This chapter introduces some practical examples for image segmentation and feature extraction as essential processes in computational image analysis for veterinary pathology. Image segmentation divides an image into distinct regions representing meaningful components like cells or tissue structures, a critical step for accurate diagnosis in veterinary pathology. Techniques such as thresholding, edge detection, and advanced machine learning models, including convolutional neural networks (CNNs), are discussed for their ability to delineate pathological features such as tumor boundaries or necrotic areas. Feature extraction, which follows segmentation, focuses on quantifying characteristics of the segmented regions, such as shape, texture, color, and intensity, to facilitate further analysis or diagnosis. The interplay between segmentation and feature extraction enhances the interpretation of complex histological images, providing insights into disease mechanisms, tissue morphology, and cellular behavior. Applications of these methods in veterinary pathology are explored, particularly their roles in automating diagnostics, improving reproducibility, and advancing research. This chapter also highlights the importance of preprocessing steps like noise reduction and contrast enhancement, which improve segmentation accuracy, ultimately leading to more precise and reliable diagnoses.