Single-cell spatial omics analysis requires consideration of biological functions and mechanisms in a microenvironment. However, microenvironment analysis using bioinformatic methods is limited by the need to detect histological morphology and extend it to the surrounding area. In this study, we developed SpatialKNifeY (SKNY), an image-processing-based toolkit that detects spatial domains that potentially reflect histology and extends these domains to the microenvironment. Using spatial transcriptomic data from breast cancer, we applied the SKNY algorithm to identify tumor spatial domains, followed by clustering of the domains, trajectory estimation, and spatial extension to the tumor microenvironment (TME). The results of the trajectory estimation were consistent with the known mechanisms of cancer progression. We observed tumor vascularization and immunodeficiency at mid- and late-stage progression in TME. Furthermore, we applied the SKNY to integrate and cluster the spatial domains of 14 patients with metastatic colorectal cancer, and the clusters were divided based on the TME characteristics. In conclusion, the SKNY facilitates the determination of the functions and mechanisms in the microenvironment and cataloguing of the features.