Several techniques are employed for image segmentation in histology, each with its own strengths and weaknesses:
Thresholding: A simple method that converts an image into a binary image based on a threshold value. It is effective for images with high contrast but may not work well for complex histological images. Edge Detection: Identifies boundaries within an image by detecting discontinuities. Common algorithms include the Canny edge detector and Sobel operator. Region-Based Segmentation: Divides an image into regions based on predefined criteria such as texture, color, or intensity. Techniques include region growing and watershed algorithm. Machine Learning: Uses algorithms like random forests or support vector machines to classify pixels. Requires extensive training data for effective performance. Deep Learning: Utilizes neural networks, particularly convolutional neural networks (CNNs), for highly accurate segmentation. Popular models include U-Net and Mask R-CNN.
