基于多曲率轮廓信息的病理图像细胞核自动检测

细胞核自动检测既是病理图像分析技术的重要步骤,也是提高病理图像自动化分析准确性的主要瓶颈之一,原因在于病理切片制作存在染色分层不均、细胞核粘连或重叠等问题。为了提高细胞核检测的准确度,定义了一种基于多曲率轮廓的细胞核自动检测模型,通过多曲率方向能量滤波器提取细胞核轮廓信息。特征检测器基于boosting算法,利用不同曲率和方向轮廓特征的完备集合产生像素软分类器,获得像素的前景背景置信度和概率。最后利用均值漂移算法得到细胞核中心位置及其置信度。实验结果表明,该算法与其他细胞核检测算法相比,在生物组织结构变异、不均匀光照或染色条件下,以及细胞核粘连或部分重叠等情况下,有着较强的鲁棒性。

Automatic detection of cell nucleus in pathological images based on multi-curvature contour features

Nucleus automatic detection is not only an important part of pathological image analysis, but also one of the primary bottlenecks to improve the analysis accuracy of the technology. The reason lies in that the pathological slice exhibits uneven layering and staining, nucleus crowding or overlapping. In order to improve the accuracy of the nucleus detection, we design a nucleus model of multi-curvature contour. The nucleus contour features are extracted by multi curvature orientation energy filter. Then the contour features, along with the cell nucleus ground truth, are put into a boosting algorithm, which creates a pixel classifier to classify pixels into foreground and background. In the end, the mean-shift algorithm produces the confidence co efficiency of the nucleus for each location. Experimental results show that in comparison with other state-of-art cell nuclei detection methods, our method is more robust to conditions such as biological variations, different staining and illumination conditions, and touching or partial occlusions.