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1.
Med Image Anal ; 13(3): 456-70, 2009 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-19362876

RESUMO

We propose a novel approach for the simultaneous segmentation of multiple structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the boundaries of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real magnetic resonance (MR) images are presented, quantitatively assessed and discussed.


Assuntos
Algoritmos , Inteligência Artificial , Encéfalo/anatomia & histologia , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Reconhecimento Automatizado de Padrão/métodos , Lógica Fuzzy , Humanos , Aumento da Imagem/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
2.
Med Image Comput Comput Assist Interv ; 11(Pt 1): 178-85, 2008.
Artigo em Inglês | MEDLINE | ID: mdl-18979746

RESUMO

A typical Cardiac Magnetic Resonance (CMR) examination includes acquisition of a sequence of short-axis (SA) and long-axis (LA) images covering the cardiac cycle. Quantitative analysis of the heart function requires segmentation of the left ventricle (LV) SA images, while segmented LA views allow more accurate estimation of the basal slice and can be used for slice registration. Since manual segmentation of CMR images is very tedious and time-consuming, its automation is highly required. In this paper, we propose a fully automatic 2D method for segmenting LV consecutively in LA and SA images. The approach was validated on 35 patients giving mean segmentation error smaller than one pixel, both for LA and SA, and accurate LV volume measurements.


Assuntos
Algoritmos , Inteligência Artificial , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imagem Cinética por Ressonância Magnética/métodos , Reconhecimento Automatizado de Padrão/métodos , Disfunção Ventricular Esquerda/diagnóstico , Humanos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
3.
Med Image Comput Comput Assist Interv ; 11(Pt 1): 186-93, 2008.
Artigo em Inglês | MEDLINE | ID: mdl-18979747

RESUMO

We propose a new method to segment long-axis cardiac MR images acquired with a late-enhancement protocol. Detecting the myocardium boundaries is difficult in these images because healthy myocardium appears dark while the intensity of enhanced areas ranges from gray to white, depending on the myocardial damage. In this context, geometrical template deformation, alternated with the update of a damaged tissue map, allows us to include abnormal myocardium parts in the final segmentation. The template and map are initialized using short-axis images and the deformation parameters are adapted according to the type of enhancement pattern. Good segmentation results are obtained on a database of real pathologic heart images presenting various types of abnormal myocardium tissues.


Assuntos
Algoritmos , Inteligência Artificial , Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Miocárdio Atordoado/patologia , Miocárdio/patologia , Reconhecimento Automatizado de Padrão/métodos , Humanos , Aumento da Imagem/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
4.
Inf Process Med Imaging ; 19: 333-44, 2005.
Artigo em Inglês | MEDLINE | ID: mdl-17354707

RESUMO

We propose to segment 3D structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the borders of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real MR images are presented, quantitatively assessed and discussed.


Assuntos
Encéfalo/anatomia & histologia , Lógica Fuzzy , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Reconhecimento Automatizado de Padrão/métodos , Algoritmos , Humanos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
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