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Boursier J.,University of Angers | Boursier J.,Angers University Hospital Center | De Ledinghen V.,Haut Leveque University Hospital | De Ledinghen V.,French Institute of Health and Medical Research | And 26 more authors.
Journal of Gastroenterology | Year: 2014

Background: Liver stiffness evaluation (LSE) by Fibroscan is now widely used to assess liver fibrosis in chronic hepatitis C. Liver steatosis is a common lesion in chronic hepatitis C as in other chronic liver diseases, but its influence on LSE remains unclear. We aimed to precisely determine the influence of steatosis on LSE by using quantitative and precise morphometric measurements of liver histology. Methods: 650 patients with chronic hepatitis C, liver biopsy, and LSE were included. Liver specimens were evaluated by optical analysis (Metavir F and A, steatosis grading) and by computerized morphometry to determine the area (%, reflecting quantity) and fractal dimension (FD, reflecting architecture) of liver fibrosis and steatosis. Results: The relationships between LSE and liver histology were better described using morphometry. LSE median was independently linked to fibrosis (area or FD), steatosis (area or FD), activity (serum AST), and IQR/LSE median. Steatosis area ≥4.0 % induced a 50 % increase in LSE result in patients with fibrosis area <9 %. In patients with IQR/LSE median ≤0.30, the rate of F0/1 patients misclassified as F ≥ 2 by Fibroscan was, respectively for steatosis area <4.0 and ≥4.0 %: 12.6 vs 32.4 % (p = 0.003). Steatosis level did not influence LSE median when fibrosis area was ≥9 %, and consequently did not increase the rate of F ≤3 patients misclassified as cirrhotic. Conclusion: A precise evaluation of liver histology by computerized morphometry shows that liver stiffness measured by Fibroscan is linked to liver fibrosis, activity, and also steatosis. High level of steatosis induces misevaluation of liver fibrosis by Fibroscan. © 2013 Springer.

Timouyas M.,Mohammed 5 Souissi University | Hammouch A.,Mohammed 5 Souissi University | Eddarouich S.,Regional Educational Center | Touahni R.,IbnTofail University | Sbihi A.,Abdelmalek Essaadi University
International Conference on Multimedia Computing and Systems -Proceedings | Year: 2014

In this paper, we present a new unsupervised colour image segmentation algorithm using competitive and morphological concepts. The algorithm is carried out in three processing stages. It starts by an estimation of the density function, followed by a training competitve neural network with a new criterion of resemblance called Mahalanobis distance which detects local maxima of the density function, and ends by the extraction of modal regions using an original method based on the morphological concept. The so detected modes are then used for the classification process. Compared to the K-means clustering or to the clustering approaches based on the different competitive learning schemes, the proposed algorithm has proven, under a number of real and synthetic test images, that it is automatic, has a fast convergence and does not need priori information about the data structure. © 2014 IEEE.

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