Automated Extraction Of Small Structures In Medical Images Based On Multi Scale Approach.

Multi scale techniques coupled with active contours have been widely used to locate the boundaries of structures in noisy images. Significant fine structures have been emphasized through appropriate scale selection

A local Rayleigh model with spatial scale selection for ultrasound image segmentation

12 pagesInternational audienceUltrasound images are very noisy, with poor contrast and the attenuation of the acoustic wave in the depth of the observed medium leads to strong inhomogeneities in the image. Segmentation methods using global image statistics give unsatisfactory results. The use of local image statistics can solve effectively the problem of attenuation. The contribution of this paper is two folds. First, we propose the study of the adaptation of the global model proposed by Sarti et al. We kept the variational framework and the Rayleigh model of the observed image statistics. Second, we propose an interesting and generic adaptive scale selection algorithm based on the Intersection of Confidence Interval rule. The latter is also applied to the local Gaussian segmentation model of Brox and Cremers. Results on realistic simulations of ultrasound images show the robustness and the superiority of the local Rayleigh model. The efficiency and the genericity of the proposed scale selection strategy is also demonstrated

Relatório de acompanhamento do projecto RHEUMUS (sistema de análise de imagens de ecografia para reumatologia): QREN - Projecto Nº 38505

O projeto RHEUMUS tem como principal objetivo o desenvolvimento de um sistema de processamento e análise de imagens de ecografia para a área da reumatologia. A solução em desenvolvimento será composta por um conjunto de ferramentas computacionais capazes de identificar, segmentar e quantificar estruturas anatómicas normais/patológicas do sistema músculo-esquelético da mão e do joelho, baseadas na tecnologia de visão por computador.projecto RHEUMUSQREN - Projecto Nº 3850

Improved quantification of left ventricular volumes and mass based on endocardial and epicardial surface detection from cardiac MR images using level set models

The reproducibility of left ventricular (LV) volume and mass measurements based on subjective slice-by-slice tracing of LV borders is affected by image quality, and volume estimates are biased by geometric modeling. The authors developed a technique for volumetric surface detection (VoSD) and quantification of LV volumes and mass without tracing and geometric approximations. The authors hypothesized that this technique is accurate and more reproducible than the conventional methodology. Methods. Images were obtained in 24 patients in 6 to 10 slices from LV base to apex (GE 1.5 T, FIESTA). Volumetric data were reconstructed, and endocardial and epicardial surfaces were detected using the level set approach. LV volumes were obtained from voxel counts and used to compute ejection fraction (EF) and mass. Conventional measurements (MASS Analysis) were used as a reference to test the accuracy of VoSD technique (linear regression, Bland-Altman). For both techniques, measurements were repeated to compute inter- and intra-observer variability. Results. VoSD values resulted in high correlation with the reference values (EDV: r = 0.98; ESV: r = 0.99; EF: r = 0.91; mass: r = 0.98), with no significant biases (8 ml, 5 ml, 0.2% and 9 g) and narrow limits of agreement (SD: 13 ml, 10 ml, 6% and 9 g). Inter-observer variability of the VoSD technique was lower (range 3 to 5%) than that of the reference technique (5 to 11%; p < 0.05). Intra-observer variability was also lower (1 to 3% vs. 7 to 10%; p < 0.05). Conclusion. VoSD technique allows accurate measurements of LV volumes, EF, and mass, which are more reproducible than the conventional methodology

Segmentación de imágenes de ultrasonido por medio de un algoritmo rápido de contornos activos

El estudio e interpretación de imágenes de ultrasonido es un desafío en el área de procesamiento de imágenes, debido al ruido que este tipo de imágenes posee. En este trabajo se propone la utilización de un método de segmentación basado en conjuntos de nivel pero que no resuelve ecuaciones diferenciales sino que ajusta el contorno del objeto de interés por medio del intercambio de elementos entre dos listas de pixels vecinos. Se propone utilizar la distribución Gaussiana para modelar los datos provenientes de la imagen y estimar los parámetros correspondientes en cada paso del algoritmo, actualizando la información de la región que se desea segmentar. Con esta propuesta logramos una mejora significativa en la precisión del ajuste del borde del objeto de interés, comparado con el método original.WCGIV - XI Workshop computación gráfica, imágenes y visualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

<p>Abstract</p> <p>Background</p> <p>Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve.</p> <p>Methods</p> <p>This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles.</p> <p>Results</p> <p>The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated.</p> <p>Conclusions</p> <p>The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.</p