This chapter introduces the growing importance of computational image analysis in veterinary medicine, particularly in the analysis of microscopic images used in veterinary pathology. As diagnostic complexity increases, the integration of computational methods has become essential for veterinary practitioners. The chapter begins by outlining the basics of digital imaging and image acquisition, focusing on how images are captured, processed, and analyzed to extract valuable diagnostic information. Computational image analysis provides tools to handle large datasets, automating tasks such as cell counting, tissue segmentation, and feature extraction. These techniques enhance diagnostic accuracy and speed, enabling the detection of subtle patterns in histological images. The chapter also explores practical tools and software available for image analysis, including open-source platforms like ImageJ and CellProfiler, and advanced options like QuPath and HALO. Additionally, the chapter discusses the integration of computational methods into veterinary practice, emphasizing consistency in image acquisition and the role of machine learning. Case studies illustrate how these technologies improve diagnostic precision and workflow efficiency. The chapter aims to equip veterinary professionals with a solid foundation in computational image analysis, enabling them to apply these techniques to improve patient outcomes and streamline diagnostic processes.