title: Automatic segmentation of cell nuclei in bladder and skin tissue for karyometric analysis.
abstract: OBJECTIVE
  To automatically segment cell nuclei in histology images of bladder and skin tissue for karyometric analysis.
  
  
  METHODS
  The 4 main steps in the program were as follows: median filtering and thresholding, segmentation, categorizing and cusp correction. This robust segmentation technique used properties of the image histogram to optimally select a threshold and create closed 4-way chain code nuclear segmentations. Each cell nucleus segmentation was treated as an individual object of which the properties of segmentation quality were used for criteria to classify each nucleus as: throw away, salvageable or good. An erosion/dilation procedure and rethresholding were performed on salvageable nuclei to correct cusps.
  
  
  RESULTS
  Ten bladder histology images were segmented both by hand and using this automatic segmentation algorithm. The automatic segmentation resulted in a sensitivity of 76.4%, defined as the percentage of hand-segmented nuclei that were automatically segmented with good quality. The median proportional difference between hand and automatic segmentations over 42 nuclei each with 95 features used in karyometric analysis was 1.6%. The same procedure was performed on 10 skin histology images with a sensitivity of 83.0% and median proportional difference of 2.6%.
  
  
  CONCLUSIONS
  The close agreement in karyometric features with hand segmentation shows that automated segmentation can be used for analysis of bladder and skin histology images.
authors.affiliation: College of Optical Sciences, University of Arizona, Tucson, Arizona, USA.
authors.forename: Vrushali R
authors.initials: VR
authors.lastname: Korde
authors.forename: Hubert
authors.initials: H
authors.lastname: Bartels
authors.forename: Jennifer
authors.initials: J
authors.lastname: Barton
authors.forename: James
authors.initials: J
authors.lastname: Ranger-Moore
mesh_terms: D000465:Algorithms; D001331:Automation / Q000379:methods; D002467:Cell Nucleus / Q000648:ultrastructure*; D018450:Disease Progression; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007620:Karyometry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012680:Sensitivity and Specificity; D012867:Skin / Q000648:ultrastructure*; D014463:Ultrasonography; D001743:Urinary Bladder / Q000000981:diagnostic imaging*; D001749:Urinary Bladder Neoplasms / Q000473:pathology / Q000648:ultrastructure