2024-03-29T11:08:52Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/1283292022-12-08T19:22:45Zcom_10803_253col_10803_254
nam a 5i 4500
Ultrasound
Ultrasò
Ultrasonido
Breast cancer
Càncer de mama
Cáncer de mama
Segmentation
Segmentació
Segmentación
Optimization framework
Optimització
Optimización
Deformable object segmentation in ultra-sound images
[Girona] :
Universitat de Girona,
2014
Accés lliure
http://hdl.handle.net/10803/128329
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Massich i Vall, Joan,
autor
1 recurs en línia (182 pàgines)
Tesi
Doctorat
Universitat de Girona. Departament d'Arquitectura i Tecnologia de Computadors
2013
Universitat de Girona. Departament d'Arquitectura i Tecnologia de Computadors
Tesis i dissertacions electròniques
Martí Bonmatí, Joan,
supervisor acadèmic
Méridaudeau, Fabrice,
supervisor acadèmic
TDX
This thesis analyses the current strategies to segment breast lesions in Ultra-Sound (US) data and proposes a fully automatic methodology for generating accurate segmentations of breast lesions in US data with low false positive rates. The proposed approach targets the segmentation as a minimization procedure for a multi-label probabilistic framework that takes advantage of min-cut/max- flow Graph-Cut (GC) minimization for inferring the appropriate label from a set of tissue labels for all the pixels within the target image. The image is divided into contiguous regions so that all the pixels belonging to a particular region would share the same label by the end of the process. From a training image dataset stochastic models are built in order to infer a label for each region of the image. The main advantage of the proposed framework is that it splits the problem of segmenting the tissues present in US the images into subtasks that can be taken care of individually
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