Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach [chapter]

Yang Wang, Bogdan Georgescu, Terrence Chen, Wen Wu, Peng Wang, Xiaoguang Lu, Razvan Ionasec, Yefeng Zheng, Dorin Comaniciu
2012 Lecture Notes in Computational Vision and Biomechanics  
Medical image processing tools are playing an increasingly important role in assisting the clinicians in diagnosis, therapy planning and image-guided interventions. Accurate, robust and fast tracking of deformable anatomical objects, such as the heart, is a crucial task in medical image analysis. One of the main challenges is to maintain an anatomically consistent representation of target appearance that is robust enough to cope with inherent changes due to target movement, imaging device
more » ... nt, varying imaging conditions, and is consistent with the domain expert clinical knowledge. To address these challenges, this chapter presents a probabilistic framework that relies on anatomically indexed component-based object models which integrate several sources of information to determine the temporal trajectory of the deformable target. Large annotated imaging databases are exploited to encode the domain knowledge in shape models and motion models and to learn discriminative image classifiers for the target appearance. The framework robustly fuses the prior information with traditional tracking approaches based on template match-
doi:10.1007/978-94-007-5446-1_9 fatcat:faije7sn2bh4nlstwbtsrziiy4