Fibrosis involves the development and hardening of fibrous connective tissue due to organ damage and marks the initial stage of cancer. Detecting fibrosis early is crucial to prevent further cell damage. This paper introduces a method for identifying fibrosis-affected regions in zoomed, colorful images of rat liver tissues by combining two advanced techniques: the Gaussian Radial Basis Kernel Function and Spatial Neighborhood Information with a conventional fuzzy segmentation algorithm. The experimental findings indicate that the proposed method, integrating the Kernel Function and Spatial Information with Fuzzy Cluster Mean (SKFCM), is more effective in fibrosis detection than using either Kernelized Fuzzy Cluster Mean (KFCM) or Spatial Fuzzy Cluster Mean (SFCM) alone. Visual comparisons, segmentation validity metrics, and pixel accuracy data support this conclusion.