Introduction to Medical Image Recognition, Segmentation, and Parsing
Abstract We introduce a probabilistic formulation that unifies medical image recognition, segmentation, and parsing into one modeling framework based on a rough to exact shape representation. We then present schemes to decompose a highly complex problem into several simple subproblems, leading to a general purpose computational pipeline. When it comes to more effective models, we leverage machine learning methods, especially discriminative learning, to depict anatomical context embedded in the medical image. We then provide a short review of commonly used discriminative methods and finally discuss a few classical segmentation algorithms for segmenting a single object.