## 2 Materials and Methods
### Choroidal Neovascularization (CNV) Detection  
After identifying the clearer vascular regions, we wanted to use the shape of the blood vessel to identify the CNV; however, the thickness of the blood vessel may cause misjudgment to occur during subsequent recognition; therefore, we must pre-process the blood vessels into the same width as was prior. The method we used was the thinning of morphology, which can reduce the thickness of the vascular regions down to the width of one pixel without having all the shapes disappear [20]. By using this approach, the shape of the region appears clearer, allowing for accurate recognition on the next step, giving the shape of the feature a more defined effect. The thinning method is used to remove the pixel point for each pixel in the binary image. As long as the two conditions, named as G1 and  
Figure 4: The schematic diagram of connected-component labeling. Mark the connected white pixels (value equal to 1) with the same color and label numbers. Figure 5: An example of clear blood vessel detection: (**a**) Otsu’s method; (**b**) Majority; (**c**) Size filter. G2, are satisfied, and a repeat of this action is done until the image is not changed, then you can get a blood vessel image with a thickness of the width of one pixel. The conditions of G1 and G2 are as shown as Equations (13) to (15). Condition G1:  
\[\sum_{i=1}^{4}bi=1,\text{ where }bi= \begin{cases}1,&\text{if }x_{2i-1}=0 \text{ and }(x_{2i}=1\text{ or }x_{2i+1})\\ 0,&\text{otherwise}\end{cases} . \tag{13}\]  
\(x_{i}\) is the value of eight neighboring pixels on each center pixel. Condition G2:  
\[2\leq\min\{n_{1}(p),n_{2}(p)\}\leq 3, \tag{14}\]  
\[\left\{ \begin{array}{l}n_{1}(p)=\sum\limits_{k=1}^{4}x_{2k-1}\lor x_{2k}\\ n_{2}(p)=\sum\limits_{k=1}^{4}x_{2k}\lor x_{2k+1}\end{array} \right.. \tag{15}\]  
\(x_{i}\) is the value of eight neighboring pixels on each center pixel. Because the OCTA image is subject to a large amount of noise interference, the blood vessel is prone to shape breakage or even fracture when the image segment was processed in the previous steps. If the regions containing the blood vessels cannot be completely presented, this will inevitably lead to errors in shape judgment; therefore, we must reconnect these broken parts. The approach taken in this paper is to first locate all of the endpoints, which is the end point of the blood vessel in each connected-component. If the endpoints of two different connected-components are fairly close together, we will connect them. For every two different connected-components, we only selected the two nearest endpoints to connect, and through this method, the broken blood vessels were reconnected. Among them, the endpoint finding algorithm in each connected-component is that of each pixel location. If the eight neighboring pixels around the center pixel have only one value equal to 1, we can treat it as an endpoint. For each endpoint, we looked for all the pixel locations in its \(5\times 5\) mask. If the endpoints of other different connected-components can be found in this mask, and they are the nearest two endpoints between two connected-components, we will connect the two endpoints to each other through a straight line. This way the broken blood vessels can be connected to avoid the influence of subsequent judgment. After locating the main vascular region and performing the thinning process, we started to search for the location of the CNV. The feature used in this paper is the eccentricity. The eccentricity is independent of the region size and directionality, but only effected by the shape [21]. Since the shape of CNV is different from that of general blood vessels, the eccentricity of CNV should also be significantly different. Therefore, we try to use eccentricity to find CNV. The eccentricity represents the relationship between the major axis and the minor axis on an ellipse. On the binary image, for each connected region, the two pixel points which are at the farthest distance in each connected-component are first connected to a line.