@article{chen_krim_mendoza_2010, title={Multiphase Joint Segmentation-Registration and Object Tracking for Layered Images}, volume={19}, ISSN={["1941-0042"]}, DOI={10.1109/tip.2010.2045164}, abstractNote={In this paper we propose to jointly segment and register objects of interest in layered images. Layered imaging refers to imageries taken from different perspectives and possibly by different sensors. Registration and segmentation are therefore the two main tasks which contribute to the bottom level, data alignment, of the multisensor data fusion hierarchical structures. Most exploitations of two layered images assumed that scanners are at very high altitudes and that only one transformation ties the two images. Our data are however taken at mid-range and therefore requires segmentation to assist us examining different object regions in a divide-and-conquer fashion. Our approach is a combination of multiphase active contour method with a joint segmentation-registration technique (which we called MPJSR) carried out in a local moving window prior to a global optimization. To further address layered video sequences and tracking objects in frames, we propose a simple adaptation of optical flow calculations along the active contours in a pair of layered image sequences. The experimental results show that the whole integrated algorithm is able to delineate the objects of interest, align them for a pair of layered frames and keep track of the objects over time.}, number={7}, journal={IEEE TRANSACTIONS ON IMAGE PROCESSING}, author={Chen, Ping-Feng and Krim, Hamid and Mendoza, Olga L.}, year={2010}, month={Jul}, pages={1706–1719} }
 @inproceedings{chen_steen_yezzi_krim_2009, title={brain MRi T-1-map and T-1-weighted image segmentation in a variational framework}, DOI={10.1109/icassp.2009.4959609}, abstractNote={In this paper we propose a constrained version of Mumford-Shah's[1] segmentationwith an information-theoretic point of view[2] in order to devise a systematic procedure to segment brain MRI data for two modalities of parametric T1-Map and T1-weighted images in both 2-D and 3-D settings. The incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the three-tissue segmentation possible. Our method uses region based active contours which have proven to be robust. The method is validated by two real objects which were used to generate T1-Maps and also by two simulated brains of T1-weighted data from the BrainWeb[3] public database.}, booktitle={International conference on acoustics speech and signal processing}, author={Chen, P. F. and Steen, R. G. and Yezzi, A. and Krim, H.}, year={2009}, pages={417–420} }