Clustering of consistent trends is closely related to the image segmentation task, which is often addressed using clustering algorithms , such as K-means. In this work, we employed the K-means algorithm, and the clustering results are shown in Fig. . The first cluster is represented by orange curves with higher intensities, while the second cluster consists of blue curves with lower intensity values. It is important to note that the clustering process considers the behavior of the entire curve rather than individual intensity values at specific time points. For instance, if a curve initially exhibits lower values but later increases in intensity, it is still assigned to the orange cluster (visible in Fig. ). This approach of analyzing the entire curve with K-means differs significantly from simple thresholding, which relies on intensity values at specific time points and is unsuitable for this context.