# Quantification of Liver Fibrosis--A Comparative Study
## 3 Literature Review
### Image Analysis Method
#### 3.5.4 Application of Automated Fibrosis Detection Techniques  
Each image in the work from [PERSON] et al. [50] was analyzed through an image analysis process through the MATLAB 6.5 environment. Key steps initially included some grayscale image enhancement techniques followed by a binary morphological opening operation to smooth the fibrosis structures. Due to the presence of many texture types within a single image, K-means clustering was then performed to obtain the segmentation of the image by isolating the fibrosis regions from the non-fibrosis tissue contents. An MLFFBP-ANN model of one output was then required to classify fibrous samples in one of the six stages of the Ishak system. In [PERSON] et al. [51], before applying a Dense Scale Invariant Feature Transform-Bag of Features (DSIFT-BoF) classification, each image was processed with Wiener filtering to remove any type of detected noise. The DSIFT descriptors came from histogram representations that combined local gradient orientations and magnitudes, in a neighborhood of pixels around a key point indicated by a bin size. The typical BoF strategy required the detected features to be converted into a vector space, taking the form of a normalized histogram. Subsequently, K-means clustering was performed to determine the centroids when grouping these features. Eventually, for every testing image, each feature vector was classified by a weighted k-Nearest Neighbor (k-NN) algorithm. In [PERSON] et al. [52], a sophisticated method was presented for histology image analysis. According to this work, from 69 good quality biopsy samples (>15 mm), three different types of tissue areas were determined (portal, fibrous, and septa), while a set of feature values was extracted from them. Thereafter, they were used to train a multinomial logistic regression (MLR) model and applying it to the remaining 38 suboptimal (<15 mm) specimens in order to detect changes of collagen patterns in cirrhosis progression or regression. Moreover, a stepwise MLR analysis was performed to find the best combination of non-invasive clinical biomarkers to differentiate Ishak stages 5 and 6. In [PERSON] et al. [53], all liver biopsy images were acquired at \(\times\)20 magnification and with a 512 \(\times\) 512 pixel resolution. Correlation analysis between the collagen features and fibrosis stages, along with the Bayesian information criterion (BIC) test, were used to select the most important features for parameterizing the qFibrosis system. In parallel, a nonlinear regression model was selected to combine the collagen features into a single index based on the distribution of fibrosis stages. Eventually, the model revealed 15 features for the best performance to be obtained. Feature selection and model fitting were processed by MATLAB 2015a. The SHG/TPEF images of [PERSON] et al. [54] were acquired using the GenesisTM software, carrying a \(\times\)20 magnification and a 512 \(\times\) 512 pixel resolution. The collagen strings were detected using a segmentation algorithm based on the binary conversion of scanned images. Multiple features including the total length, width, area and perimeter of collagen strings were measured to build two grading method types. In the first method, a linear combination of n-feature variables (X\({}_{1}\), X\({}_{2}\),... X\({}_{n}\)) was applied, as shown in the following equation:  
\[\mathrm{y}=\mathrm{F(x)}=\frac{1}{1+\mathrm{e}^{-(\beta_{0}+\beta_{1}X_{1}+ \beta_{2}X_{2}+\ldots+\beta_{n}X_{n})}}, \tag{6}\]  
where \(\beta_{0}\), \(\beta_{1}\),..., \(\beta_{n}\) denote the coefficients estimated with the maximum likelihood method. The second method employed the supervised SVM classification model with the radial basis function (RBF) kernel, to calculate the distance measure between two feature vectors x and x\({}^{\prime}\) in an m-dimensional space and with m \(\geq\) n:  
\[\mathrm{K(x,\ x^{\prime})}=\exp\left(-\frac{\|\mathrm{x}-\mathrm{x^{\prime}} \|^{2}}{2\sigma^{2}}\right), \tag{7}\]  
where \(\sigma\) was set to control the width of the kernel.