Detection of blur artifacts in histopathological whole-slide images of endomyocardial biopsies - PubMed (original) (raw)
Detection of blur artifacts in histopathological whole-slide images of endomyocardial biopsies
Hang Wu et al. Annu Int Conf IEEE Eng Med Biol Soc. 2015.
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.
Figures
Fig. 1
Blur artifacts in an endomyocardial biospy (EMB) whole-slide images (WSI)
Fig. 2
Classification of blurry and sharp images using histogram features of local blur metrics.
Fig. 3
Histograms of local kurtosis in a blurry (a) and sharp (b) image.
Fig. 4
Histograms of local average power spectrum in a blurry (a) and sharp image (b).
Fig. 5
Histograms of local probablity metric in a blurry (a) and sharp image (b).
Fig. 6
Local Pixel-Level metrics (red and orange) for detection of blurry images are more accurate compared to global metrics (blue). Results are consistent for a) KNN, b) NB, c) SVM, and d) RF; and for metrics F1-Score, Accuracy, and AUC.
Fig. 7
Detection of image blur using local metric is sensitive to the size of neighbors used to calculate local metrics, as well as the number of histogram bins.
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