Computer vision systems in histology typically involve several steps:
Image Acquisition: High-resolution digital images of tissue samples are captured using microscopes. Preprocessing: The images are enhanced and prepared for analysis by removing noise and correcting for variations in staining. Segmentation: The tissue is segmented into different regions, such as nuclei, cytoplasm, and extracellular matrix. Feature Extraction: Relevant features, such as shape, size, and texture, are extracted from the segmented regions. Classification: Machine learning algorithms classify the features to identify patterns and anomalies.
