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.

Diagnostic Performance and Accuracy of 3-D Spoiled Gradient-Dual-Echo MRI With Water- and Fat-Signal Separation in Liver-Fat Quantification

“…18,27 Our results confirm and extend these find- 28 regardless to the overall amount of fat displayed in the area which is potentially prone to overestimation of the total LFC. This problem was recently discussed by El-Badry et al 29 who evaluated the interobserver variability of different pathologist analyzing the same histopathological specimen.…”

Automated morphometry provides accurate and reproducible virtual staging of liver fibrosis in chronic hepatitis C

“…A classical limiting factor is thresholding for black-and-white binarization but, in our technique, thresholding was automated. [1011] We observed that cleaning of artefacts had nonstatistically significant effect on morphometric scores, but they induced a large individual variation in about 1 out 20 patients. Since this technical prerequisite is useful, a further study aimed at the automated detection of artefacts is ongoing.…”

“…Only fibrosis pixels were taken into account, thanks to the preprocessing algorithm and the optional manual cleaning step that deleted artefacts or inappropriate structures. Second, this graded-intensity green image was thresholded according to an automated process:[ 10 11 ] a fuzzy generalized classification process allowed for the merging of pixel intensities into three classes (fibrosis, steatosis or healthy tissue) using the minimization of an original energy function. This produced a binary black-and-white image where fibrosis appeared in black, and all other structures appeared in white.…”

Automated Detection of Liver Histopathological Findings Based on Biopsy Image Processing

“…More sophisticated methods have been recently presented based on machine learning. The earliest one is presented by Roullier et al [20], where modifications of the Fuzzy C-Means Algorithm was used to cluster the pixels of HSV (Hue, Saturation, Value) saturation images. Unsupervised clustering was also used by Nativ et al [21], where features of the detected regions were extracted from the rules of Decision Trees.…”