Relaxed image foresting transforms for interactive volume image segmentation
Paper i proceeding, 2010

The Image Foresting Transform (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned correct segmentation labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. Here, we propose the relaxed IFT (RIFT). This modified version of the IFT features an additional parameter to control the smoothness of the segmentation boundary. The RIFT yields more intuitive segmentation results in the presence of noise and weak edges, while maintaining a low computational complexity. We show an application of the method to the refinement of manual segmentations of a thoracolumbar muscle in magnetic resonance images. The performed study shows that the refined segmentations are qualitatively similar to the manual segmentations, while intra-user variations are reduced by more than 50%.

Seeded segmentation

Image Foresting Transform

Minimum cost paths

Interactive segmentation


Filip Malmberg

Ingela Nyström

Andrew Mehnert

Chalmers, Signaler och system

Craig Engstrom

Ewert Bengtsson

Proc. SPIE 7623, Medical Imaging 2010: Image Processing

Vol. 7623


Livsvetenskaper och teknik (2010-2018)


Datorseende och robotik (autonoma system)

Medicinsk bildbehandling



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