Automatic liver segmentation for volume measurement in CT Images

Lim, Seong-Jae; Jeong, Young-Ju; Ho, Yo‐Sung

doi:10.1016/j.jvcir.2005.07.001

Cited by 119 publications

(71 citation statements)

References 8 publications

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“…In most of the works based on gray-level statistics, a threshold is used to generate a binary volume that is later processed by morphological operators in order to separate desired organs. Recent gray-level methods have been presented by Soler et al (Soler et al, 2001), Fujimoto et al (Fujimoto et al, 2002), Liu et al (Liu et al, 2005) and Lim et al (Lim et al, 2004;Lim et al, 2005;Lim et al, 2006). However, the high variability among liver CT images due to the differences of intensity values in different kind of tumors and the different settings regarding contrast media, make difficult the optimal operation of the methods just based on gray-level statistics.…”

Section: State Of the Artmentioning

confidence: 99%

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Fernandez-de-Manuel¹,

Ledesma-Carbayo²,

Jiménez-Carretero³

et al. 2011

Theory and Applications of CT Imaging and Analysis

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Section: State Of the Artmentioning

confidence: 99%

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Fernandez-de-Manuel¹,

Ledesma-Carbayo²,

Jiménez-Carretero³

et al. 2011

Theory and Applications of CT Imaging and Analysis

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“…Although these correction steps are always successful in removing the unwanted parts, they might also remove some liver voxels; to recover these regions we apply a refinement process. In the literature [9,17], this is usually done by complex techniques (eg. : snakes, level set methods), that might take much computational time and need a cost function to be defined; besides they are applied separately to each slice, neglecting the 3D relationships among neighboring slices.…”

Section: The Terms In E(l) Are Simply Defined As: E1(l(i)) = |G(i) − mentioning

confidence: 99%

“…gray level based techniques, learning techniques, model fitting techniques, probabilistic atlases, and level set) this problem is still open. Indeed, although the gray level based techniques proposed so far [11,12,5,13,14,15,16,17] obtain the most promising results, they are not robust to database variations; this is because their basic step of organ gray level estimation does not take into account the high variability observed both in the same and in different CT volumes. For this reason, when tested on larger and complex data sets, these methods' performance could decrease significantly.…”

Section: Introductionmentioning

confidence: 99%

Automatic Abdominal Organ Segmentation from CT images

Casiraghi

Campadelli

Pratissoli

et al. 2009

ELCVIA

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In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. Some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies and the 3D volume rendering of the abdominal organs. The first and fundamental step in all these studies is the automatic organs segmentation, that is still an open problem. In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart, bones (i.e. ribs and spine), liver and its blood vessels, kidneys, and spleen. The overall system has been evaluated on the data of 100 patients, obtaining a good assessment both by visual inspection by three experts, and by comparing the computed results to the boundaries manually traced by experts.

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“…Liver segmentation is the first step to calculate objective measurements and liver/lesion ratios for decisions regarding treatment and planning for the patient. The segmentation of internal organs is also essential for image-guided surgery and virtual reality scenarios for medical training [2,3,4,5,6]. In addition, the liver segmentation can help in hepatic steatosis quantification because the results of this segmentation can be correlated to measure fat fractions [7].…”

Section: Introductionmentioning

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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Automatic liver segmentation for volume measurement in CT Images

Cited by 119 publications

References 8 publications

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

Automatic Abdominal Organ Segmentation from CT images

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

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