| Literature DB >> 26736365 |
John H Phan, Ajay K Bhatia, Caitlin A Cundiff, Bahig M Shehata, May D Wang.
Abstract
Histopathological whole-slide images (WSIs) have emerged as an objective and quantitative means for image-based disease diagnosis. However, WSIs may contain acquisition artifacts that affect downstream image feature extraction and quantitative disease diagnosis. We develop a method for detecting blur artifacts in WSIs using distributions of local blur metrics. As features, these distributions enable accurate classification of WSI regions as sharp or blurry. We evaluate our method using over 1000 portions of an endomyocardial biopsy (EMB) WSI. Results indicate that local blur metrics accurately detect blurry image regions.Entities:
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Year: 2015 PMID： 26736365 PMCID： PMC4983426 DOI： 10.1109/EMBC.2015.7318465
Source DB: PubMed Journal: Conf Proc IEEE Eng Med Biol Soc ISSN： 1557-170X