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

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

20 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Sayfalar	137-146
Sayfa sayısı	10
Baskı	PART 3
DOI'lar	https://doi.org/10.1007/978-3-642-15711-0_18
Yayın durumu	Yayınlandı - 2010
Harici olarak yayınlandı	Evet
Etkinlik	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China Süre: 20 Eyl 2010 → 24 Eyl 2010

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Sayı	PART 3
Hacim	6363 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

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???event.eventtypes.event.conference???	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Ülke/Bölge	China
Şehir	Beijing
Periyot	20/09/10 → 24/09/10

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 3 Sağlık ve Kaliteli Yaşam

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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); Hac. 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).

@inproceedings{9d117d68bf754927bf0ed4d7d1485193,

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",

language = "English",

isbn = "3642157106",

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

number = "PART 3",

pages = "137--146",

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

edition = "PART 3",

note = "13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 ; Conference date: 20-09-2010 Through 24-09-2010",

}

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, hac. 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); Hac. 6363 LNCS, No. PART 3).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Cellular automata segmentation of brain tumors on post contrast MR images

AU - Hamamci, Andac

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.

UR - https://www.scopus.com/pages/publications/84857977127

U2 - 10.1007/978-3-642-15711-0_18

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

M3 - Conference contribution

C2 - 20879393

AN - SCOPUS:84857977127

SN - 3642157106

SN - 9783642157103

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

SP - 137

EP - 146

BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings

T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010

Y2 - 20 September 2010 through 24 September 2010

ER -

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

Özet

Yayın serisi

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BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

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