Orlov Nikita V, Chen Wayne W, Eckley David Mark, Macura Tomasz J, Shamir Lior, Jaffe Elaine S, Goldberg Ilya G
National Institute on Aging, NIH, Baltimore, MD 21224, USA.
IEEE Trans Inf Technol Biomed. 2010 Jul;14(4):1003-13. doi: 10.1109/TITB.2010.2050695.
We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content. A unique two-stage approach was employed in this study. At the outer level, raw pixels were transformed with a set of transforms into spectral planes. Simple (Fourier, Chebyshev, and wavelets) and compound transforms (Chebyshev of Fourier and wavelets of Fourier) were computed. Raw pixels and spectral planes were then routed to the second stage (the inner level). At the inner level, the set of multipurpose global features was computed on each spectral plane by the same feature bank. All computed features were fused into a single feature vector. The specimens were stained with hematoxylin (H) and eosin (E) stains. Several color spaces were used: RGB, gray, CIE-Lab*, and also the specific stain-attributed H&E space, and experiments on image classification were carried out for these sets. The best signal (98%-99% on earlier unseen images) was found for the HE, H, and E channels of the H&E data set.
慢性淋巴细胞白血病、滤泡性淋巴瘤和套细胞淋巴瘤。目标是找到指示淋巴瘤恶性肿瘤的模式，并能够按类型对这些恶性肿瘤进行分类。我们使用计算机视觉方法对图像内容进行定量表征。本研究采用了一种独特的两阶段方法。在外部层面，原始像素通过一组变换转换到光谱平面。计算了简单变换（傅里叶变换、切比雪夫变换和小波变换）和复合变换（傅里叶变换的切比雪夫变换和傅里叶变换的小波变换）。然后将原始像素和光谱平面路由到第二阶段（内部层面）。在内部层面，通过同一个特征库在每个光谱平面上计算一组多用途全局特征。所有计算出的特征被融合成一个单一特征向量。标本用苏木精（H）和伊红（E）染色。使用了几种颜色空间：RGB、灰度、CIE-Lab*，以及特定的染色归因H&E空间，并针对这些集合进行了图像分类实验。在H&E数据集的HE、H和E通道上发现了最佳信号（在早期未见过的图像上为98%-99%）。
