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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Bibliographic Details
Published in:Analytical cellular pathology (Amsterdam) 2015-01, Vol.2015 (2015), p.1-10
Main Authors: Chang, Yeun-Chung, Yuan, Ang, Lu, Yi-Chien, Tsou, Chi-Hsuan, Chen, Chung-Ming
Format: Article
Language:English
Subjects:
Algorithms
Automation
Blood Vessels - pathology
Cell Line, Tumor
Cluster Analysis
Fuzzy Logic
Humans
Image Processing, Computer-Assisted
Immunohistochemistry
Staining and Labeling
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Summary: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.
ISSN:2210-7177
2210-7185
DOI:10.1155/2015/589158