We present a new, interactive tool called Intelligent Scissors which we use for image segmentation and composition. Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary "snaps" to, and wraps around the object of interest.Live-wire boundary detection formulates discrete dynamic programming (DP) as a two-dimensional graph searching problem. DP provides mathematically optimal boundaries while greatly reducing sensitivity to local noise or other intervening structures. Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neighborhood. Boundary cooling automatically freezes unchanging segments and automates input of additional seed points. Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted.Extracted objects can be scaled, rotated, and composited using live-wire masks and spatial frequency equivalencing. Frequency equivalencing is performed by applying a Butterworth filter which matches the lowest frequency spectra to all other image components. Intelligent Scissors allow creation of convincing compositions from existing images while dramatically increasing the speed and precision with which objects can be extracted.

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An adaptive boundary detection algorithm that uses two-dimensional dynamic programming is presented. The algorithm is less constrained than previous onedimensional dynamic programming algorithms and allows the user to interactively determine the mathematically optimal boundary between a user-selected seed point cad any other dynamically selected 'Ifree" point in the image. Interactive movement of the free point by the cursor causes the boundary to behave like a "live wire" as it adapts to the new minimum cost path between the seed point and the currently selectedfree point. The algorithm can also be adapted or customized to learn boundarydefining features for a particular class of images. Adaptive 2 0 DP performs well on a variety of images (angiocardiograms, CT, MRI). In particular, it accurately detects the boundaries of low contrast objects, such as occur with intravenous injections, as well as those found in noisy, low SNR images.