Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Contour Continuity in Region Based Image Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Proceedings of the 30th DAGM symposium on Pattern Recognition
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Cell tracking and segmentation in electron microscopy images using graph cuts
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
An energy minimization approach to the data driven editing of presegmented images/volumes
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Hi-index | 0.00 |
There are various automated segmentation algorithms for medical images. However, 100% accuracy may be hard to achieve because medical images usually have low contrast and high noise content. These segmentation errors may require manual correction. In this paper, we present an interactive editing framework that allows the user to quickly correct segmentation errors produced by automated segmentation algorithms. The framework includes two editing methods: (1) editing through multiple choice and (2) interactive editing through graph cuts. The first method provides a set of alternative segmentations generated from a confidence map that carries valuable information from multiple cues, such as the probability of a boundary, an intervening contour cue, and a soft segmentation by a random walker. The user can then choose the most acceptable one from those segmentation alternatives. The second method introduces an interactive editing tool where the user can interactively connect or disconnect presegmented regions. The editing task is posed as an energy minimization problem: We incorporate a set of constraints into the energy function and obtain an optimal solution via graph cuts. The results show that the proposed editing framework provides a promising solution for the efficient correction of incorrect segmentation results.