Spatial proteomics via multiplexed tissue imaging is transforming how we study biology, enabling researchers to investigate dozens of markers in a single tissue section and explore how cells behave in their native habitat. While imaging technologies have advanced rapidly, data analyses remain a bottleneck. To address this, we developed PIP{Sigma}X (Pipeline for Image Processing and EXploration), a user-friendly, end-to-end open-source software designed to make complex image analysis approachable, even for those with little or no programming skills. PIP{Sigma}X combines robust automation with an intuitive graphical user interface, guiding users through each step of the analysis, from image preprocessing and membrane-aware cell segmentation to signal quantification and spatial data exploration. Each feature includes built-in explanations, recommendations, and quality controls to help users make confident choices throughout the process. PIP{Sigma}X is compatible with a wide range of multiplexed imaging platforms, and its outputs integrate seamlessly with visualization tools like TissUUmaps and QuPath. Also, it supports downstream applications by enabling direct export of selected cell coordinates for laser microdissection. This functionality facilitates precise isolation of target cell populations for deep proteomic or transcriptomic profiling. With PIP{Sigma}X, researchers can extract meaningful biological insights from multiplexed images more easily and robustly, helping to bridge the gap between powerful imaging technologies and real-world scientific discovery.