Project Summary (Song Wang) EIA-0312861 Song Whang USC Research Foundation
The proposed research is to develop an integrated and practical framework for shape modeling and use it to achieve more reliable segmentation of medical images. This research is motivated by the fact that reliable extraction, representation, and incorporation of some prior shape information can greatly reduce the segmentation error resulting from image noise. Three important problems will be addressed and integrated to effectively explore shape information for medical image segmentation: shape representation, shape correspondence, and shape deformation. The shape representation is to compactly describe a shape in terms of a set of landmark points. The shape correspondence is to establish landmark-based correspondence among a set of shape samples. The shape deformation is to first construct a shape template that captures representative shape characteristics of a particular object class, and then use it to delineate an object of the same class from a new image. Intellectual Merits: This research studies the shape modeling method from a unique systematic perspective. While previous researches usually focus on a particular problem in shape modeling, we will investigate all three interrelated key problems, i.e., shape representation, shape correspondence, and shape deformation, in an integrated and unified framework, specifically for medical image segmentation. Under this unified framework, we develop novel methods to address most important issues in each of these three problems. In the shape representation, we will incorporate our resolution-independent feature extraction method to estimate key shape parameters such as position, tangent direction, and curvature more accurately. This will provide a representation format that is accurate, compact, and flexible in describing a group of shape instances. In the shape correspondence, we will develop global and local correspondence methods to choose landmarks based on the following criteria: (1) simple shape representation, (2) small representation error, and (3) optimal inter-shape landmark correspondence. An advanced network-flow technique will be used to integrate these three measures into a new unified formulation. In the shape deformation, we will develop effective shape-learning techniques based on a group of well-corresponded training samples. The techniques employ more general and accurate probability distribution models than simple Gaussian distributions. Based on the models, we will further develop our shape deformation method to deal with important issues like algorithm robustness and topology preservation. Broad Impacts: Fast growth of medical imaging industry provides us tremendous amount of data in many forms like MRI, CT, PET, cryosection, microscopic, etc. It is urgent to develop advanced information technology to convert those medical data into useful information. Among them, geometric shape information is of particular importance as they are widely used in clinical diagnoses. The proposed research will greatly facilitate the shape information exploration from medical images and provide physician and radiologists new and powerful computational tools. In the long term, this research will further intensify the current efforts to bridge the gap between information technology and medical applications. Furthermore, the proposed research can be applied to many other applications like video tracking and biometrics that shares the same problems as in shape exploration from medical images. This research also provides many educational contributions. We will integrate the medical image processing into a series of image processing and computer vision courses in the University of South Carolina (USC). This research will also actively contribute to the K-12 education in the state by assisting the NSF Bridges for Engineering Education Program awarded to the University and participating in the summer mentor program by the South Carolina Governor's School for Science and Mathematics. Biomedical engineering has been selected as one of three main focus areas for research at USC. Thus, our research effort will directly contribute to the university wide efforts. In particular, our effort can be integrated with the neuro-imaging initiative at the Psychology Department and the new Center for Colon Cancer Research.
