Cellular automata segmentation of brain tumors on post contrast MR images

Andac Hamamci; Gozde Unal; Nadir Kucuk; Kayihan Engin

doi:10.1007/978-3-642-15711-0_18

Cellular automata segmentation of brain tumors on post contrast MR images

Andac Hamamci^*, Gozde Unal, Nadir Kucuk, Kayihan Engin

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Citations (Scopus)

Abstract

In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Pages	137-146
Number of pages	10
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-15711-0_18
Publication status	Published - 2010
Externally published	Yes
Event	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China Duration: 20 Sept 2010 → 24 Sept 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6363 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Country/Territory	China
City	Beijing
Period	20/09/10 → 24/09/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-15711-0_18

Cite this

Hamamci, A., Unal, G., Kucuk, N., & Engin, K. (2010). Cellular automata segmentation of brain tumors on post contrast MR images. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings (PART 3 ed., pp. 137-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6363 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-15711-0_18

Hamamci, Andac ; Unal, Gozde ; Kucuk, Nadir et al. / Cellular automata segmentation of brain tumors on post contrast MR images. Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3. ed. 2010. pp. 137-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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title = "Cellular automata segmentation of brain tumors on post contrast MR images",

abstract = "In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.",

author = "Andac Hamamci and Gozde Unal and Nadir Kucuk and Kayihan Engin",

year = "2010",

doi = "10.1007/978-3-642-15711-0_18",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings",

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Hamamci, A, Unal, G, Kucuk, N & Engin, K 2010, Cellular automata segmentation of brain tumors on post contrast MR images. in Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6363 LNCS, pp. 137-146, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 20/09/10. https://doi.org/10.1007/978-3-642-15711-0_18

Cellular automata segmentation of brain tumors on post contrast MR images. / Hamamci, Andac; Unal, Gozde; Kucuk, Nadir et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3. ed. 2010. p. 137-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6363 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Unal, Gozde

AU - Kucuk, Nadir

AU - Engin, Kayihan

PY - 2010

Y1 - 2010

N2 - In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.

AB - In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.

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DO - 10.1007/978-3-642-15711-0_18

M3 - Conference contribution

C2 - 20879393

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SN - 3642157106

SN - 9783642157103

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Hamamci A, Unal G, Kucuk N, Engin K. Cellular automata segmentation of brain tumors on post contrast MR images. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3 ed. 2010. p. 137-146. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-15711-0_18

Cellular automata segmentation of brain tumors on post contrast MR images

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