Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging

被引：5

作者：

Noyel, G. ^{[1
]}

Angulo, J. ^{[1
]}

Jeulin, D. ^{[1
]}

Balvay, D. ^{[2
]}

Cuenod, C-A. ^{[2
]}

机构：

[1] Ecole Mines Paris, Ctr Morphol Math, 35 Rue St Honore, F-77305 Fontainebleau, France

[2] HEGP, LRI EA 4062 Paris V Descartes, APHP, Serv Radiol, Paris, France

来源：

2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4 | 2008年

关键词：

hyperspectral images; mathematical morphology; MRI; segmentation; angiogenesis imaging;

D O I：

10.1109/ISBI.2008.4541297

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Segmenting dynamic contrast enhanced-MRI series of small animal, which are intrinsically noisy and low contrasted images with low resolution, is the aim of this paper. To do this, a segmentation method taking into account the temporal (spectral) and spatial information is presented on several series. The idea is to start from a temporal classification, and to build a probability density function of contours conditionally to this classification. Then, this function is segmented to find potentially tumorous areas. The method is presented on several series after a range normalization histogram in order to compare the series.

引用

页码：1517 / +

页数：2

共 12 条

[1] MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES
Noyel, Guillaume
Angulo, Jesus
Jeulin, Dominique
Balvay, Daniel
Cuenod, Charles Andre
[J]. IMAGE ANALYSIS & STEREOLOGY, 2015, 34 (01): : 1 - 25
[2] Imaging angiogenesis using DCE-MRI
Jayson, G.
[J]. EJC SUPPLEMENTS, 2006, 4 (12): : 139 - 139
[3] Breast lesion segmentation in DCE-MRI Imaging
Koper, Zuzanna
Frackiewicz, Mariusz
Palus, Henryk
Borys, Damian
Psiuk-Maksymowicz, Krzysztof
[J]. 2018 14TH INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS (SITIS), 2018, : 308 - 313
[4] BreastDM: A DCE-MRI dataset for breast tumor image segmentation and classification
Zhao, Xiaoming
Liao, Yuehui
Xie, Jiahao
He, Xiaxia
Zhang, Shiqing
Wang, Guoyu
Fang, Jiangxiong
Lu, Hongsheng
Yu, Jun
[J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 164
[5] SEGMENTATION AND CLASSIFICATION OF TRIPLE NEGATIVE BREAST CANCERS USING DCE-MRI
Agner, Shannon C.
Xu, Jun
Fatakdawala, Hussain
Ganesan, Shridar
Madabhushi, Anant
Englander, Sarah
Rosen, Mark
Thomas, Kathleen
Schnall, Mitehell
Feldman, Miehael
Tomaszewski, John
[J]. 2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2, 2009, : 1227 - +
[6] Evaluating Impacts of Motion Correction on Deep Learning Approaches for Breast DCE-MRI Segmentation and Classification
Galli, Antonio
Gravina, Michela
Marrone, Stefano
Piantadosi, Gabriele
Sansone, Mario
Sansone, Carlo
[J]. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT II, 2019, 11679 : 294 - 304
[7] Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequences
Prinzi, Francesco
Orlando, Alessia
Gaglio, Salvatore
Vitabile, Salvatore
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2024, 249
[8] Case-Based Repeatability of AI Classification On Multi-Modality Imaging of Breast Lesions Using DCE-MRI and FFDM
Whitney, H.
Drukker, K.
Edwards, A.
Lan, L.
Abe, H.
Giger, M.
[J]. MEDICAL PHYSICS, 2022, 49 (06) : E296 - E296
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Huang, Junhui
Yang, Shangpo
Zou, Liyan
Chen, Yingying
Yang, Long
Yao, Bingyu
Huang, Zhenxing
Zhong, Yihong
Liu, Zhou
Zhang, Na
[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 87
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Fang, Wanxuan
Mao, Yijun
Wang, Haolin
Sugimori, Hiroyuki
Kiuch, Shinji
Sutherland, Kenneth
Kamishima, Tamotsu
[J]. JAPANESE JOURNAL OF RADIOLOGY, 2024, 42 (10) : 1198 - 1198

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