Review on the Methods of Automatic Liver Segmentation from Abdominal Images

Luo, Siwei; Li, Xuechen; Li, Jiaming

doi:10.4236/jcc.2014.22001

Cited by 66 publications

(28 citation statements)

References 36 publications

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“…There are multiple algorithms and software packages capable of segmenting lung tissue, the liver, the aorta, the spine, and multiple organs . However, these algorithms can be more computationally expensive, less robust, and not fully automated . Our fully automated algorithm is written in MATLAB and runs for less than five minutes for a typical dataset on a 2.7 GHz Core i5 machine.…”

Section: Discussionmentioning

confidence: 99%

Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images

2017

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Patient-specific organ HUs can be measured from clinical datasets. The algorithm that was developed can be run on both contrast-enhanced and non-contrast-enhanced clinical datasets. The method can be applied to automatically extract image HU-contrast characteristics of clinical CT images, not captured in phantom data, whereby enabling quantification and optimization of image quality and contrast administration.

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Section: Discussionmentioning

confidence: 99%

Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images

2017

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“…The liver also detoxifies chemicals and metabolizes drugs and also makes proteins that are important for blood clotting and other functions. There is a research that conduct the review method for liver segmentation method in 2014 [3]. The research reviews the segmentation method for liver from the abdominal images.…”

Section: Livermentioning

confidence: 99%

Review methods for image segmentation from computed tomography images

Mamat

Rahman

Soh

et al. 2014

AIP Conference Proceedings

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Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.

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“…However, there is no global technique that can be used with all kinds of images; each technique has its own strengths and weaknesses. Interested readers can refer to [2], [12], [13] for a thorough survey of liver segmentation techniques. Also, quantitative segmentation validation methods are considered among the open issues in the literature.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images

Mohamed

Seada

Hamdy

et al. 2017

IJCA

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Despite the importance of the liver segmentation in the medical images for efficient noninvasive diagnosis, few studies found in the literatures for fully automated methods for liver segmentation in Magnetic Resonance Imaging (MRI) compared to that in Computed Tomography (CT) scans. Motivated by this, we propose an adaptive fully automatic liver segmentation method for MRI images based on thresholding and Bayesian classification. Bayesian classifications have proved to be highly robust to various image degradations. It only requires a small amount of training data to estimate the parameters necessary for classification, which is a huge advantage in medical applications. Furthermore, the Bayesian model is robust when large uncertainties are involved in medical image analysis problems. The proposed method is successfully tested on many MRI cases acquired from different patients, in various sizes. Experiments proved the robustness of the proposed liver automatic segmentation process even on data from different scanner types. The segmentation accuracy of the model has a mean Dice Similarity Coefficient (DSC) of 95.5% for MRI datasets.

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Review on the Methods of Automatic Liver Segmentation from Abdominal Images

Cited by 66 publications

References 36 publications

Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images

Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images

Review methods for image segmentation from computed tomography images

An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images

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