扩展到包括一种光谱特征无法与其他组织的光谱特征的所有组合分开的染色组织。我们定性和定量地证明，我们的方法比基于非负矩阵分解和独立分量分析的方法产生更准确的分解。结果是每个染色组织类型的一个密度图，将像素的部分分类为正确的染色组织，从而可以准确识别可能与癌症相关的形态特征。
