Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing

A. Baâzaoui; W. Barhoumi; A. Ahmed; E. Zagrouba

doi:10.1016/j.irbm.2017.02.003

Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing

A. Baâzaoui, W. Barhoumi, A. Ahmed, E. Zagrouba

Computer Science

Research output: Contribution to journal › Article (journal) › peer-review

54 Citations (Scopus)

Abstract

The liver CT image segmentation is still until now a challenging problem due to the fuzzy nature of the tumor transition to the surrounding tissues. The objective of this article is the consideration of the uncertainty present around the boundaries of a tumor region in the segmentation of the liver CT images as well as the segmentation of multiple tumors in the same CT image.

Original language	English
Pages (from-to)	98-108
Journal	IRBM
Volume	38
Issue number	2
DOIs	https://doi.org/10.1016/j.irbm.2017.02.003
Publication status	Published - 1 Apr 2017

Research Centres

Centre for Intelligent Visual Computing Research

Access to Document

10.1016/j.irbm.2017.02.003

Cite this

@article{7786c7947e0f407ba0f9c0018474bae1,

title = "Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing",

abstract = "The liver CT image segmentation is still until now a challenging problem due to the fuzzy nature of the tumor transition to the surrounding tissues. The objective of this article is the consideration of the uncertainty present around the boundaries of a tumor region in the segmentation of the liver CT images as well as the segmentation of multiple tumors in the same CT image.",

author = "A. Ba{\^a}zaoui and W. Barhoumi and A. Ahmed and E. Zagrouba",

year = "2017",

month = apr,

day = "1",

doi = "10.1016/j.irbm.2017.02.003",

language = "English",

volume = "38",

pages = "98--108",

journal = "IRBM",

issn = "1959-0318",

publisher = "Elsevier Masson",

number = "2",

}

TY - JOUR

T1 - Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing

AU - Baâzaoui, A.

AU - Barhoumi, W.

AU - Ahmed, A.

AU - Zagrouba, E.

PY - 2017/4/1

Y1 - 2017/4/1

N2 - The liver CT image segmentation is still until now a challenging problem due to the fuzzy nature of the tumor transition to the surrounding tissues. The objective of this article is the consideration of the uncertainty present around the boundaries of a tumor region in the segmentation of the liver CT images as well as the segmentation of multiple tumors in the same CT image.

AB - The liver CT image segmentation is still until now a challenging problem due to the fuzzy nature of the tumor transition to the surrounding tissues. The objective of this article is the consideration of the uncertainty present around the boundaries of a tumor region in the segmentation of the liver CT images as well as the segmentation of multiple tumors in the same CT image.

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85016505069&partnerID=MN8TOARS

U2 - 10.1016/j.irbm.2017.02.003

DO - 10.1016/j.irbm.2017.02.003

M3 - Article (journal)

SN - 1959-0318

VL - 38

SP - 98

EP - 108

JO - IRBM

JF - IRBM

IS - 2

ER -

Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing

Abstract

Research Centres

Access to Document

Other files and links

Fingerprint

Cite this