# Quantification of Liver Fibrosis--A Comparative Study
## 3 Literature Review
### Image Analysis Method
#### 3.5.4 Application of Automated Fibrosis Detection Techniques
In the [PERSON] et al. [55] image enhancement process, the red channel in the RGB color space was chosen as the determining factor because the red channel histograms of control tissue and fibrosis differ significantly. Thereafter, various low-pass filtering techniques were applied to reduce the amplitude of image variations, with average pixel filtering producing the optimal results. In parallel, ground truth images, in which the fibrosis areas were manually annotated, were used as inputs to train the Bayesian classifier. The classification accuracy was finally calculated by comparing the fibrosis rate obtained from the automated software with the corresponding ground truth images. The Evolutionary computation (EC) at [PERSON] and [PERSON] [56] utilized the Genetic Algorithm II (NSGA-II) algorithm to ensure the survival of an elite group of offspring population. Initially, the crossover operation was performed after selecting a random pair of programs as parents. Following the crossover operation, the mutation step was performed, where one individual's genes were shown to be likely to be inserted, deleted or modified, which increased the diversity of the created programs. In [PERSON] et al. [57], the application of the K-means algorithm was proven to be more beneficial than thresholding, due to the use of all three channels of the color image. More specifically, the pixel intensity from each channel was used as the feature vector for clustering and overall, the pixel color information was included in the segmentation procedure. Using the above vectors as samples, the K-means algorithm was employed to provide two centroids: (a) 1 = liver tissue and (b) 2 = background. Thus, the number of clusters was set to K = 2 and a square-error criterion for each of the two clusters was calculated, based on the following steps:  
\[\text{Error}^{2}=\sum_{k=1}^{2}\text{e}_{k}^{2}, \tag{8}\]  
with  
\[\text{e}_{k}^{2}=\sum_{i=1}^{n_{k}}(\text{x}_{ik}-\text{M}_{k})^{2}, \tag{9}\]  
where xik is the \(3\times 1\) feature vector of the ith pixel, nk the number of pixels which belong to the kth (k = 1, 2) cluster and Mk the centroids of the kth cluster, respectively according to  
\[\text{M}_{k}=\frac{1}{n_{k}}\sum_{i=1}^{n_{k}}\text{x}_{ik}. \tag{10}\]  
As an expansion of the previous work, in [PERSON] et al. [58], after performing a background-tissue separation through the K-means and Fuzzy C-means (FCM) clustering algorithms, the group turned to the identification of fibrosis. This was achieved by training several classification algorithms, including (a) k-NN), (b) C4.5 decision tree (DT), (c) random forest (RF), (d) multilayer perceptron (MLP) neural network, and (e) support vector machines (SVM), to exclude regions of no interest from the final CPA computation. Advancing with [PERSON] et al. [59], by inserting some constraints to the centers' initialization in K-means algorithm and their update during the clustering procedure, each RGB pixel value was able to be segmented into one of three classes: (1) background, (2) liver tissue, and (3) collagen. In [PERSON] et al. [60], some common image preprocessing operations were performed. Each image was converted to grayscale and 0.1-99.9% histogram percentiles were then computed. For most of the operations, the image was then converted to HSV (Hue, Saturation, Value) to reduce the contrast/luminosity variance in the data. The optimum hue and saturation channel thresholds were determined using the ROC curve on a validation set consisting of several manually labeled images. In the case of deep neural networks, initially, the visual geometry group (VGG) architecture proved to be unstable and not able to learn basic aspects of the disease findings, with U-net being the second fully automated approach to perform pixel-wise region segmentations. All images in [PERSON] et al. [61] were at first obtained from SHG microscopy (Olympus IX81, Olympus, Tokyo, Japan) carrying a \(\times 20\) magnification.