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1.
J Biomed Opt ; 20(8): 80502, 2015 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-26263413

RESUMO

To enable tissue function-based tumor diagnosis over the large number of existing digital mammography systems worldwide, we propose a cost-effective and robust approach to incorporate tomographic optical tissue characterization with separately acquired digital mammograms. Using a flexible contour-based registration algorithm, we were able to incorporate an independently measured two-dimensional x-ray mammogram as structural priors in a joint optical/x-ray image reconstruction, resulting in improved spatial details in the optical images and robust optical property estimation. We validated this approach with a retrospective clinical study of 67 patients, including 30 malignant and 37 benign cases, and demonstrated that the proposed approach can help to distinguish malignant from solid benign lesions and fibroglandular tissues, with a performance comparable to the approach using spatially coregistered optical/x-ray measurements.


Assuntos
Neoplasias da Mama/diagnóstico , Interpretação de Imagem Assistida por Computador/métodos , Mamografia/métodos , Imagem Multimodal/métodos , Técnica de Subtração , Tomografia Óptica/métodos , Algoritmos , Estudos de Viabilidade , Feminino , Humanos , Aumento da Imagem/métodos , Reconhecimento Automatizado de Padrão/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.
IEEE Trans Med Imaging ; 24(4): 477-85, 2005 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-15822806

RESUMO

This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (two thresholds). For the more complex problem of thrombus segmentation, a grey level modeling approach with a nonparametric pattern classification technique is used, namely k-nearest neighbors. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding to the intensity profile at each vertex on the surface. A parameter optimization study is conducted, followed by experiments to assess the overall segmentation quality and the robustness of results against variation in user input. Results obtained in a study of 17 patients show that the agreement with respect to manual segmentations is comparable to previous values reported in the literature, with considerable less user interaction.


Assuntos
Algoritmos , Aneurisma da Aorta Abdominal/diagnóstico por imagem , Inteligência Artificial , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Trombose/diagnóstico por imagem , Angiografia/métodos , Aneurisma da Aorta Abdominal/complicações , Análise por Conglomerados , Gráficos por Computador , Humanos , Modelos Cardiovasculares , Modelos Estatísticos , Intensificação de Imagem Radiográfica/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Trombose/etiologia , Tomografia Computadorizada por Raios X/métodos
5.
IEEE Trans Med Imaging ; 21(9): 1059-68, 2002 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-12564874

RESUMO

Quantitative functional analysis of the left ventricle plays a very important role in the diagnosis of heart diseases. While in standard two-dimensional echocardiography this quantification is limited to rather crude volume estimation, three-dimensional (3-D) echocardiography not only significantly improves its accuracy but also makes it possible to derive valuable additional information, like various wall-motion measurements. In this paper, we present a new efficient method for the functional evaluation of the left ventricle from 3-D echographic sequences. It comprises a segmentation step that is based on the integration of 3-D deformable surfaces and a four-dimensional statistical heart motion model. The segmentation results in an accurate 3-D + time left ventricle discrete representation. Functional descriptors like local wall-motion indexes are automatically derived from this representation. The method has been successfully tested both on electrocardiography-gated and real-time 3-D data. It has proven to be fast, accurate, and robust.


Assuntos
Ecocardiografia Tridimensional , Função Ventricular Esquerda , Eletrocardiografia , Humanos , Modelos Cardiovasculares , Contração Miocárdica
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