Segmentation is an image processing technique that provides information about various regions of interest in an image. It involves classifying each pixel to one or more classes.
For semantic segmentation, all pixels corresponding to a class are given the same pixel value. For instance segmentation, all pixels corresponding to each instance (or object) of a class are given unique values. The values range from 0 (background) to N, where N refers to the total number of objects in the image.
There are many techniques for segmentation ranging from histogram thresholding to traditional machine learning to deep learning. The exact approach depends on the complexity of the application. Deep learning has been proven to be very efficient at segmenting objects against a busy background.
The following video provides a further explanation by demonstrating segmentation approaches for simple, medium complex, and high complex images, respectively.
For complex images that require deep learning, arivis Cloud offers free tools for semantic and instance segmentation. These tools allow the user to perform segmentation using state of the art deep learning algorithms without writing or interacting with code.
Dr. Sreenivas Bhattiprolu's team at ZEISS focuses on solving tough microscopy challenges by leveraging the latest advancements in digital technology and artificial intelligence. Dr. Bhattiprolu has over 25 years of experience in microscopy. He received his Doctorate in Materials Sciences and Engineering from Michigan Technological University and earned his Master’s degree in Physics from the University of Hyderabad.