一种基于前景标记的质心初始化组织病理图像的测地线活动轮廓

P. Shivamurthy, T. N. Nagabhushan, V. Basavaraj
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引用次数: 3

摘要

核分割被认为是组织病理成像领域的主要挑战之一。文献中提出了各种分割方法。组织病理学图像的质量对这些提出的技术提出了各种挑战,并且由于边缘信息差和边界的不规则性,它们都存在缺陷。活动轮廓被认为是解决这一具有挑战性任务的有希望的解决方案。活动轮廓的主要问题是梯度信息的计算、初始化和遮挡检测。为了有效地解决这些问题,本文提出了一种基于前景标记的边缘梯度驱动的测地线活动轮廓(GAC)检测种子点的新方法。实验在乳腺癌组织图像上进行,并参照JSS医院病理科病理学家提出的ground truth,计算了目标检测精度、重叠分辨率等效率指标,并与无前景标记的GAC进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A foreground marker based centroid initialized Geodesic active contours for histopathological image segmentation
Nuclear segmentation is considered to be one of the major challenge in the field of Histopathological Imaging. Various segmentation approaches have been proposed in the literature. The quality of the histopathological images have posed various challenges to those proposed techniques and they all suffer with deficiencies due to poor edge information and irregularities of the boundary. Active contours are considered to be the promising solutions to such a challenging task. The major issues with Active contours are computation of gradient information, initialization and occlusion detection. To address these issues effectively, an edge gradient driven Geodesic active contour(GAC) with a novel approach of detecting seed points based on foreground markers is proposed in this paper. The experimentation is performed on breast cancer tissue images and the efficiency measures such object detection accuracy and overlap resolution have been computed and compared with that of GAC without foreground markers as referred to the ground truth opined by the pathologists from Department of Pathology, JSS Hospital.
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