Digital image processing is fundamental to modern medical imaging, enabling feature extraction, visual structure enhancement, and computer-aided diagnosis. With the development of computational power and AI, traditional image analysis techniques have evolved into hybrid systems combining classical signal processing with machine learning, especially in histology for cancer cell detection and classification. AI, particularly machine learning, has brought new opportunities in digital pathology, like CNNs showing high accuracy in tissue differentiation. However, they rely on large annotated datasets and lack adaptability. This has led to the exploration of Reinforcement Learning (RL), which formulates image analysis as a sequential decision-making process. In histopathology, segmentation is crucial for quantitative analysis, and Q-learning, a model-free RL algorithm, can iteratively adjust segmentation parameters without pre-annotated data. Q-learning in medical imaging offers adaptivity to image-specific characteristics and robustness against suboptimal segmentation. This chapter covers image processing and histological segmentation fundamentals, RL framework introduction, reformulating adaptive thresholding as a Q-learning problem, implementation details, case studies on real histological images, and a discussion of the approach’s benefits, limitations, challenges, and future perspectives.