Manual and automated intima-media thickness and diameter measurements of the common carotid artery in patients with renal failure disease.

The objective of this study was to investigate differences in intima-media thickness (IMT) and diameter (D) measurements of the common carotid artery (CCA) in ultrasound imaging in normal subjects and renal failure disease (RFD) patients. Manual measurements by two experts and automated segmentation measurements (based on snakes and active contour models (ACM)) were carried out on 73 normal subjects, and 80 RFD patients. Statistical analysis was carried out using the Wilcoxon rank-sum test at p<0.

Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery.

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging.

Segmentation of atherosclerotic carotid plaque in ultrasound video.

The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames.

Classification of noisy signals using fuzzy ARTMAP neural networks.

This paper describes an approach to classification of noisy signals using a technique based on the fuzzy ARTMAP neural network (FAMNN). The proposed method is a modification of the testing phase of the fuzzy ARTMAP that exhibits superior generalization performance compared to the generalization performance of the standard fuzzy ARTMAP in the presence of noise. An application to textured gray-scale image segmentation is presented.

Wavelet-based rotational invariant roughness features for texture classification and segmentation.

In this paper, we introduce a rotational invariant feature set for texture segmentation and classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized.