Image preprocessing plays a pivotal role in the computational analysis of histological images, especially in veterinary pathology. This chapter introduces practical examples for various preprocessing techniques, which enhance image quality and ensure consistency for further analysis, such as segmentation, feature extraction, and classification. Key methods discussed include noise reduction, contrast enhancement, normalization, resizing, cropping, and data augmentation. Noise reduction methods like Gaussian blur and median filtering minimize artifacts without compromising diagnostic detail. Contrast enhancement, including histogram equalization, improves the visibility of subtle features in poorly contrasted images. Image normalization and calibration standardize images to account for variations in lighting, staining, and magnification, enabling accurate and reproducible measurements. Resizing and cropping optimize image handling and processing by focusing on regions of interest and ensuring consistent image dimensions. Data augmentation, particularly valuable in the context of limited veterinary datasets, simulates real-world variability through transformations like rotations and flips, improving model generalization and robustness. Together, these preprocessing techniques facilitate more precise and accurate computational analysis, ultimately contributing to better diagnostic outcomes in veterinary pathology.