Quantitative image analysis in veterinary medicine has revolutionized the diagnosis and prognosis of various animal diseases by providing objective and reproducible data from microscopic images. This chapter explores novel practical examples in Python, for some transformative computational techniques on traditional pathology, specifically through automated analysis of biological tissues and cells. Quantitative methods offer significant advantages over subjective visual interpretation, including the ability to accurately measure cell density, morphology, and spatial distribution. These parameters are crucial for diagnosing conditions such as cancers and inflammatory disorders, where changes at the cellular level are indicative of disease. Advanced segmentation algorithms, texture analysis, and feature extraction further enable detailed tissue characterization at both microscopic and macroscopic levels. The integration of deep learning and artificial intelligence into image analysis has expanded diagnostic capabilities, allowing for the detection of complex patterns associated with disease progression. Automated cell counting, morphological measurements, and staining intensity quantification significantly enhance diagnostic accuracy while reducing human error. The chapter also discusses the applications of these techniques in research and education, where they contribute to a better understanding of disease mechanisms and training of veterinary professionals.