title: Fast and accurate identification of fat droplets in histological images.
abstract: 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 0.05s.
  
  
  CONCLUSIONS
  The presented method is simple to implement and can provide the basis for the fast and accurate quantification of steatosis.
authors.affiliation: Fraunhofer MEVIS, Universitätsallee 29, 28359 Bremen, Germany. Electronic address: andre.homeyer@mevis.fraunhofer.de.
authors.forename: André
authors.initials: A
authors.lastname: Homeyer
authors.affiliation: Fraunhofer MEVIS, Universitätsallee 29, 28359 Bremen, Germany.
authors.forename: Andrea
authors.initials: A
authors.lastname: Schenk
authors.affiliation: Department of General, Visceral and Vascular Surgery, Friedrich-Schiller-University Jena, Drackendorfer Str. 1, 07747 Jena, Germany.
authors.forename: Janine
authors.initials: J
authors.lastname: Arlt
authors.affiliation: Department of General, Visceral and Vascular Surgery, Friedrich-Schiller-University Jena, Drackendorfer Str. 1, 07747 Jena, Germany.
authors.forename: Uta
authors.initials: U
authors.lastname: Dahmen
authors.affiliation: Institute of Pathology, Jena University Hospital, Ziegelmühlenweg 1, 07747 Jena, Germany; Institute of Pathology, Chemnitz Central Hospital, Flemmingstr. 1, 09116 Chemnitz, Germany.
authors.forename: Olaf
authors.initials: O
authors.lastname: Dirsch
authors.affiliation: Fraunhofer MEVIS, Universitätsallee 29, 28359 Bremen, Germany.
authors.forename: Horst K
authors.initials: HK
authors.lastname: Hahn
mesh_terms: D000465:Algorithms; D005234:Fatty Liver / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D066292:Lipid Droplets / Q000473:pathology*; D000069550:Machine Learning; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Histological image analysis; Shape features; Steatosis quantification; Texture features