Survey on segmentation of liver from CT images

Priyadarsini, Subhashree; Selvathi, D.

doi:10.1109/icaccct.2012.6320777

Cited by 27 publications

(26 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past, various techniques for liver segmentation have been proposed. 28 A comprehensive survey on liver CT image segmentation methods is given by Mharib et al, 3 reviewing semiautomatic and automatic liver segmentation, experimenting on several methods (i.e., gray level based techniques, learning techniques, model fitting techniques and probabilistic atlases). As noted by Mharib, 3 the liver segmentation task however still relies greatly on heavy manual intervention and heavy parameter tuning.…”

Section: A Liver Segmentationmentioning

confidence: 99%

3D-SIFT-Flow for atlas-based CT liver image segmentation

Xiao

et al. 2016

Med. Phys.

View full text Add to dashboard Cite

show abstract

Section: A Liver Segmentationmentioning

confidence: 99%

3D-SIFT-Flow for atlas-based CT liver image segmentation

Xiao

et al. 2016

Med. Phys.

View full text Add to dashboard Cite

show abstract

“…Although there is increasing awareness about the health risks of radiation and iodinated contrast agents related to computed tomography (CT) imaging , most of the liver segmentation studies in the literature are from CT images . There are only a few studies for liver segmentation from magnetic resonance (MR) images .…”

Section: Introductionmentioning

confidence: 99%

“…All methods applied for liver segmentation in the literature have their own weaknesses and drawbacks according to the conclusions of review works . Except the reviewed liver segmentation methods, there is a novel study that uses both registration and graph‐cut techniques for liver segmentation from CT data sets.…”

Section: Introductionmentioning

confidence: 99%

Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Göçeri

2016

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Quantitative analysis and precise measurements on the liver have vital importance for pre-evaluation of surgical operations and require high accuracy in liver segmentation from all slices in a data set. However, automated liver segmentation from medical image data sets is more challenging than segmentation of any other organ due to various reasons such as vascular structures in the liver, high variability of liver shapes, similar intensity values, and unclear edges between liver and its adjacent organs. In this study, a variational level set-based segmentation approach is proposed to be efficient in terms of processing time and accuracy. The efficiency of this method is achieved by (1) automated initialization of a large initial contour, (2) using an adaptive signed pressure force function, and also (3) evolution of the level set with Sobolev gradient. Experimental results show that the proposed fully automated segmentation technique avoids local minima and stops evolution of the active contour at desired liver boundaries with high speed and accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

“…There exist many methods of liver segmentation including region growing, active contour, level set, graph cuts, clustering and threshold based methods, deformable model, statistic shape model; support vector machine (SVM) based, neural network (NN) based, etc. Some comprehensive reviews have been done on liver segmentation [1][2][3][4]. It is noticed that a particular categorising methodology is adopted to well address an emphasis in these reviews.…”

Section: Introductionmentioning

confidence: 99%

“…It is noticed that a particular categorising methodology is adopted to well address an emphasis in these reviews. For example, [5] reviews the machine learning techniques for automatic segmentation of liver images, where the techniques are classified as NN based, support vector machine-based, clustering based, and hybrid technique; [1] surveys segmentation of liver from CT images, where the techniques are classified as region based, threshold based, level set, model based, active contour based, histogram based, gray level based, and clustering based.…”

Section: Introductionmentioning

confidence: 99%

Review on the Methods of Automatic Liver Segmentation from Abdominal Images

Luo¹,

Li²,

Li³

2014

JCC

View full text Add to dashboard Cite

Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation.We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed together to achieve better segmentation performance.

show abstract

Survey on segmentation of liver from CT images

Cited by 27 publications

References 13 publications

3D-SIFT-Flow for atlas-based CT liver image segmentation

3D-SIFT-Flow for atlas-based CT liver image segmentation

Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Review on the Methods of Automatic Liver Segmentation from Abdominal Images

Contact Info

Product

Resources

About