## 3 Results and Discussion
### Detection of Protein Overexpression in Gels Using the IPBBIS Algorithm
#### 3.3.5 Choice of Threshold for the Elimination of Multiple Maximums  
IPBBIS automatically detected the number of samples present in a gel image. The multiple minimum values between the various maxima indicated the number of samples in the polyacrylamide gel. Sometimes, when matching these values, marked by blue dotted vertical lines, it was impossible to detect the number of samples in the gel image (see Figure 13A). The amount of contaminating proteins caused the excess background noise in the samples. We chose a threshold containing the minimum values for this case to eliminate the background noise. In this way, we allowed the elimination of the multiple maxima detected in the graph obtained by the new image intensity profile. These multiple maxima were removed with a low-pass filter (represented by the black line in Figure 13B) applied to the latest image intensity profile, and a new graph was obtained in which the minima corresponded perfectly to the existing separations with every sample present in the gel image (Figure 13C). This method allowed us to automatically select the regions with the tiniest white pixels and link them to the regions where the samples are separated. As a result, the experiments present in the gel were automatically detected and identified with blue dotted vertical lines that perfectly matched the sample separations in the image (Figure 13D). They were identified with blue lines drawn on the gel image to verify their correspondence with the different samples. The data were stored in an array to determine the position of each of the samples in subsequent analyses. Figure 13: (**A**) Detected maxima (blue dash lines). (**B**) Threshold that allows obtaining a cut-off region that includes only the minima that represent the separation of the samples. (**C**) Graph obtained from the cut-off region. (**D**) Total of automatically detected samples (blue dash lines). \begin{table}
\begin{tabular}{c c c} \hline
**Interpolation Method** & **Calculated Weight (kDa)** & **Total Error \%** \\ \hline Linear & 33.4 & 3.35648148 \\ Nearest & 37.0 & 7.060185185 \\ Cubic & 31.38 & 9.194960019 \\ \hline \end{tabular}
\end{table} 
Table 3: The table shows the error obtained by applying three numerical interpolation methods to the polyacrylamide gel ladder in Figure 11A. 3.6 Analysis of the Region of Interest Using the IPBBIS Methods, Manual Area Calculation, Area Calculation by K-Means Segmentation, and Area Calculation by Otsu Segmentation  
A random sample was selected on the gel to repeat the molecular weight detection process (lane 5, Figure 14A). The developed IPBBIS method was applied to detect the bands present per sample, as indicated in Figure 14B; knowing the molecular weight of the GPN protein, approximately 34 KDa, the interpolation method was applied to detect it automatically within the gel, and it was identified as indicated in Figure 14C. Once the positions separating the samples in the gel were detected (Figure 14B), the molecular weight of the GPN protein and its place within the gel (Figure 14C) were identified, then the region of interest (ROI) that included the expression of the recombinant protein at different concentrations from all samples (red box in Figure 14A including lanes 2 to 11) was selected and isolated as shown in Figure 14D. Analysis was then performed to compare protein overexpression and to identify whether image profiling based on binarized image segmentation can detect proteins with higher or lower overexpression. Before this, the image color in Figure 14D was changed from RGB to HSV. The result is shown in Figure 14E, where the height of the peaks is related to the level of overexpression of the protein analyzed. Every sample with protein contained in the region of interest (ROI) in Figure 14D (lanes 2, 4, 5, 6, 6, 7, 8, 9, 10, and 11) was isolated to obtain a higher precision in the analysis.