Fully Automatic Liver Segmentation through Graph-Cut Technique

Massoptier, Laurent; Casciaro, Sergio

doi:10.1109/iembs.2007.4353524

Cited by 59 publications

(43 citation statements)

References 18 publications

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“…This ratio-pair based technique resulted in the accurate selection of large samples of hepatic parenchyma for analysis. A variety of liver segmentation techniques have been investigated, which typically attempt to extract the entire liver volume from an image data set (20)(21)(22)(23)(24)(25). These use a number of techniques, including deformable models, statistical/probabilistic techniques, atlas-guided methods, and basic image processing/thresholding, among others; ultimately, simple threshold-and model-based techniques predominate (26).…”

Section: Discussionmentioning

confidence: 99%

Automated liver sampling using a gradient dual‐echo Dixon‐based technique

Bashir

Dale

Merkle

et al. 2011

Magnetic Resonance in Med

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Magnetic resonance spectroscopy of the liver requires input from a physicist or physician at the time of acquisition to insure proper voxel selection, while in multiecho chemical shift imaging, numerous regions of interest must be manually selected in order to ensure analysis of a representative portion of the liver parenchyma. A fully automated technique could improve workflow by selecting representative portions of the liver prior to human analysis. Complete volumes from three-dimensional gradient dual-echo acquisitions with twopoint Dixon reconstruction acquired at 1.5 and 3 T were analyzed in 100 subjects, using an automated liver sampling algorithm, based on ratio pairs calculated from signal intensity image data as fat-only/water-only and log(in-phase/opposedphase) on a voxel-by-voxel basis. Using different gridding variations of the algorithm, the average correct liver volume samples ranged from 527 to 733 mL. The average percentage of sample located within the liver ranged from 95.4 to 97.1%, whereas the average incorrect volume selected was 16.5-35.4 mL (2.9-4.6%). Average run time was 19.7-79.0 s. The algorithm consistently selected large samples of the hepatic parenchyma with small amounts of erroneous extrahepatic sampling, and run times were feasible for execution on an MRI system console during exam acquisition.

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Section: Discussionmentioning

confidence: 99%

Automated liver sampling using a gradient dual‐echo Dixon‐based technique

Bashir

Dale

Merkle

et al. 2011

Magnetic Resonance in Med

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“…Adaptive Thresholding [3] is additionally called local or dynamic thresholding. The principal idea of adaptive threshold is to apply different threshold on the different region of the image.…”

Section: E Adaptive Thresholdingmentioning

confidence: 99%

“…GC is not iterative method Graph cut is functioning admirably in homogenous area. Graph cut [3] can be made fully automatic www.ijacsa.thesai.org using different algorithms. In case of liver tumor segmentation general active contour come up short when tumor is near liver surface, graph cut handle this kind of active contour issues extremely well.…”

Section: Graph Cutmentioning

confidence: 99%

Overview of Technical Elements of Liver Segmentation

Khan¹,

Ahmed²,

Kiran³

et al. 2016

ijacsa

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Abstract-Liver diseases are life-threatening, it's important to detect it tumor in early stages. So, for tumor detection Segmentation of the liver is a first and significant stride. Segmentation of the liver is a yet difficult undertaking in view of its intra patient variability in intensity, shape and size of the liver. The aim of this paper is to assemble a wide assortment of techniques and used CT scan dataset information for liver segmentation that will provide a decent beginning to the new researcher. There are different strategies from basic to advance like thresholding, active contour, region growing to graph cut is briefly abridge to give an outline of existing segmentation strategies. We review the concept of particular strategies and review their original ideas. Our idea is to provide information under which condition a chosen strategy will work or utilize.

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“…Consequently, in comparison with CT-based liver segmentation approaches [7,, there are fewer studies for liver image segmentation from MR datasets. The present approaches for MR-based image segmentation in the literature can be listed as fuzzy c-means classification [32,33], graph-cut [34], snakes [35], the level set method [36,37], the synchronized oscillator network [38], the active shape model [39,40], watershed [41], iterative quadtree decomposition [42], the Gaussian model and Markov random field [43], modified region growing [44], and the application of free-form registration on manually segmented CT images [45]. At present, it is clear that there is no method capable of simultaneously solving all of the problems of different modality characteristics, atypical liver shapes, and similar gray values with adjacent tissues.…”

Section: Introductionmentioning

confidence: 99%

“…Although these hybrid methods were shown to be effective in segmenting large objects, because of imaging modality differences and the notion of individual organ segmentation in our case using a specific MR sequence for the liver, a full evaluation and comparison to the methods in this work is outside the scope of their paper. In the present literature, most methods developed for automatic liver segmentation from MR images have either over-or undersegmentation or leakage problems [42][43][44], are tested with only a few datasets [34,44], or have complex calculations such as active contour-based approaches [45]. In [41], the watershed transformation and neural networks are used for liver detection without identifying the modality characteristics of the MR images used.…”

Section: Introductionmentioning

confidence: 99%

A comparative performance evaluation of various approaches for liver segmentation from SPIR images

Göçeri¹,

Unlu²,

Dıcle³

2015

Turk J Elec Eng & Comp Sci

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Developing a robust method for liver segmentation from magnetic resonance images is a challenging task because of the similar intensity values between adjacent organs, the geometrically complex liver structure, and injection of contrast media. Most importantly, a high anatomical variability of a healthy or diseased liver is a major challenge in defining the exact boundaries of the liver. Several artifacts of pulsation, motion, and partial volume effects are also among the variety of factors that make automatic liver segmentation difficult. In this paper, we present an overview of liver segmentation methods in magnetic resonance images and show comparative results of seven different pseudo-3D liver segmentation approaches chosen from deterministic (K-means-based), probabilistic (Gaussian model-based), supervised neural network (multilayer perceptron-based), and deformable model-based (level set) segmentation methods. The results of quantitative and qualitative analyses using sensitivity, specificity, and accuracy metrics show that the multilayer perceptron-based approach and a level set-based approach, both of which use distance regularization terms and signed pressure force function, are the most successful methods for liver segmentation from spectral presaturation inversion recovery (SPIR) images. However, the multilayer perceptron-based segmentation method has a higher computational cost. The automatic method using the distance regularized level set evolution with signed pressure force function avoids the sensitivity of a user-defined initial contour for each slice, gives the most efficient results for liver segmentation after the preprocessing steps, and also requires less computational time.

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Fully Automatic Liver Segmentation through Graph-Cut Technique

Cited by 59 publications

References 18 publications

Automated liver sampling using a gradient dual‐echo Dixon‐based technique

Automated liver sampling using a gradient dual‐echo Dixon‐based technique

Overview of Technical Elements of Liver Segmentation

A comparative performance evaluation of various approaches for liver segmentation from SPIR images

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