Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5519
Title: A comparative performance evaluation of various approaches for liver segmentation from SPIR images
Authors: Göçeri, Evgin
Ünlü, Mehmet Zübeyir
Dicle, Oğuz
Keywords: Gaussian mixture model
K-means
Liver segmentation
Magnetic resonance image
Multilayer perceptron
Image segmentation
Publisher: Türkiye Klinikleri Journal of Medical Sciences
Source: Göçeri, E., Ünlü, M. Z., and Dicle, O. (2015). A comparative performance evaluation of various approaches for liver segmentation from SPIR images. Turkish Journal of Electrical Engineering and Computer Sciences, 23(3), 741-768. doi:10.3906/elk-1304-36
Abstract: Developing a robust method for liver segmentation from magnetic resonance images is a challenging task because of the similar intensity values between adjacent organs, the geometrically complex liver structure, and injection of contrast media. Most importantly, a high anatomical variability of a healthy or diseased liver is a major challenge in defining the exact boundaries of the liver. Several artifacts of pulsation, motion, and partial volume effects are also among the variety of factors that make automatic liver segmentation difficult. In this paper, we present an overview of liver segmentation methods in magnetic resonance images and show comparative results of seven different pseudo-3D liver segmentation approaches chosen from deterministic (K-means-based), probabilistic (Gaussian model-based), supervised neural network (multilayer perceptron-based), and deformable model-based (level set) segmentation methods. The results of quantitative and qualitative analyses using sensitivity, specificity, and accuracy metrics show that the multilayer perceptron-based approach and a level set-based approach, both of which use distance regularization terms and signed pressure force function, are the most successful methods for liver segmentation from spectral presaturation inversion recovery (SPIR) images. However, the multilayer perceptron-based segmentation method has a higher computational cost. The automatic method using the distance regularized level set evolution with signed pressure force function avoids the sensitivity of a user-defined initial contour for each slice, gives the most efficient results for liver segmentation after the preprocessing steps, and also requires less computational time.
URI: http://doi.org/10.3906/elk-1304-36
http://hdl.handle.net/11147/5519
https://search.trdizin.gov.tr/yayin/detay/168945
ISSN: 1300-0632
1300-0632
1303-6203
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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