Interactive tools for segmenting 2-D and 3-D images are presented. These tools allow a user to quickly revise a segmentation result obtained from an automatic method. A thresholding technique is described that finds a unique threshold value for each homogeneous region in an image. The threshold value is found such that variance in the region is minimized under change in the threshold value. Curve- and surface-fitting methods are described that can accurately represent a region boundary in 2-D or 3-D with a parametric curve or a surface, respectively. A curve or a surface is optimized to minimize the number of control points representing a region with a prescribed accuracy. The optimized curve or surface is then revised by moving its control points interactively. Once a curve or a surface is found to accurately enclose a region of interest, it is quantized to produce the final 2-D region contour or 3-D region surface. These interactive tools can be used to revise unsatisfactory results obtained from any automatic segmentation method.

Figure 1. (a) An MR brain image and (b) a skin lesion image.

Figure 2. (a,c) Automatic and (b,d) manual segmentation results.

Figure 3. (a-c) Approximating curves with Error=2,3,4, sigma=0.02 and (d-f) Error=2,3,4 sigma=0.03.

Figure 4. (a-c) Steps in revising a segmentation result.

Figure 5. More initial segmentation results obtained from the iterative thresholding method.

Figure 6. Final segmentation results after interative revision.

Figure 7. (a) A 3-D brain image and (b) result of iterative thresholding.

Figure 8. (a-c) Approximating surfaces with Error=2,3,4, sigma=0.02 and (d-f) Error=2,3,4 sigma=0.03.

Figure 9. (a) Initial segmentation result. (b) Interactive revision. (c) Final result.

Figure 10. (a) Initial segmentation result. (b) Interactive revision. (c) Final result.

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For more information contact A. Goshtasby (agoshtas@cs.wright.edu).

Last modified: 1/26/99.