title: Fuzzy algorithms to extract vacuoles of steatosis on liver histological color images.
abstract: In this paper, we present an automatic, robust and reliable process to quantify liver steatosis. The degree of steatosis is a useful marker of steatohepatitis. This degree is routinely assessed visually by an expert and then lacks of accuracy and robustness. The process that we have developed is divided in two steps. A fuzzy classification first merges into classes pixels according to their intensity. We use a generalized objective function that allows to detect micro and blurredness vacuoles of steatosis. Then, regions with inhomogeneous texture and irregular shape were eliminated with compactness and standard deviation parameters. The obtained results are good correlated with expert graduation (in five levels). A better correlation is obtained with a more precise grading.
authors.affiliation: LISA UPRES-EA 4014, 62, Avenue Notre Dame du Lac, 49000 Angers, France. vincent.roullier@etud.univ-angers.fr
authors.forename: Vincent
authors.initials: V
authors.lastname: Roullier
authors.forename: Christine
authors.initials: C
authors.lastname: Cavaro-Ménard
authors.forename: Calmon
authors.initials: C
authors.lastname: Guillaume
authors.forename: Christophe
authors.initials: C
authors.lastname: Aubé
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D005234:Fatty Liver / Q000473:pathology*; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014617:Vacuoles / Q000473:pathology*