Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy

Fanghua Liu; Chaijie Duan; Kehong Yuan; Zhengrong Liang; Shanglian Bao

doi:10.1007/978-3-642-25719-3_11

Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy

Fanghua Liu
, Chaijie Duan
, Kehong Yuan
, Zhengrong Liang
, Shanglian Bao

Radiology

Tsinghua University
Peking University

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

Abstract

This paper presents a level set based method for bladder abnormality detection on T1-weighted MR images. First, the bladder wall is segmented by using a coupled level set framework, in which the inner and outer borders of the bladder wall are extracted by two level set functions. Then, the middle layer of the bladder wall is founded and represented by a new level set function. Finally, the new level set function divides the bladder wall into several layers. The inter-layer intensity of all voxels in each layer is sorted in ascending order to generate the inter-layer intensity curve. The results prove the effectiveness of inter-layer intensity curve in indicating the emerging of the bladder abnormalities.

Original language	English
Title of host publication	Virtual Colonoscopy and Abdominal Imaging
Subtitle of host publication	Computational Challenges and Clinical Opportunities - Second International Workshop, Held in Conjunction with MICCAI 2010, Revised Selected Papers
Pages	76-83
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-25719-3_11
State	Published - 2011
Event	2nd Int.Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, Held in Conjunction with the 13th Int.Conf. on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China Duration: Sep 20 2010 → Sep 20 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6668 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd Int.Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, Held in Conjunction with the 13th Int.Conf. on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Country/Territory	China
City	Beijing
Period	09/20/10 → 09/20/10

Keywords

abnormalities detection
bladder wall
inter-layer intensity curve

Access to Document

10.1007/978-3-642-25719-3_11

Cite this

Liu, F., Duan, C., Yuan, K., Liang, Z., & Bao, S. (2011). Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy. In Virtual Colonoscopy and Abdominal Imaging: Computational Challenges and Clinical Opportunities - Second International Workshop, Held in Conjunction with MICCAI 2010, Revised Selected Papers (pp. 76-83). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6668 LNCS). https://doi.org/10.1007/978-3-642-25719-3_11

Liu, Fanghua ; Duan, Chaijie ; Yuan, Kehong et al. / Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy. Virtual Colonoscopy and Abdominal Imaging: Computational Challenges and Clinical Opportunities - Second International Workshop, Held in Conjunction with MICCAI 2010, Revised Selected Papers. 2011. pp. 76-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy",

abstract = "This paper presents a level set based method for bladder abnormality detection on T1-weighted MR images. First, the bladder wall is segmented by using a coupled level set framework, in which the inner and outer borders of the bladder wall are extracted by two level set functions. Then, the middle layer of the bladder wall is founded and represented by a new level set function. Finally, the new level set function divides the bladder wall into several layers. The inter-layer intensity of all voxels in each layer is sorted in ascending order to generate the inter-layer intensity curve. The results prove the effectiveness of inter-layer intensity curve in indicating the emerging of the bladder abnormalities.",

keywords = "abnormalities detection, bladder wall, inter-layer intensity curve",

author = "Fanghua Liu and Chaijie Duan and Kehong Yuan and Zhengrong Liang and Shanglian Bao",

year = "2011",

doi = "10.1007/978-3-642-25719-3\_11",

language = "English",

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booktitle = "Virtual Colonoscopy and Abdominal Imaging",

note = "2nd Int.Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, Held in Conjunction with the 13th Int.Conf. on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 ; Conference date: 20-09-2010 Through 20-09-2010",

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Liu, F, Duan, C, Yuan, K, Liang, Z & Bao, S 2011, Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy. in Virtual Colonoscopy and Abdominal Imaging: Computational Challenges and Clinical Opportunities - Second International Workshop, Held in Conjunction with MICCAI 2010, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6668 LNCS, pp. 76-83, 2nd Int.Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, Held in Conjunction with the 13th Int.Conf. on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 09/20/10. https://doi.org/10.1007/978-3-642-25719-3_11

Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy. / Liu, Fanghua; Duan, Chaijie; Yuan, Kehong et al.
Virtual Colonoscopy and Abdominal Imaging: Computational Challenges and Clinical Opportunities - Second International Workshop, Held in Conjunction with MICCAI 2010, Revised Selected Papers. 2011. p. 76-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6668 LNCS).

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

TY - GEN

T1 - Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy

AU - Liu, Fanghua

AU - Duan, Chaijie

AU - Yuan, Kehong

AU - Liang, Zhengrong

AU - Bao, Shanglian

PY - 2011

Y1 - 2011

N2 - This paper presents a level set based method for bladder abnormality detection on T1-weighted MR images. First, the bladder wall is segmented by using a coupled level set framework, in which the inner and outer borders of the bladder wall are extracted by two level set functions. Then, the middle layer of the bladder wall is founded and represented by a new level set function. Finally, the new level set function divides the bladder wall into several layers. The inter-layer intensity of all voxels in each layer is sorted in ascending order to generate the inter-layer intensity curve. The results prove the effectiveness of inter-layer intensity curve in indicating the emerging of the bladder abnormalities.

AB - This paper presents a level set based method for bladder abnormality detection on T1-weighted MR images. First, the bladder wall is segmented by using a coupled level set framework, in which the inner and outer borders of the bladder wall are extracted by two level set functions. Then, the middle layer of the bladder wall is founded and represented by a new level set function. Finally, the new level set function divides the bladder wall into several layers. The inter-layer intensity of all voxels in each layer is sorted in ascending order to generate the inter-layer intensity curve. The results prove the effectiveness of inter-layer intensity curve in indicating the emerging of the bladder abnormalities.

KW - abnormalities detection

KW - bladder wall

KW - inter-layer intensity curve

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U2 - 10.1007/978-3-642-25719-3_11

DO - 10.1007/978-3-642-25719-3_11

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AN - SCOPUS:83755206160

SN - 9783642257186

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

SP - 76

EP - 83

BT - Virtual Colonoscopy and Abdominal Imaging

T2 - 2nd Int.Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, Held in Conjunction with the 13th Int.Conf. on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010

Y2 - 20 September 2010 through 20 September 2010

ER -

Liu F, Duan C, Yuan K, Liang Z, Bao S. Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy. In Virtual Colonoscopy and Abdominal Imaging: Computational Challenges and Clinical Opportunities - Second International Workshop, Held in Conjunction with MICCAI 2010, Revised Selected Papers. 2011. p. 76-83. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-25719-3_11

Detecting bladder abnormalities based on inter-layer intensity curve for virtual cystoscopy

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Keywords

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