The iterative image foresting transform and its application to user-steered 3D segmentation

Alexandre Xavier Falcao; Felipe P. G. Bergo

doi:10.1117/12.480303

15 May 2003 The iterative image foresting transform and its application to user-steered 3D segmentation

Alexandre Xavier Falcao, Felipe P. G. Bergo

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480303
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

Segmentation and 3D visualization at interactive speeds are highly desirable for routine use in clinical settings. We circumvent this problem in the framework of the image foresting transform (IFT) - a graph-based approach to the design of image processing operators. In this paper we introduce the iterative image foresting transform (IFT⁺), which computes sequences of IFTs in a differencial way, present the general IFT⁺ algorithm, and instantiate it to be a watershed transform. The IFT⁺-watershed transform is evaluated in the context of interactive segmentation, where the user makes corrections by adding/removing scene regions with mouse clicks. The IFT⁺-watershed requires time proportional to the number of voxels in the modified regions, while the conventional algorithm computes one watershed transform over the entire scene for each iteration. The IFT⁺-watershed is 5.75 times faster than the watershed and considerably reduces from 17.7 to 3.16 seconds the user's waiting time in segmentation with 3D visualization. These results were obtained in an 1.5GHz Pentium-IV PC over 10 MR scenes of the head, requiring 12 to 28 corrections to segment cerebellum, pons-medulla, ventricle, and the rest of the brain, simultaneously. These results indicate that the IFT⁺ is a significant contribution toward interactive segmentation and 3D visualization.

Citation Download Citation

Alexandre Xavier Falcao and Felipe P. G. Bergo "The iterative image foresting transform and its application to user-steered 3D segmentation", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480303

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