Department of Biomedical Informatics, Ohio State University, Columbus, OH 43201, USA.
IEEE Trans Med Imaging. 2011 Sep;30(9):1661-77. doi: 10.1109/TMI.2011.2141674. Epub 2011 Apr 11.
For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method. For the segmentation part, we segment the cell regions from the other areas by classifying the image pixels into either cell or extra-cellular category. Instead of using pixel color intensities, the color-texture extracted at the local neighborhood of each pixel is utilized as the input to our classification algorithm. The color-texture at each pixel is extracted by local Fourier transform (LFT) from a new color space, the most discriminant color space (MDC). The MDC color space is optimized to be a linear combination of the original RGB color space so that the extracted LFT texture features in the MDC color space can achieve most discrimination in terms of classification (segmentation) performance. To speed up the texture feature extraction process, we develop an efficient LFT extraction algorithm based on image shifting and image integral. For the splitting part, given a connected component of the segmentation map, we initially differentiate whether it is a touching-cell clump or a single nontouching cell. The differentiation is mainly based on the distance between the most likely radial-symmetry center and the geometrical center of the connected component. The boundaries of touching-cell clumps are smoothed out by Fourier shape descriptor before carrying out an iterative, concave-point and radial-symmetry based splitting algorithm. To test the validity, effectiveness and efficiency of the framework, it is applied to follicular lymphoma pathological images, which exhibit complex background and extracellular texture with nonuniform illumination condition. For comparison purposes, the results of the proposed segmentation algorithm are evaluated against the outputs of superpixel, graph-cut, mean-shift, and two state-of-the-art pathological image segmentation methods using ground-truth that was established by manual segmentation of cells in the original images. Our segmentation algorithm achieves better results than the other compared methods. The results of splitting are evaluated in terms of under-splitting, over-splitting, and encroachment errors. By summing up the three types of errors, we achieve a total error rate of 5.25% per image.
对于组织病理学图像的定量分析，例如淋巴瘤分级系统，通常在通过分类算法对其进行分类之前，对单个细胞的特征进行量化。为此，我们提出了一个由新的有监督细胞图像分割算法和新的粘连细胞分割方法组成的集成框架。对于分割部分，我们通过将图像像素分类为细胞或细胞外类别，将细胞区域与其他区域分开。我们的分类算法不使用像素颜色强度，而是利用每个像素的局部邻域提取的颜色纹理作为输入。颜色纹理是通过局部傅里叶变换（LFT）从新的颜色空间（最具判别力的颜色空间，MDC）中提取的。MDC 颜色空间被优化为原始 RGB 颜色空间的线性组合，以使 MDC 颜色空间中提取的 LFT 纹理特征在分类（分割）性能方面具有最大的可区分性。为了加速纹理特征提取过程，我们开发了一种基于图像移位和图像积分的高效 LFT 提取算法。对于分割部分，给定分割图的一个连通分量，我们最初区分它是粘连细胞团还是单个非粘连细胞。这种区分主要基于最可能的径向对称中心与连通分量的几何中心之间的距离。在进行迭代、凹点和基于径向对称的分割算法之前，先通过傅里叶形状描述符对粘连细胞团的边界进行平滑处理。为了测试框架的有效性、有效性和效率，将其应用于滤泡性淋巴瘤病理图像，这些图像表现出复杂的背景和具有不均匀照明条件的细胞外纹理。为了进行比较，使用原始图像中手动分割细胞建立的真实分割，评估了所提出的分割算法与超像素、图割、均值漂移和两种最先进的病理图像分割方法的输出结果。我们的分割算法比其他比较方法取得了更好的结果。分割结果根据欠分割、过分割和入侵误差进行评估。通过总结这三种类型的误差，我们获得了每张图像 5.25%的总误差率。
