Beamforming with sparse prior in ultrasound medical imaging

Nowadays the classical Delay-and-Sum (DAS) beamformer is extensively used in ultrasound imaging due to its low computational characteristics. However, it suffers from high sidelobe level, poor resolution and low contrast. An alternative is the Minimum-Variance (MV) beamformer which results in a higher image quality both in terms of spatial resolution and contrast. Even so, these benefits come at the expense of a higher computation complexity that limits its real-time capabilities. One solution that recently gained noticeable interest is the exploit of the sparsity of the scanned medium. Based on this assumption, we extend the DAS method to yield sparse results by using the Bayesian Information Criterion (BIC). Our realistic simulations demonstrate that the proposed beamforming (BF) method shows better performance than the classical DAS and MV in terms of lateral resolution, sidelobe reduction and contrast

Synthesizing Whole Slide Images

The increasing availability of digital whole slide images opens new perspectives for computer-assisted image analysis complementing modern histopathology, assuming we can implement reliable and efficient image analysis algorithms to extract the biologically relevant information. Both validation and supervised learning techniques typically rely on ground truths manually made by human experts. However, this task is difficult, subjective and usually not exhaustive. This is a well-known issue in the field of biomedical imaging, and a common solution is the use of artificial “phantoms”. Following this trend, we study the feasibility of synthesizing artificial histological images to create perfect ground truths. In this paper, we show that it is possible to generate a synthetic whole slide image with reasonable computing resources, and we propose a way to evaluate its quality

Simulation of realistic echocardiographic sequences for ground-truth validation of motion estimation

International audienceWe present a framework for the simulation of realistic cardiac ultrasound sequences. Both the visual aspect and the synthesized motion mimic a real echocardiography sequence used as template. The resulting simulation appears virtually indistinguishable from a real scan. As the true tissue motion is known, these synthetic sequences can provide a trustful benchmark for the analysis of heart motion. This possibility is illustrated by comparing the performance of two well known motion estimation algorithms