# Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
## 3 Methodology
### Feature Extraction and Feature Selection  
In this section, we have generated various normalized color spaces for feature analysis. RGB, HSV, YCbCr, CIE Lab, YUV, XYZ, and CMYK color spaces have been chosen for feature extraction and feature selection. By using these secen color spaces, we have analyzed 22 separate color bands individually. These bands contain various information of lesion and it is essential to identify the best band for the classification step. We have used two methods to identify the best suitable band by separating foreground (non-ulcerated) and ulcer pixels. These methods involve statistical analysis of foreground and ulcer pixels in each band. We have implemented the first method (or Method 1) named as the overlapping area (OA) to measure the overlapping area between ulcer and foreground pixels. The normal distribution curve for foreground and ulcer pixels is analyzed from each pixel set. Here, we are able to highlight the index of separation for each band which shows overlapping area between foreground  
Figure 2: Methodology of proposed feature selection and classification. and ulcer pixels. Of course, lesser overlapping area signifies better separation in a particular band. The second method (or Method 2) uses overlapping area with cross-correlation (OACCorr) value in each band. Cross-correlation [27] is a standard measure for determining similarity between two images or matrices. It is more accurate and computationally more efficient. Additionally, it depends on the calculation of covariance between two bands. The method of fast normalized cross-correlation is fast, independent, and accurate (in terms of pixel contrast and brightness values) in computing image similarity. This technique involves the estimation of correlation between all bands in the data cube of band \(i\). Equation (1) was used to estimate the correlation values of other bands in \(j\) data cube:  
\[NCC_{i,j} = \frac{\sum_{xy}\left[\left(D_{i}(x,y)-\overline{D_{i}}\right) \times\left(D_{j}(x,y)-\overline{D_{j}}\right)\right]}{\sqrt{\sum_{xy}\left(D_{ i}(x,y)-\overline{D_{i}}\right)^{2}}\times\sqrt{\sum_{xy}\left(D_{j}(x,y)- \overline{D_{j}}\right)^{2}}} \tag{1}\]  
where \(NCC_{i,j}\) is the normalized cross-correlation between band \(i\) and \(j\), \(D_{i}(x,y)\). is the intensity of pixel, \((x,y)\) are the pixel indices within one band, and \(\overline{D_{i}}\) is the mean of pixel intensity values of band \(i\). Table 1 shows the OA and OACCorr values for 22 color bands used in extracting the color features. We have used seven color spaces in the following order: RGB, HSV, YCbCr, CIE Lab, YUV, XYZ, and CMYK. The first color space is RGB with rgbR, rgbG, and rgbB color bands with allotted band numbers of 1, 2 and 3, respectively. The second color space is HSV with hsvH, hsvS, and hsvV color bands. Their allotted band numbers are 4, 5 and 6, respectively. The third color space is YCbCr with ycbrY, ycbrCB, and ycbrCR color bands. Their allotted band numbers are 7, 8 and 9, respectively. The fourth color space is CIE Lab with CIE Lab, CIE LabA, and CIE LabB color bands. Their allotted band numbers are 10, 11 and 12, respectively. The fifth color space is YUV with yvuY, yvuU, and yuvV color bands of allotted band numbers 13, 14 and 15, respectively. The sixth color space is XYZ with xyzX, xyzY, and xyzZ color bands of allotted band numbers 16, 17 and 18, respectively. Finally, the seventh color space is CMYK with four bands, i.e., cmykC, cmykM, cmykY, and cmykK. Their allotted band numbers are 19, 20, 21 and 22, respectively. These entire bands are used as feature vector for further classification. From Table 1 (columns 2 and 3), the overlapping areas of the respective band number and band name obtained using Method 1 are displayed in an incremental manner. For Method 2 (see columns 4 and 5 in Table 1), the overlapping areas added with standard measure of degree of similarity between two images are displayed in an incremental manner as well.