Journal Staff

This book constitutes the refereed proceedings of the 18th Scandinavian Conference on Image Analysis, SCIA 2013, held in Espoo, Finland, in June 2013. The 67 revised full papers presented were carefully reviewed and selected from 132 submissions. The papers are organized in topical sections on feature extraction and segmentation, pattern recognition and machine learning, medical and biomedical image analysis, faces and gestures, object and scene recognition, matching, registration, and alignment, 3D vision, color and multispectral image analysis, motion analysis, systems and applications, human-centered computing, and video and multimedia analysis

Segmentation of nerve bundles and ganglia in spine MRI using particle filters

14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part IIIAutomatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.National Institutes of Health (U.S.) (NAMIC award U54-EB005149)National Science Foundation (U.S.) (CAREER grant 0642971

Rib detection in 3D MRI using dynamic programming based on vesselness and ridgeness

In this paper, a fully automatic method is proposed to detect the ribs in 3D MRI. The purpose of the detection is MR-guided HIFU treatment of liver lesions, in which the ribs should be avoided. Rib segmentations are required for treatment planning and they may also be used for motion tracking during treatment. The rib detection results can serve as an initialization to automatic rib cage segmentation. The algorithm is based on surface detection and dynamic programming. First, the outer surface of the rib cage is detected. Vesselness and ridgeness are computed to highlight elongated structures. The ribs are tracked simultaneously on a 2D projection of the vesselness in the surface, using dynamic programming. Finally, the extracted lines are backprojected into the original 3D volume. Preliminary results of this algorithm are presented on data of five subjects. The results were evaluated by visual inspection of the backprojected lines in 3D. It was checked whether a line belonged to the correct rib and whether it stayed inside this rib. Overall, our algorithm was capable of detecting the ribs that were visible in the images. Testing on five volunteers yielded one failure. The remaining four results were satisfactory. Our method seems suitable to serve as initialization to a full rib cage segmentation in MRI. © 2013 Springer-Verlag