1. Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging
- Author
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Daniel Balvay, Jesús Angulo, Dominique Jeulin, Charles-André Cuénod, Guillaume Noyel, Centre de Morphologie Mathématique (CMM), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), LRI-EA4062, Université Paris Descartes - Paris 5 (UPD5), Service de radiologie (AP-HP - HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de Morphologie Mathématique ( CMM ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), Université Paris Descartes - Paris 5 ( UPD5 ), Assistance publique - Hôpitaux de Paris (AP-HP), and MINES ParisTech - École nationale supérieure des mines de Paris - PSL Research University (PSL)
- Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging ,[INFO.INFO-IM] Computer Science [cs]/Medical Imaging ,Normalization (image processing) ,ACM : I.4.6 ,tumours ,02 engineering and technology ,ACM : I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ,medical image processing ,[ SDV.CAN ] Life Sciences [q-bio]/Cancer ,030218 nuclear medicine & medical imaging ,ACM : I.5 ,0302 clinical medicine ,ACM : I.4 ,ACM : I.: Computing Methodologies/I.5: PATTERN RECOGNITION ,[ INFO.INFO-TI ] Computer Science [cs]/Image Processing ,0202 electrical engineering, electronic engineering, information engineering ,Segmentation ,Computer vision ,image segmentation ,[ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging ,Mathematics ,[MATH.TR-IMG] Mathematics [math]/domain_math.tr-img ,ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ,[ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging ,Contextual image classification ,Hyperspectral imaging ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,filtering theory ,020201 artificial intelligence & image processing ,hyperspectral images ,probability ,angiogenesis imaging ,biomedical MRI ,[SDV.CAN]Life Sciences [q-bio]/Cancer ,Mathematical morphology ,Edge detection ,blood vessels ,03 medical and health sciences ,[SDV.CAN] Life Sciences [q-bio]/Cancer ,[INFO.INFO-TI] Computer Science [cs]/Image Processing ,Histogram ,Machine learning ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,cancer ,image enhancement ,ACM : I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation ,edge detection ,Dynamic Contrast Enhanced MRI ,ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ,business.industry ,segmentation ,Pattern recognition ,Image segmentation ,[MATH.APPL] Mathematics [math]/domain_math.appl ,ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation ,[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging ,Computer Science::Computer Vision and Pattern Recognition ,Mathematical Morphology ,Artificial intelligence ,Multivariate images ,business ,image classification - Abstract
ISBN : 978-1-4244-2002-5; International audience; 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.
- Published
- 2008