## 1 Introduction  
Among the techniques used to identify the presence, absence, or overexpression of proteins of biological interest, sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) is present. This technique separates proteins by applying an electric field to the gel. The proteins move through the gel and are retained in different positions according to molecular weight. The gel is stained using Coomassieie blue. Each of the samples analyzed is represented in columns (lanes), and the horizontal bands correspond to the proteins detected in each piece. Thicker bands indicate higher protein concentration [1] (see Figure 1). The gels obtained are used to visually find proteins and gene fragments in DNA gels [2, 3, 4] or as a method for disease diagnosis [3, 4, 5, 6]. Unlike DNA gels, protein gels can contain many bands per sample, making them difficult for the human eye to interpret (see lane 4 in Figure 1). As a result, misinterpretations of protein gels can occur due to errors generated by optical illusions, visual sensitivity, or fatigue [3, 5, 6, 7]. Image analysis has been used to interpret the DNA and protein bands in the images of SDS-PAGE gels. These analyses include background noise removal, lane detection, and segmentation of the lanes to analyze the protein or gene sought [8; 9]. Many techniques have been used to remove background noise, such as contourlet and wavelet transforms, top-hat transforms, Gaussian low-pass filters, normalization, intensity shifts, median filters, adaptive thresholding, non-linear Gaussians, Fourier analysis, fuzzy-c-means, and some convolution matrix and image slices to search for regions of interest. These methods have been used individually or in combination to obtain improved and noise-free image profiles to extract gel features [1; 2; 3; 4; 5; 7; 8; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Several tools have been employed to detect lanes and segmentation of the bands [1; 2; 3; 4; 5; 7; 8; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. These techniques involve various methods to improve segmentation by selecting necessary pixels related to background contrast. They include edge detection using Bayesian approximations, thresholding, or Otsu segmentation. Users can choose a region of interest to reduce size, minimize noise, calculate standard deviation, and manually set a threshold to generate the gel profile. Division of the area between user-specified lanes can be achieved using Gaussian functions or templates. Sobel filters can be applied to identify lanes and bands in the gel. Additionally, Gaussian processes or templates can split the region between user-specified lanes. Sobel filters can also detect peaks and troughs in the intensity profiles, aiding in identifying the gaps between lanes. Brightness changes can be measured, and the number of pixels in the lanes can be counted to determine their distances using variations in grey levels. Spectral density can be calculated to average the width of lanes, allowing for the selection of specific regions within the image. An analysis is also performed on the profile of the peaks through their areas to group them using techniques such as K-means, and the bands are delimited with ellipses to avoid their intersection and to determine the separation of the lanes. Among all the proposed methods, the ones that have shown the best results include the user's choice of regions of interest to reduce noise and generate  
Figure 1: Image of a polyacrylamide protein gel. The vertical columns or lanes represent different experiments placed within the gel (numbered 1 to 15). The horizontal lines or bands represent the proteins identified per column. more accurate profiles, which facilitates the identification of the minima related to the separation of lanes in the gel.