Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity

Farhan Akram; Miguel Angel Garcia; Vivek Kumar Singh; Nasibeh Saffari; Mostafa Kamal Sarker; Domenec Puig

doi:10.3233/978-1-61499-806-8-146

Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity

Farhan Akram^*, Miguel Angel Garcia, Vivek Kumar Singh, Nasibeh Saffari, Mostafa Kamal Sarker, Domenec Puig

^*Corresponding author for this work

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

Abstract

In this paper, a novel region-based active contour method is proposed based to both correct and segment the intensity inhomogeneous images. A phase stretch transform (PST) kernel is used to compute new intensity means and bias field, which are employed to define a bias fitted image. In the proposed energy function, a new signed pressure force (SPF) function is formulated with a bias image fitted difference, which helps to segment the intensity inhomogeneous objects. A Gaussian kernel is also used to regularize the level set curve, which also removes the computationally expensive re-initialization. Finally, the proposed method is compared with the state-of-the-art both qualitatively and quantitatively using the synthetic and real brain magnetic resonance (MR) images, which shows it yields the best segmentation and correction results.

Original language	English
Title of host publication	Recent Advances in Artificial Intelligence Research and Development - Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017
Editors	Isabel Aguilo, Cecilio Angulo, Rene Alquezar, Alberto Ortiz, Joan Torrens
Publisher	IOS Press BV
Pages	146-155
Number of pages	10
ISBN (Electronic)	9781614998051
DOIs	https://doi.org/10.3233/978-1-61499-806-8-146
Publication status	Published - 2017
Externally published	Yes
Event	20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017 - Deltebre, Terres de l'Ebre, Spain Duration: 25 Oct 2017 → 27 Oct 2017

Publication series

Series	Frontiers in Artificial Intelligence and Applications
Volume	300
ISSN	0922-6389

Conference

Conference	20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017
Country/Territory	Spain
City	Deltebre, Terres de l'Ebre
Period	25/10/17 → 27/10/17

Bibliographical note

Funding Information:
This research has been partly supported by the Spanish Government through project DPI2016-77415-R.

Publisher Copyright:
© 2017 The authors and IOS Press. All rights reserved.

Access to Document

10.3233/978-1-61499-806-8-146

Cite this

Akram, F., Garcia, M. A., Singh, V. K., Saffari, N., Sarker, M. K., & Puig, D. (2017). Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity. In I. Aguilo, C. Angulo, R. Alquezar, A. Ortiz, & J. Torrens (Eds.), Recent Advances in Artificial Intelligence Research and Development - Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017 (pp. 146-155). IOS Press BV. https://doi.org/10.3233/978-1-61499-806-8-146

Akram, Farhan ; Garcia, Miguel Angel ; Singh, Vivek Kumar et al. / Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity. Recent Advances in Artificial Intelligence Research and Development - Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017. editor / Isabel Aguilo ; Cecilio Angulo ; Rene Alquezar ; Alberto Ortiz ; Joan Torrens. IOS Press BV, 2017. pp. 146-155 (Frontiers in Artificial Intelligence and Applications, Vol. 300).

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title = "Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity",

abstract = "In this paper, a novel region-based active contour method is proposed based to both correct and segment the intensity inhomogeneous images. A phase stretch transform (PST) kernel is used to compute new intensity means and bias field, which are employed to define a bias fitted image. In the proposed energy function, a new signed pressure force (SPF) function is formulated with a bias image fitted difference, which helps to segment the intensity inhomogeneous objects. A Gaussian kernel is also used to regularize the level set curve, which also removes the computationally expensive re-initialization. Finally, the proposed method is compared with the state-of-the-art both qualitatively and quantitatively using the synthetic and real brain magnetic resonance (MR) images, which shows it yields the best segmentation and correction results.",

author = "Farhan Akram and Garcia, {Miguel Angel} and Singh, {Vivek Kumar} and Nasibeh Saffari and Sarker, {Mostafa Kamal} and Domenec Puig",

note = "Funding Information: This research has been partly supported by the Spanish Government through project DPI2016-77415-R. Publisher Copyright: {\textcopyright} 2017 The authors and IOS Press. All rights reserved.; 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017 ; Conference date: 25-10-2017 Through 27-10-2017",

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doi = "10.3233/978-1-61499-806-8-146",

language = "English",

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editor = "Isabel Aguilo and Cecilio Angulo and Rene Alquezar and Alberto Ortiz and Joan Torrens",

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Akram, F, Garcia, MA, Singh, VK, Saffari, N, Sarker, MK & Puig, D 2017, Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity. in I Aguilo, C Angulo, R Alquezar, A Ortiz & J Torrens (eds), Recent Advances in Artificial Intelligence Research and Development - Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017. IOS Press BV, Frontiers in Artificial Intelligence and Applications, vol. 300, pp. 146-155, 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017, Deltebre, Terres de l'Ebre, Spain, 25/10/17. https://doi.org/10.3233/978-1-61499-806-8-146

Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity. / Akram, Farhan; Garcia, Miguel Angel; Singh, Vivek Kumar et al.
Recent Advances in Artificial Intelligence Research and Development - Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017. ed. / Isabel Aguilo; Cecilio Angulo; Rene Alquezar; Alberto Ortiz; Joan Torrens. IOS Press BV, 2017. p. 146-155 (Frontiers in Artificial Intelligence and Applications, Vol. 300).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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AB - In this paper, a novel region-based active contour method is proposed based to both correct and segment the intensity inhomogeneous images. A phase stretch transform (PST) kernel is used to compute new intensity means and bias field, which are employed to define a bias fitted image. In the proposed energy function, a new signed pressure force (SPF) function is formulated with a bias image fitted difference, which helps to segment the intensity inhomogeneous objects. A Gaussian kernel is also used to regularize the level set curve, which also removes the computationally expensive re-initialization. Finally, the proposed method is compared with the state-of-the-art both qualitatively and quantitatively using the synthetic and real brain magnetic resonance (MR) images, which shows it yields the best segmentation and correction results.

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Akram F, Garcia MA, Singh VK, Saffari N, Sarker MK, Puig D. Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity. In Aguilo I, Angulo C, Alquezar R, Ortiz A, Torrens J, editors, Recent Advances in Artificial Intelligence Research and Development - Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2017. IOS Press BV. 2017. p. 146-155. (Frontiers in Artificial Intelligence and Applications, Vol. 300). doi: 10.3233/978-1-61499-806-8-146

Image segmentation using active contours driven by bias fitted image robust to intensity inhomogeneity

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