# Quantification of Liver Fibrosis--A Comparative Study
## 3 Literature Review
### Image Analysis Method
#### 3.5.2 DIA Systems Using Manual Thresholding  
Commonly, image analysis involves a segmentation procedure (i.e., histogram thresholding) for the detection of collagen regions and the elimination of artifacts and other undesired areas. In most methods for calculating liver fibrosis, the authors processed the images manually. [PERSON] et al. [17] analyzed a set of grayscale images obtained from a Leica DMR microscope (Leica, Wetzlar, Germany) to quantify the percentage of fibrosis. The images were digitized at \(\times 100\) magnification. The authors mentioned that a user-defined threshold was applied to convert the images into binary. Then, a semi-automated procedure was used to eliminate the unwanted artifacts in them. Finally, the percentage of the fibrosis (fibrosis index--FI) was computed using the identified areas of fibrosis, divided by the whole area of the hepatic parenchyma, as follows:  
\[\text{FI}(\%)=\frac{\text{Total stained fibrosis area}}{\text{Total area of sample}}\times 100. \tag{1}\]  
In terms of the image analysis field, the first extended work that presented the FibroQuant software was that by [PERSON] et al. [18]. The method consisted of four steps, and it was applied to a biopsy image with a magnification of \(\times 200\). During the first step, a histogram equalization procedure enhanced the initial image. In the next three steps, different adaptive thresholding techniques have been used to detect specific areas, including percentage areas of perisinusoidal fibrosis (PSF), portal-periportal and septal fibrosis (PPF) and portal vessel and biliary duct lumina (PLA). In all phases, user inference was allowed to improve the inclusion of these areas when the thresholds failed. FibroQuant was also utilized in [PERSON] et al. [19] in order pre and post-treatment biopsies to be compared and for fibrosis areas isolation, according to their staining and their exterior limits. Each biopsy image in [PERSON] et al. [21] was edited with a morphometric program and with an adequate threshold to produce a monochrome image. The staining area was calculated with a computerized digitizer, in which the fibrosis index (FI) was determined according to Equation (1) and calculated in 18-20 randomly selected fields, dependent on the size of the sample. To evaluate the chronological change of FI from the hepatic portoenterostomy to the stroma closure (\(n=26\)), the difference rate of FI (FIDR) was defined as follows:  
\[\text{FIDR}(\%/\text{mo})=\frac{\text{FI at stoma closure}-\text{FI at HPE}}{\text{Interval from HPE to stoma closure (months)}}. \tag{2}\]  
[PERSON] et al. [22] used the Scion (Scion Corporation, Frederick, MD, USA) image software. Microscopy was performed using high levels of illumination, in addition to a green optical filter to control the picro-Sirius red influence without distorting the color values of fibrotic areas. All histological images were initially converted to grayscale and the total tissue area was calculated with a threshold value. The pixel count was then measured and divided by the total tissue area to calculate the proportion of fibrous tissue staining on each slide. In [PERSON] et al. [23], the fibrosis ratio for each liver biopsy was calculated using the Optimas 5.0 (Media Cybernetics, Silver Spring, MD, USA) image analysis software. Images from trichrome-stained sections were captured with a magnification of \(\times 40\) and analyzed as RGB (Red, Green, Blue) 24-bit images. The stained blue section represented the fibrosis, while the red, the parenchyma respectively. After interactive thresholding, the image was converted into binary, where the two-dimensional patterns were measured by direct pixel counting. In [PERSON] et al. [25], the Image-Pro Plus 4.5 (Media Cybernetics Co., Rockville, MD, USA) software was used to analyze the digitized biopsy specimens as RGB 24-bit color images. Fibrosis (%) measurements for six liver biopsies were compared using both the \(\times 40\) and \(\times 100\) magnifications.