# Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
## 4 Discussion  
Researchers face daily the task of highly complex and information-rich digital data that require computer tools to correctly visualise, analyse, and interpret large amounts of data. Therefore, the scientific community needs versatile and efficient software capable of handling massive amounts of information, thus avoiding a bottleneck between the image acquisition and obtaining results [10]. Various software for digital image processing are available [5; 22], and the most widely used is ImageJ, an intensely robust open-source tool that allows the analysis of numerous parameters in a single image [23]. In this context, IF images represent highly informative systems, as they allow to obtain valuable information about the localisation and the expression levels of cellular proteins [24; 25]. The development of automated computer systems capable of identifying and quantifying IF signals is a particularly daring challenge, as it could not only remove the interpretation bias introduced by the user but also ensure objective, standardised and reproducible analysis, even on large image sets. Recently, several methodologies that automatically analyse cell images have been developed. They are based on automatic thresholding algorithms [26; 27] and segmentation methods [28; 29; 30]. Among these, the _NeuronCyto_ tool, based on watershed segmentation, provides quantitative measurements of several biologically relevant information, such as the length, the branching complexity, and the neurite number [31]. Regarding segmentation in IF images, classification consists of distinguishing pixels into different clusters. The current methods are based on threshold techniques [26], k-means clustering algorithms [32], mean shift clustering approaches [33], and methods based on geodesic active contours [34]. However, the translational application of automated software in cellular image analysis is rare, due to several obstacles. First, most methodologies employ an optimal threshold, previously set. Indeed, algorithms are often designed for a very specific hypothesis. This constitutes a considerable limit, as it does not accommodate the enormous variability that signals can present: some are low, others intense, others might manifest with unexpected shapes or sizes, etc. Therefore, basing the detection algorithm on generic or frequent characteristics may fail for some images, where the preset parameters may require user adjustments. Furthermore, most of the present methodologies have not been published with an open-source script that would allow not only to understand in detail how they work, but also to reproduce or adapt the algorithms. Aware of these limitations, we develop a new open-source workflow that combines automatic IF image quantification with statistical data analysis, providing a simple and user-friendly graphical interface. The signal quantification is based on the automatic pixel analysis to extrapolate only those of interest. Indeed, the calculation of the ROIs is ensured  
Figure 10: Comparison between the Otsu and Q-IF thresholding methods. Fluorescence intensity is quantified in SH-SYSY cell line pictures with Otsu’s (**left panel**) and the Q-IF thresholding methods (**right panel**). Ten pictures are selected and, after the two thresholding analysis, the graphs are obtained automatically by the Q-IF tool. Data represent mean \(\pm\) SD. Statistical analysis is performed with the unpaired Student’s \(t\)-test. *** \(p<0.001\). by two successive steps: a first opening operation to eliminate spurious pixels and the second closing operation to join the largest points in the image and include only the specific pixels. Only after that, the algorithm extrapolates the individual ROIs from the initial image. These operations guarantee analytical specificity, as only the pixels of the cellular signals are detected and calculated.