Feature extraction is typically performed using a combination of image processing and machine learning techniques. Here are some common steps involved:
1. Preprocessing: This step involves preparing the image for analysis by enhancing contrast, removing noise, and correcting for any distortions. 2. Segmentation: This involves partitioning the image into meaningful regions, such as separating cells from the background. 3. Feature Calculation: Once the regions of interest are identified, various features such as cell size, shape, and texture are quantified. 4. Classification: Machine learning algorithms are often employed to classify the extracted features into categories, such as normal or abnormal tissue.
