UNSUPERVISED DETECTION OF CONTOURS USING A STATISTICAL MODEL DIRO,

In this paper, we describe an unsupervised segmentation method for contours which proves quite adapted for the images obtained by electronic acquisition. We present two statistical models for the norm of the gradient of the gray level at the pixels of an image, one for contour points and one for points outside contours. We also describe a Markov model with constraint which incorporates those two statistical distributions as likelihood together with a simple a priori model. Our model is suitable for an Iterative Conditional Estimation (ICE) procedure for the estimation of the parameters and an Iterated Conditional Modes (ICM) algorithm, or a simulated annealing, for the segmentation. A preliminary step proceeds to the segmentation of the image into sub-regions and uses a Markov model without constraint based on the gray level distribution on the image. 1

Ultrasound characterization of red blood cell aggregation with intervening attenuating tissue-mimicking phantoms

The analysis of the ultrasonic frequency-dependent backscatter coefficient of aggregating red blood cells reveals information about blood structural properties. The difficulty in applying this technique in vivo is due to the frequency-dependent attenuation caused by intervening tissue layers that distorts the spectral content of signals backscattered by blood. An optimization method is proposed to simultaneously estimate tissue attenuation and blood structure properties, and was termed the structure factor size and attenuation estimator (SFSAE). An ultrasound scanner equipped with a wide-band 25 MHz probe was used to insonify porcine blood sheared in both Couette and tubular flow devices. Since skin is one of the most attenuating tissue layers during in vivo scanning, four skin-mimicking phantoms with different attenuation coefficients were introduced between the transducer and the blood flow. The SFSAE gave estimates with relative errors below 25% for attenuations between 0.115 and 0.411 dB∕MHz and kR<2.08 (k being the wave number and R the aggregate radius). The SFSAE can be useful to examine in vivo and in situ abnormal blood conditions suspected to promote pathophysiological cardiovascular consequences

Estimation of polydispersity in aggregating red blood cells by quantitative ultrasound backscatter analysis

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