Motion Estimation and Classification in Medical Image Analysis

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Accurate motion estimation and classification is crucial in many medical imaging applications as it serves as the basis for the diagnosis and treatment option. Most computer vision and image processing approaches overlook the motion estimation aspect of temporal data, and they primarily focused on independently analyzing the images in a sequence. This leads to significant problems in adapting these methodologies to practical problems as the lack of temporal consistency render these methods ineffective for many real-life applications. Although integration of prior knowledge has been shown to significantly improve the motion estimation accuracy in radar signal processing, their application has not been studied well for medical imaging. Additionally, the mathematical models derived in radar signal processing for estimating the motions of objects such as aircraft deviate significantly from the tissue motions arising in medical imaging applications. New models have to be derived using fundamental principles to characterize movements that occur in medical imaging applications accurately. Recently, several deep convolutional neural network-based machine learning algorithms have been proposed to address the medical image segmentation problem. However, these methods do not provide an option to achieve temporally consistent segmentation. Additionally, these methods often require a significant amount of training data.  Over the next five years, I plan to continue working on the exciting area of analyzing temporal medical image sequences and intend to develop novel solutions to motion estimation and classification problems. The overall aim of the proposed research program is to develop novel, automated, and efficient (realtime) algorithms for the estimation and classification of tissue motion from medical image scans. Specific objectives include: i) Theoretical objectives: Novel tracking approaches that are applicable under more general circumstances will be developed. Additionally, advanced classification approaches will be developed to discriminate between different types of tissue or object motions. Such developments will include automated classification of normal, hypokinetic, akinetic and dyskinetic motions in regional cardiac function. The overall purpose is to develop algorithms that can identify subtle differences in motion patterns thereby allowing computer-assisted diagnosis algorithms to detect abnormalities as early as possible. ii) Practical objectives: In practice, I plan to develop mathematical models to characterize tissue motion for essential and challenging problems, e.g., estimating and classifying motion patterns for the cardiac left ventricle. The proposed research program intends to train students and produce young scientists with unique expertise in machine learning and image processing to enable them to be world-class experts in the fields of computer vision and medical image analysis.