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

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

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 imag...

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Main Authors: Chi-Hsuan Tsou, Yi-Chien Lu, Ang Yuan, Yeun-Chung Chang, Chung-Ming Chen
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Analytical Cellular Pathology
Online Access:http://dx.doi.org/10.1155/2015/589158
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spelling doaj-5b0cc37d8103462086b7f0cccfd6103a2021-07-02T06:34:05ZengHindawi LimitedAnalytical Cellular Pathology2210-71772210-71852015-01-01201510.1155/2015/589158589158A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel ImmunohistochemistryChi-Hsuan Tsou0Yi-Chien Lu1Ang Yuan2Yeun-Chung Chang3Chung-Ming Chen4Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Section 1, Jen-Ai Road, Taipei 100, TaiwanDepartment 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, TaiwanDepartment of Internal Medicine, National Taiwan University College of Medicine, No. 7, Chung-Shan South Road, Taipei 100, TaiwanDepartment 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, TaiwanInstitute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Section 1, Jen-Ai Road, Taipei 100, TaiwanThe 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.http://dx.doi.org/10.1155/2015/589158
collection DOAJ
language English
format Article
sources DOAJ
author Chi-Hsuan Tsou
Yi-Chien Lu
Ang Yuan
Yeun-Chung Chang
Chung-Ming Chen
spellingShingle Chi-Hsuan Tsou
Yi-Chien Lu
Ang Yuan
Yeun-Chung Chang
Chung-Ming Chen
A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry
Analytical Cellular Pathology
author_facet Chi-Hsuan Tsou
Yi-Chien Lu
Ang Yuan
Yeun-Chung Chang
Chung-Ming Chen
author_sort Chi-Hsuan Tsou
title A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry
title_short A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry
title_full A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry
title_fullStr A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry
title_full_unstemmed A Heuristic Framework for Image Filtering and Segmentation: Application to Blood Vessel Immunohistochemistry
title_sort heuristic framework for image filtering and segmentation: application to blood vessel immunohistochemistry
publisher Hindawi Limited
series Analytical Cellular Pathology
issn 2210-7177
2210-7185
publishDate 2015-01-01
description 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.
url http://dx.doi.org/10.1155/2015/589158
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