The recently developed automated image-based topology optimization approach, presented in this study, is designed to optimize mechanical stiffness in multi-functional integrated structures. This paper incorporates the bidirectional evolutionary structural optimization (BESO) algorithm within the scaled boundary finite element (SBFE) framework using digital image-based quadtree mesh constructions. This integration allows for the precise definition of topological boundaries of multiphase materials without the use of intermediate density materials. The approach effectively transfers the geometrical characteristics and material distribution from digital photos, enabling direct modeling of stress analysis. The polygonal SBFE approach efficiently handles model development with hanging nodes. Additionally, the pre-computation methodology generates a small number of master cells with balanced quadtree meshes, resulting in a highly efficient design process. Furthermore, convolution filtering facilitates a multi-level quadtree hierarchy by modifying the color intensity between solid components and empty areas. Adaptive quadtree SBFE schemes create practical layouts and significantly reduce the required degrees of freedom, facilitating computationally efficient optimal designs of practical-scale structures. A simple numerical example demonstrates the method’s effectiveness, efficiency, and ease of implementation for multiphase materials topology optimization, ensuring accurate optimal designs for practical-scale structures.