Anatomical object recognition and labeling by atlas-based focused non-rigid registration and region-growing

Computer-assisted recognition of anatomical objects in medical images is at the center of many important clinical applications. Automatic extraction and recognition of human abdominal structures from CT images has been particularly challenging for medical imaging research and applications. Intra-patient and inter-patient spatial, morphological and intensity variability is typically significantly contributing to great difficulties in developing satisfactory automatic solutions to the problem. In this paper we report on a new approach, which treats recognition of anatomical objects from a given medical image as a task of non-rigid registration followed by segmentation. It uses the knowledge inferred from an atlas or model image to specify a sequence of smaller sub-image spaces or spatial contexts to register progressively the atlas image with the given patient image. The labels of the target objects in the atlas image are carried over to the patient image by the registration process representing the recognition result, which is further improved by a region-growing process. Preliminary experiments of artery blood vessel recognition and labeling with real patient data have demonstrated the potential of the method to be a viable alternative solution to the problem.