Automatic liver segmentation from abdominal MRI images using active contours

Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa

Research output: Contribution to journalArticle

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Abstract

Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a system for the automatic segmentation of the liver from Magnetic Resonance Images (MRI). The system works without the need for setting manual seed points or setting a region of interest. Instead, the proposed system automatically detects and segments the liver through relying on its anatomical features for detection and using active contour for segmentation. The proposed segmentation system begins with localizing the liver or a part of it from a given MRI image using biggest components analysis. The extracted liver part is later used as a mask for full liver segmentation using active contour. The proposed system is fully automatic, works on different cases of MRI images (different sizes, healthy and abnormal liver). The detection and segmentation of the liver succeeded in 95% of the test cases acquired from different MRI imaging modalities.
Original languageEnglish
Pages (from-to)30-37
Number of pages8
JournalInternational Journal of Computer Applications
Volume176
Issue number1
DOIs
Publication statusPublished - 17 Oct 2017
Externally publishedYes

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Magnetic resonance
Liver
Biomarkers
Decision support systems
Seed
Masks
Imaging techniques

Cite this

G. Mohamed, Roaa ; A. Seada, Noha ; Hamdy, Salma ; G. M. Mostafa, Mostafa. / Automatic liver segmentation from abdominal MRI images using active contours. In: International Journal of Computer Applications. 2017 ; Vol. 176, No. 1. pp. 30-37.
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Automatic liver segmentation from abdominal MRI images using active contours. / G. Mohamed, Roaa; A. Seada, Noha; Hamdy, Salma; G. M. Mostafa, Mostafa.

In: International Journal of Computer Applications, Vol. 176, No. 1, 17.10.2017, p. 30-37.

Research output: Contribution to journalArticle

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