Interactive volume segmentation with differential image foresting transforms

The absence of object information very often asks for considerable human assistance in medical image segmentation. Many interactive two-dimensional and three-dimensional (3-D) segmentation methods have been proposed, but their response time to user's actions should be considerably reduced to make them viable from the practical point of view. We circumvent this problem in the framework of the image foresting transform (IFT)-a general tool for the design of image operators based on connectivity-by introducing a new algorithm (DIFT) to compute sequences of IFTs in a differential way. We instantiate the DIFT algorithm for watershed-based and fuzzy-connected segmentations under two paradigms (single-object and multiple-object) and evaluate the efficiency gains of both approaches with respect to their linear-time implementation based on the nondifferential IFT. We show that the DIFT algorithm provides efficiency gains from 10 to 17, reducing the user's waiting time for segmentation with 3-D visualization on a common PC from 19-36 s to 2-3 s. We also show that the multiple-object approach is more efficient than the single-object paradigm for both segmentation methods.

[1]  Luciano da Fontoura Costa,et al.  Erratum to multiscale skeletons by image foresting transform and its applications to neuromorphometry: [Pattern Recognition 35(7) (2002) 1571-1582] , 2003, Pattern Recognit..

[2]  Jayaram K. Udupa,et al.  Artery-vein separation via MRA-An image processing approach , 2001, IEEE Transactions on Medical Imaging.

[3]  Heinz-Otto Peitgen,et al.  Efficient Semiautomatic Segmentation of 3D Objects in Medical Images , 2000, MICCAI.

[4]  W. A. Hanson,et al.  Interactive 3D segmentation of MRI and CT volumes using morphological operations. , 1992, Journal of computer assisted tomography.

[5]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004 .

[6]  Jayaram K. Udupa,et al.  Fuzzy-connected 3D image segmentation at interactive speeds , 2000, Graph. Model..

[7]  John M. Gauch,et al.  Image segmentation and analysis via multiscale gradient watershed hierarchies , 1999, IEEE Trans. Image Process..

[8]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[9]  Alexandre X. Falcão,et al.  Interactive 3D Segmentation of Brain MRI with Differential Watersheds , 2003 .

[10]  Reinhard Männer,et al.  Interactive Segmentation and Visualization of Volume Data Sets , 1997 .

[11]  Jayaram K. Udupa,et al.  Relative Fuzzy Connectedness among Multiple Objects: Theory, Algorithms, and Applications in Image Segmentation , 2001, Comput. Vis. Image Underst..

[12]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[13]  Alexandre X. Falcão,et al.  The iterative image foresting transform and its application to user-steered 3D segmentation , 2003, SPIE Medical Imaging.

[14]  R. S. Kahn,et al.  Automatic Segmentation of the Ventricular System from MR Images of the Human Brain , 2001, NeuroImage.

[15]  Jayaram K. Udupa,et al.  Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation , 2000, Comput. Vis. Image Underst..

[16]  Jayaram K. Udupa,et al.  A 3D generalization of user-steered live-wire segmentation , 2000, Medical Image Anal..

[17]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[19]  Alexandre X. Falcão,et al.  Design of connected operators using the image foresting transform , 2001, SPIE Medical Imaging.

[20]  Alexandre X. Falcão,et al.  IFT-Watershed from gray-scale marker , 2002, Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing.

[21]  Jayaram K. Udupa,et al.  User-Steered Image Segmentation Paradigms: Live Wire and Live Lane , 1998, Graph. Model. Image Process..

[22]  W E Higgins,et al.  Interactive morphological watershed analysis for 3D medical images. , 1993, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[23]  O Musse,et al.  Three-dimensional segmentation of anatomical structures in MR images on large data bases. , 2001, Magnetic resonance imaging.

[24]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[25]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..

[26]  Nicholas Ayache,et al.  Medical Image Analysis: Progress over Two Decades and the Challenges Ahead , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Alexandre X. Falcão,et al.  The Ordered Queue and the Optimality of the Watershed Approaches , 2000, ISMM.

[28]  Rainer Wegenkittl,et al.  Implementation and Complexity of the Watershed‐from‐Markers Algorithm Computed as a Minimal Cost Forest , 2001, Comput. Graph. Forum.

[29]  Luciano da Fontoura Costa,et al.  A graph-based approach for multiscale shape analysis , 2004, Pattern Recognit..

[30]  Jayaram K. Udupa,et al.  An ultra-fast user-steered image segmentation paradigm: live wire on the fly , 2000, IEEE Transactions on Medical Imaging.