Multi-focus image fusion combines two or more images of the same scene with different focus points to create a single detailed fully-focused image. The primary purpose of multi-focus image fusion methods is to transfer the correct focus information from the source images to the fused image. This study proposes a new classification mechanism based on focus metrics. This mechanism is designed to classify focused, non-focused and ambiguous regions. The most important feature of the proposed mechanism is that it can detect ambiguous areas and transfer these regions to the fused image correctly. Firstly, each source image is split into non-overlapping image patches of specified sizes in this study. Then, the generated image patches are classified using created classification mechanism. After the classification process, a decision map is created for each source image. These decision maps are then refined using morphological operations. In the final stage of the designed study, a dynamic fusion rule is proposed. This fusion rule transfers focused and non-focused pixels to the fused image according to a specific rule. In contrast, ambiguous regions, frequently encountered in transitions from focused to non-focused areas, are transferred to the fused image using the gradient-based fusion rule. In this way, the negative effect of the regions that the classification algorithms classified incorrectly on the fused image is reduced. In addition, in this study, the impact of image size on image fusion success is analyzed by using different image sizes in the classification mechanism. As a result, the proposed study is evaluated using objective and subjective metrics. The evaluations show that the proposed method is suitable for achieving multi-focus image fusion purposes.