Information-theoretic feature detection in ultrasound images

The detection of image features is an essential component of medical image processing, and has wide-ranging applications including adaptive filtering, segmentation, and registration. In this paper, we present an information-theoretic approach to feature detection in ultrasound images. Ultrasound images are corrupted by speckle noise, which is a disruptive random pattern that obscures the features of interest. Using theoretical probability density functions of the speckle intensity distributions, we derive analytic expressions that measure the distance between distributions taken from different regions in an ultrasound image and use these distances to detect features. We compare the technique to classic gradient-based feature detection methods

Registration of Ultrasound Images Using an Information-Theoretic Feature Detector

In this paper, we present a new method for ultrasound image registration. For each image to be registered, our method first applies an ultrasound-specific information-theoretic feature detector, which is based on statistical modeling of speckle and provides a feature image that robustly delineates important edges in the image. These feature images are then registered using differential equations, the solution of which provides a locally optimal transformation that brings the images into alignment. We describe our method and present experimental results demonstrating its effectiveness, particularly for low contrast, speckled images. Furthermore, we compare our method to standard gradient-based techniques, which we show are more susceptible to misregistration

Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours

Segmentation of ultrasound images is often a very challenging task due to speckle noise that contaminates the image. It is well known that speckle noise exhibits an asymmetric distribution as well as significant spatial correlation. Since these attributes can be difficult to model, many previous ultrasound segmentation methods oversimplify the problem by assuming that the noise is white and/or Gaussian, resulting in generic approaches that are actually more suitable to MR and X-ray segmentation than ultrasound. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that first decorrelates the image, and then performs segmentation on the whitened result using statistical region-based active contours. In particular, we design a gradient ascent flow that evolves the active contours to maximize a log likelihood functional based on the Fisher-Tippett distribution. We present experimental results that demonstrate the effectiveness of our method

Semi-Automatic 3-D Segmentation of Anatomical Structures of Brain MRI Volumes using Graph Cuts

We present a semi-automatic segmentation technique of the anatomical structures of the brain: cerebrum, cerebellum, and brain stem. The method uses graph cuts segmentation with an anatomic template for initialization. First, a skull stripping procedure is applied to remove non-brain tissues. Then, the segmentation is done hierarchically by first, extracting first the cerebrum from the brain, and then from the remaining volume the cerebellum and the brain stem are separated. This method is fast and can separate different anatomical structures of the brain in spite of weak boundaries. We describe our approach and present experimental results demonstrating its usefulness

Efficient and Robust Segmentations Based on Eikonal and Diffusion PDEs

In this paper, we present efficient and simple image segmentations based on the solution of two separate Eikonal equations, each originating from a different region. Distance functions from the interior and exterior regions are computed, and final segmentation labels are determined by a competition criterion between the distance functions. We also consider applying a diffusion partial differential equation (PDE) based method to propagate information in a manner inspired by the information propagation feature of the Eikonal equation. Experimental results are presented in a particular medical image segmentation application, and demonstrate the proposed methods

Complementarities of Probabilistic and Evidence Approaches: An Uncertainty Assessment for Selection of Composite Material

A complimentary probabilistic and evidence theory approach is utilized to enhance uncertainty assessments in the area of critical safety characteristics for conceptual design. This research provides additional exploration into the failure modes necessary to utilize Fiber Reinforced Polymer (FRP) and various composites to their fullest potential and to minimize uncertainty by comparing probability and evidence theories. This combined approach has been applied to a selection of composite material that could provide uncertainty assessment design for a space transportation system. Uncertainty estimates presented are bounded by belief and plausibility functions. The results may provide additional information to the decision makers in critical system safety and uncertainty assessments. Benefits and limitations are discussed

An information-theoretic detector based scheme for registration of speckled medical images

Several studies dealt with medical ultrasound registration. Their similarity metrics relied on pixel-to-pixel intensity comparisons. Hence, they are not well suited to the case of speckled images. To better handle the speckle noise, our previous work proposed an information-theoretic feature detector-based registration approach. This work aims to extend it to the cases where the image speckle model is Rayleigh or normalized Fisher-Tippett distributed. Using speckle modeling based on these distributions, a speckle-specific information- theoretic feature detector is constructed and applied to provide feature images. Those feature images are then registered using differential equations, the solution of which provides a transformation to bring the images into alignment. Compared to standard gradient-based techniques, the experimental results demonstrate the effectiveness of our method, particularly for low contrast ultrasound images

Population assessment of future trajectories in coronary heart disease mortality.

Background: Coronary heart disease (CHD) mortality rates have been decreasing in Iceland since the 1980s, largely reflecting improvements in cardiovascular risk factors. The purpose of this study was to predict future CHD mortality in Iceland based on potential risk factor trends. Methods and findings: The previously validated IMPACT model was used to predict changes in CHD mortality between 2010 and 2040 among the projected population of Iceland aged 25–74. Calculations were based on combining: i) data on population numbers and projections (Statistics Iceland), ii) population risk factor levels and projections (Refine Reykjavik study), and iii) effectiveness of specific risk factor reductions (published meta-analyses). Projections for three contrasting scenarios were compared: 1) If the historical risk factor trends of past 30 years were to continue, the declining death rates of past decades would level off, reflecting population ageing. 2) If recent trends in risk factors (past 5 years) continue, this would result in a death rate increasing from 49 to 70 per 100,000. This would reflect a recent plateau in previously falling cholesterol levels and recent rapid increases in obesity and diabetes prevalence. 3) Assuming that in 2040 the entire population enjoys optimal risk factor levels observed in low risk cohorts, this would prevent almost all premature CHD deaths before 2040. Conclusions: The potential increase in CHD deaths with recent trends in risk factor levels is alarming both for Iceland and probably for comparable Western populations. However, our results show considerable room for reducing CHD mortality. Achieving the best case scenario could eradicate premature CHD deaths by 2040. Public health policy interventions based on these predictions may provide a cost effective means of reducing CHD mortality in the future

Perforated Hepatic Hydatid Cyst into the Peritoneum with Mild Symptoms

Rupture into the abdominal cavity is a rare but serious complication of hydatid disease that necessitates emergency surgical intervention. We present herein a case with mild abdominal symptoms due to hydatid cyst rupture into the peritoneum after trauma. A 24-year-old man was admitted to the emergency room with mild abdominal pain. His symptoms had started after a fall four days earlier. Ultrasonography and computed tomography showed cystic lesions in the liver and peritoneum with intraabdominal free fluid. He was treated surgically with partial cystectomy and falciformoplasty. Postoperative albendazole therapy was given for two months. There was not recurrence four months postoperatively at control computed tomography