Liver segmentation from computed tomography scans: A survey and a new algorithm

Campadelli, Paola; Casiraghi, Elena; Esposito, A.

doi:10.1016/j.artmed.2008.07.020

Cited by 169 publications

(107 citation statements)

References 28 publications

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“…The marker definition not only requires internal markers (that mark the object of interest) but also external markers that are used to constrain the growth of the regions. In this first slice, an external marker image m ext is obtained as the boundary of the patient's abdomen as previously was proposed in [46].…”

Section: Segmentation Algorithm For Initial Slicementioning

confidence: 99%

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

López-Mir

Naranjo

Angulo

et al. 2014

Computer Methods and Programs in Biomedicine

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There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of .91 ± .02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.

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Section: Segmentation Algorithm For Initial Slicementioning

confidence: 99%

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

López-Mir

Naranjo

Angulo

et al. 2014

Computer Methods and Programs in Biomedicine

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“…The mapping or normalization is performed in a soft tissue window determined by selecting the lower (Lo) and upper (Hi) bounds of the right distribution in the histogram of the raw CT data. This mapping is performed according to (1).…”

Section: Preprocessingmentioning

confidence: 99%

“…In literature, there are many attempts to solve the liver segmentation problem and various approaches have been proposed, including intensity or texture based approaches, deformable and statistical model-based approaches, and probabilistic atlases based approaches. Survey and comparison of different liver segmentation approaches have been presented in [1,2].…”

Section: Introductionmentioning

confidence: 99%

Liver Segmentation Approach Using Graph Cuts and Iteratively Estimated Shape and Intensity Constrains

Afifi

Nakaguchi

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. In this paper, we present a liver segmentation approach. In which, the relation between neighboring slices in CT images is utilized to estimate shape and statistical information of the liver. This information is then integrated with the graph cuts algorithm to segment the liver in each CT slice. This approach does not require prior models construction, and it uses single phase CT images; even so, it is talented to deal with complex shape and intensity variations. Moreover, it eliminates the burdens associated with model construction like data collection, manual segmentation, registration, and landmark correspondence. In contrast, it requires a low user interaction to determine the liver landmarks on a single CT slice only. The proposed approach has been evaluated on 10 CT images with several liver abnormalities, including tumors and cysts, and it achieved high average scores of 81.7 using MICCAI-2007 Grand Challenge scoring system. Compared to contemporary approaches, our approach requires significantly less interaction and processing time.

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“…Some of them are Clustering methods (Li et al, 2008), Histogram-based methods (Tobias and Seara, 2002), Watershed transformation methods (Tarabalka et al, 2010), Graph partitioning methods (Yuan et al, 2009). Some of the practical applications of image segmentation are medical Imaging (Campadelli et al, 2009), study of anatomical structures, location of objects in satellite images (roads, forests, etc.) (Gamanya et al, 2007), and face recognition (Zhao et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Region-based segmentation of 2D and 3D images with tissue-like P systems

Christinal

Díaz-Pernil

Real

2011

Pattern Recognition Letters

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a b s t r a c tMembrane Computing is a biologically inspired computational model. Its devices are called P systems and they perform computations by applying a finite set of rules in a synchronous, maximally parallel way. In this paper, we develop a variant of P-system, called tissue-like P system in order to design in this computational setting, a region-based segmentation algorithm of 2D pixel-based and 3D voxel-based digital images. Concretely, we use 4-adjacency neighborhood relation between pixels in 2D and 6-adjacency neighborhood relation between voxel in 3D for segmenting digital images in a constant number of steps. Finally, specific software is used to check the validity of these systems with some simple examples.

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Liver segmentation from computed tomography scans: A survey and a new algorithm

Cited by 169 publications

References 28 publications

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

Liver Segmentation Approach Using Graph Cuts and Iteratively Estimated Shape and Intensity Constrains

Region-based segmentation of 2D and 3D images with tissue-like P systems

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