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Erschienen in:
An Approach for Estimation of Distance Information Between Two Persons from Single 2D Image Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Review on Recent Methods for Segmentation of Liver Using Computed Tomography and Magnetic Resonance Imaging Modalities

verfasst von : T. M. Geethanjali, Minavathi

Erschienen in: Emerging Research in Electronics, Computer Science and Technology

Verlag: Springer Singapore

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Abstract

The span of modern medical imaging provides new and efficient techniques for segmentation of liver that are used by the clinicians to view in order to diagnose, monitor and treat liver diseases. Liver cancer is one of the most prominent diseases which cause death. Extraction of liver in different modalities is a difficult task because of its varying shape, similarity between organ intensities and variability in liver region intensities. In this review paper, a study has been carried out on liver segmentation in CT and MRI images with different methodologies and datasets. The observation has been made to highlight the merits, demerits and performance metrics of different works published.

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Vorheriges Kapitel A Literature Review on Energy Harvesting for Internet of Things Applications
Nächstes Kapitel A Survey: On Network Forensic Data Acquisition and Analysis Tools
Literatur
1.
Skandalakis JE, Skandalakis LJ, Skandalakis PN, Mirilas P (2004) Hepatic surgical anatomy. Surg Clin North Am 84:413–435CrossRef
2.
Lu D, Wu Y, Harris G, Cai W (2015) Iterative mesh transformation for 3D segmentation of livers with cancers in CT images. Comput Med Imaging Graph 43:1–14
3.
Abdelwaha R, Abdallah Y, Hayder A, Wagiallah E (2014) Application of texture analysis algorithm for data extraction in dental X-ray images. Int J Sci Res (IJSR) 3(10):1934–1939
4.
Ecabert O et al (2008) Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imag 27(9):1189–1201CrossRef
5.
Barstugan M, Ceylan R, Sivri M, Erdogan H (2018) Automatic liver segmentation in abdomen CT images using SLIC and AdaBoost algorithms. ICBBB, Tokyo, Japan, 18–20 Jan 2018
6.
Liao M et al (2017) Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Comput Methods Programs Biomed
7.
Jin X, Ye H, Li L, Xia Q (2017) Image segmentation of liver CT based on fully convolutional network. In: 10th international symposium on computational intelligence and design
8.
Zhang Y, Zhiqiang H, Zhong C, Zhang Y, Shi Z (2017) Fully convolutional neural network with post-processing methods for automatic liver segmentation from CT. IEEE
9.
Li X, Chen H, Qi X, Dou Q, Fu C-W, Heng P-A (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. arXiv:​1709.​07330v2 [cs.CV]. Accessed 22 Nov 2017
10.
11.
Zheng S, Fang B, Li L, Gao M, Zhang H, Chen H, Wang Y (2017) A novel variational method for liver segmentation based on statistical shape model prior and enforced local statistical feature. IEEE
12.
Farzaneh N, Habbo-Gavin S, Reza Soroushmehr SM, Patel H, Fessell DP, Ward KR, Najarian K (2017) Atlas based 3D liver segmentation using adaptive thresholding and superpixel approaches. In: ICASSP 2017
13.
Saito K, Lu H, Tan JK, Kim H, Yamamoto A, Kido S, Tanabe M (2017) Automatic liver segmentation from multiphase CT images by using level set method. In: 17th international conference on control, automation and systems (ICCAS)
14.
Christ PF et al (2017) Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv:​1702.​05970v2 [cs.CV]
15.
16.
Mohamed RG, Seada NA, Hamdy S, Mostafa GM (2017) Automatic liver segmentation from Abdominal MRI images using active contours. Int J Comput Appl 176(1), (0975-8887)
17.
Chartrand G, Cresson T, Chav R, Gotra A, Tang A, De Guise JA (2016) Liver segmentation on CT and MR using Laplacian mesh optimization. Trans Biomed Eng
18.
Bereciartua A, Picon A, Galdran A, Iriondo P (2016) 3D active surfaces for liver segmentation in multisequence MRI images. In: Computer methods and programs in biomedicine. Elsevier
19.
20.
21.
22.
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241
23.
Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H (2016) Fully convolutional network for liver segmentation and lesions detection. In: International workshop on large-scale annotation of biomedical data and expert label synthesis. Springer, pp 77–85
24.
Christ PF, Elshaer MEA, Ettlinger F et al (2016) Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 415–423
25.
Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: International conference on artificial intelligence and statistics
26.
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:​1502.​03167
27.
Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition
28.
Farzaneh N, Samavi S, Soroushmehr SMR, Patel H, Habbo-Gavin S, Fessell D, Ward K, Najarian K (2016) Liver segmentation using location and intensity probabilistic atlases. In: International conference of the IEEE engineering in medicine and biology society (EMBC). IEEE
29.
Chan TF, Vese LA (1999) An active contour model without edges. Lecture notes in computer science, vol 1682, pp 141–151
30.
Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Remper M, Armbruster M, Hofmann F, D’Anastasi M, Sommer WH, Ahmadi S-A, Menze BH (2016) Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. MICCAI, ChamCrossRef
31.
Long J, Shelhamer E (2015) Darrell T Fully convolutional networks for semantic segmentation. In: CVPR
32.
Krähenbühl P, Koltun V (2011) Efficient inference in fully connected CRFs with gaussian edge potentials. In: Advances in neural information processing systems, pp 109–117
33.
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vision 1(4):321–331CrossRef
34.
Nealen A, Igarashi T et al (2006) Laplacian mesh optimization. In: Proceedings of the 4th international conference on computer graphics and interactive techniques in Australasia and Southeast Asia—GRAPHITE ’06. ACM Press, New York, USA, p 381
35.
36.
37.
38.
Chan TF, Vese LA (2001) Active contour without edges. IEEE Trans Image Process 10(2):266–277CrossRef
Metadaten
Titel
Review on Recent Methods for Segmentation of Liver Using Computed Tomography and Magnetic Resonance Imaging Modalities
verfasst von
T. M. Geethanjali
Minavathi
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Emerging Research in Electronics, Computer Science and Technology

Print ISBN: 978-981-13-5801-2

Electronic ISBN: 978-981-13-5802-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-13-5802-9_56

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