Designing automated algorithms to extract and analyze anatomical brain structures from neuro-images is of significant scientific and clinical importance in detecting abnormal brain patterns, analyzing various brain diseases, and studying the brain growth.
This project will develop a general statistical modeling/computing framework to perform 3D holistic brain image understanding. The framework emphasizes rigorous, efficient, and effective learning-based statistical models to integrate the complex appearances, varying 3D shapes, and the large spatial configuration of anatomical brain structures.
Implicit models through discriminative approaches have the advantages of fusing a large amount of information and obtaining decisions quickly. Explicit models through generative approaches can directly represent the information and thus, better explain the structure and model the transformation and scale change. The PI explores harmonic relationships between discriminative and generative models for 3D image parsing by combining implicit and explicit models along several directions: (1) learning-based models with rich appearance, and implicit shape and context; (2) integrating skeleton with surfaces for 3D shapes; (3) effective 3D shape representation and similarity measure; (4) component-based simultaneous registration and segmentation.
This research will contribute to automating the process of extracting a large number of anatomical structures, and enhancing the shape analysis needed for detecting brain diseases, monitoring health conditions, studying drug effects, and discovering brain functions. The scope of the proposed model goes beyond medical image analysis and can be applied in other problems of statistical modeling/computing, computer vision, multi-variate labeling in machine learning.
