• Corpus ID: 8271216

An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut

@inproceedings{Zhu2014AnEI,
  title={An Effective Interactive Medical Image Segmentation Method Using Fast GrowCut},
  author={Linagjia Zhu and Ivan Kolesov and Yi Gao and Ron Kikinis and Allen R. Tannenbaum},
  year={2014}
}
Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. In this paper, we present an effective interactive segmentation method that reformulates the GrowCut algorithm as a clustering problem and computes a fast, approximate solution. The method is further improved by using an efficient updating scheme requiring only local… 
interactivemedical.org
62 Citations
Highly Influential Citations
11
Background Citations
9
Methods Citations
31

Figures and Tables from this paper

62 Citations

Efficient Interactive Multi-object Segmentation in Medical Images
TLDR
An efficient method is proposed for the multiple object segmentation of medical images by constructing an associated weighted digraph of superpixels, attending its individual high-level priors and considering structural relations of inclusion and exclusion.
User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP
TLDR
The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRats reference manual segmentation, than the second-best performer in theBRATS challenge.
Automated Growcut for segmentation of endoscopic images
TLDR
A new segmentation algorithm based on GrowCut is proposed and apply the algorithm for CE images containing bleeding and the suitability of the proposed method for automated segmentation and annotation of CE images is justified.
Segmentation of GBM in MRI images using an efficient speed function based on level set method
TLDR
The proposed combined method improves the accuracy of GBM segmentation by 16% compared to conventional level set method with threshold based speed function and is also robust to change of parameters.
BGrowth: an efficient approach for the segmentation of vertebral compression fractures in magnetic resonance imaging
TLDR
This paper presents a semi-automatic segmentation method, called Balanced Growth (BGrowth), which has achieved an accuracy up to 95% while keeping acceptable processing time performance, that is equivalent to the state-of-the-art methods.
Automatic Breast Ultrasound Image Segmentation: A Survey
3DBGrowth: Volumetric Vertebrae Segmentation and Reconstruction in Magnetic Resonance Imaging
TLDR
3DBGrowth is proposed, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images and works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist.
Interactive segmentation in MRI for orthopedic surgery planning: bone tissue
TLDR
An interactive segmentation framework that allows iterative corrections leading to substantial speed gains in bone annotation in MRI will allow semi-automatic segmentations of other musculoskeletal anatomy first in a user-in-the-loop manner, where later less user interactions or perhaps only few for quality control will be necessary as the authors' annotation suggestions improve.
Renal compartment segmentation in DCE-MRI images
Polymorph segmentation representation for medical image computing
...
...

References

SHOWING 1-8 OF 8 REFERENCES
Interactive Medical Image Segmentation using PDE Control of Active Contours
TLDR
In this paper, the goal of easy-to-use and consistent interactive segmentation is transformed into a control synthesis problem and an open-loop system achieves correct segmentation under ideal conditions, but does not agree with a human expert's ideal boundary for real image data.
Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications
TLDR
A cellular automata based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance images, which standardizes the volume of interest (VOI) and seed selection, and an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response.
“GrowCut”-Interactive Multi-Label N-D Image Segmentation By Cellular Automata
TLDR
A novel algorithm for interactive multilabel segmentation of N-dimensional images given a small number of user-labelled pixels, the rest of the image is segmented automatically by a Cellular Automaton.
Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images
TLDR
A new technique for general purpose interactive segmentation of N-dimensional images where the user marks certain pixels as "object" or "background" to provide hard constraints for segmentation.
Interactive Segmentation with Intelligent Scissors
TLDR
Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse, using live-wire boundary detection as an optimal path search in a weighted graph.
Computer Vision, Pattern Recognition and Image Processing in Left Ventricle Segmentation: The Last 50 Years
  • J. Suri
  • Computer Science
    Pattern Analysis & Applications
  • 2000
TLDR
Any one pattern recognition or computer vision technique is not sufficient for accurate 2D, 3D or 4D modelling of LV and not much attention has been paid to LC modelling validation.
A note on two problems in connexion with graphs
  • E. Dijkstra
  • Mathematics, Computer Science
    Numerische Mathematik
  • 1959
TLDR
A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
Fibonacci Heaps and Their Uses in Improved Network Optimization Algorithms
TLDR
Using F-heaps, a new data structure for implementing heaps that extends the binomial queues proposed by Vuillemin and studied further by Brown, the improved bound for minimum spanning trees is the most striking.