Enhancing medical microscopic images often extends beyond traditional image-processing techniques such as denoising and contrast enhancement by incorporating segmentation to highlight specific structures such as cells, tissues, or pathological regions. Segmentation-based enhancement can improve both the visual clarity and the interpretability of microscopic images. In this study, microscopic medical images were enhanced using the proposed algorithm using Otsu’s method which was applied to segment the Value (V) channel in HSV space into two regions, and each region was separately enhanced with Adaptive Histogram Equalization (AHE). The enhanced regions were then merged, and AHE was applied again. Meanwhile, the Hue (H) and Saturation (S) channels were improved based on the IEIF approach. Finally, the processed HSV channels were converted back to the RGB color space. Meanwhile, the Hue (H) and Saturation (S) channels were enhanced by applying the Enhanced Information Fidelity and Image Entropy approach to the original image in HSV space. Finally, all enhanced HSV channels were converted back to the RGB color space. To assess the effectiveness of the proposed method, several non-reference quality metrics, namely, Average Gradient, Contrast Enhancement Measurement, and the Blind/Referenceless Image Spatial Quality Evaluator, were calculated, and the results showed notable improvements, achieving average values of 21.932, 0.943, and 38.688, respectively.