## 3 Feature Extraction from Dermoscopic Images
### Extraction of Edge Feature Information via Line-Segment-Type Analysis
Subsequently, \(\alpha\) is calculated using the mean \(m_{G}\) and standard deviation \(\sqrt{\sigma_{G}^{2}}\) of the image \(I_{T}(\cdot)\), and the optimal \(k\) and \(\overline{k}\) are generated. Figure 4a shows the skin lesion image, and the image obtained after applying image thresholding using Equations (1) and (2) is shown in Figure 4b. When the \(k\) derived using [PERSON]'s algorithm is applied to image (b), the OB information is obtained as shown in Figure 4c. Subsequently, by applying \(\overline{k}\), the IB image is obtained, as shown in Figure 4d. Figure 4e,f show edge images detected by the Canny edge detector from the images shown in Figure 4c,d, respectively. Figure 4e shows the extraction of the IB region, and Figure 4f shows the line-segment feature of the OB region. Finally, the image shown in Figure 4g is generated by the XOR operation of the images shown in Figure 4e,f. Figure 4: Edge feature collected through inter & outer-border extraction and canny edge detector; (**a**) skin lesion image, (**i**. ), (**b**) thresholding image by \([q_{1},q_{2}]\), \(I_{T}(\cdot)\), (**c**) Otsu \(-k\) image, \(I_{B}^{O}(\cdot)\), (**d**) Otsu \(-\overline{k}\) image, \(I_{B}^{I}(\cdot)\), (**e**) Otsu \(-k\) edge image, \(I_{E}^{O}(\cdot)\), (**f**) Otsu \(-\overline{k}\) edge image, \(I_{E}^{O}(\cdot)\), (**g**) XOR image \(I_{E}(\cdot)\) of (**e,f**). Figure 3: Example of a histogram for skin lesion image; (**a**) original image, (**b**) histogram of original image, (**c**) thresholding image, (**d**) histogram of thresholding image. In Figure 4b, the normal skin region was removed via the previous processing; however, the normal skin region remained around the lesion. Therefore, [PERSON]'s algorithm was used to distinguish between the normal skin region and lesion such that only the normal skin region is extracted, as shown in Figure 4c. As for the regional separation, the boundary lines closer to the center represent the characteristics inside the lesion, while the boundary lines further away from the center form the outer shape of the lesion. After extracting the edges from the images shown in Figure 4c,d using the Canny detector (i.e., (c) -> (e), (d) -> (f)), an XOR operation was performed to remove the border of the normal skin region, which is the common area in the two images, and the resulting image is presented in Figure 4g. As shown in Figure 4g, when an edge image with visual shape information of the IB and OB for the skin lesion was generated, line-segment-type analysis was performed in pixel units using a detector filter based on Equation (3). ```
Input: \(I_{T}(x,y)\) Image with removed brightness via thresholding \(I(x,y)\); image size, \(N\times M\) Output: Inner and outer border-image \(I_{B}(x,y)\) and edge image \(I_{E}(x,y)\) of \(I_{B}(x,y)\) 1 Generate an array \(hist[L]\) of size \(L\) to create a histogram distribution diagram 2 Use the brightness intensity of input \(I_{T}(x,y)\) as the index of array \(hist[L]\) and increase the value of the corresponding index by 1. 3 Repeat Step 2 to scan all the pixels of the input \(I_{T}(x,y)\). 4 For array \(hist[i]\) that has completed step 3, obtain the normalized histogram value is using \(p_{i}=\frac{hist[i]}{N\times M}\).