Fast and accurate identification of fat droplets in histological images


Background and objective: The accurate identification of fat droplets is a prerequisite for the automatic quantification of steatosis in histological images. A major challenge in this regard is the distinction between clustered fat droplets and vessels or tissue cracks.

Methods: We present a new method for the identification of fat droplets that utilizes adjacency statistics as shape features. Adjacency statistics are simple statistics on neighbor pixels.

Results: The method accurately identified fat droplets with sensitivity and specificity values above 90%. Compared with commonly-used shape features, adjacency statistics greatly improved the sensitivity toward clustered fat droplets by 29% and the specificity by 17%. On a standard personal computer, megapixel images were processed in less than 0.05 s.

Conclusions: The presented method is simple to implement and can provide the basis for the fast and accurate quantification of steatosis.


Projects: D4: Regeneration and Liver Size, Showcase Steatosis

Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
1st Jun 2015

André Homeyer, Andrea Schenk, Janine Arlt, Uta Dahmen, Olaf Dirsch, Horst K. Hahn

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[André Homeyer] [Andrea Schenk] [Janine Arlt] [Uta Dahmen] [Olaf Dirsch]

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Cell, Liver lobule and Liver
Views: 1804
  • Created: 25th Jun 2015 at 09:02
  • Last updated: 29th Jun 2015 at 09:38

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