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一种用于图像滤波与分割的启发式框架:应用于血管免疫组织化学

A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry.

作者信息

Tsou Chi-Hsuan, Lu Yi-Chien, Yuan Ang, Chang Yeun-Chung, Chen Chung-Ming

机构信息

Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Section 1, Jen-Ai Road, Taipei 100, Taiwan.

Department of Radiology, National Taiwan University College of Medicine and Department of Medical Imaging, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei 100, Taiwan.

出版信息

Anal Cell Pathol (Amst). 2015;2015:589158. doi: 10.1155/2015/589158. Epub 2015 Dec 27.

Abstract

The blood vessel density in a cancerous tissue sample may represent increased levels of tumor growth. However, identifying blood vessels in the histological (tissue) image is difficult and time-consuming and depends heavily on the observer's experience. To overcome this drawback, computer-aided image analysis frameworks have been investigated in order to boost object identification in histological images. We present a novel algorithm to automatically abstract the salient regions in blood vessel images. Experimental results show that the proposed framework is capable of deriving vessel boundaries that are comparable to those demarcated manually, even for vessel regions with weak contrast between the object boundaries and background clutter.

摘要

癌组织样本中的血管密度可能代表肿瘤生长水平的提高。然而,在组织学(组织)图像中识别血管既困难又耗时,并且严重依赖于观察者的经验。为了克服这一缺点,人们研究了计算机辅助图像分析框架,以提高组织学图像中的目标识别能力。我们提出了一种新颖的算法,用于自动提取血管图像中的显著区域。实验结果表明,即使对于目标边界与背景杂波之间对比度较弱的血管区域,所提出的框架也能够得出与手动划定的边界相当的血管边界。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21af/4707018/b7627800e4a0/ACP2015-589158.001.jpg

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