Rui Rocha

Segmentation of the carotid intima-media region in B-mode ultrasound images

This paper proposes a new approach for the segmentation of both near-end and far-end intima-media regions of the common carotid artery in ultrasound images. The method requires minimal user interaction and is able to segment the near-end wall in arteries with large, hypoechogenic and irregular plaques, issues usually not considered previously due to the increased segmentation difficulty. The adventitia is detected by searching for the best fit of a cubic spline to edges having features compatible with the adventitia boundary. The algorithm uses a global smoothness constraint and integrates discriminating features of the adventitia to reduce the attraction by other edges. Afterwards, using the information of the adventitia location, the lumen boundary is detected by combining dynamic programming, smooth intensity thresholding surfaces and geometric snakes. Smooth contours that correctly adapt to the intima are produced, even in the presence of deep concavities. Moreover, unlike balloon-based snakes, the propagation force does not depend on gradients and does not require a predefined direction. An extensive statistical evaluation is computed, using a set of 47 images from 24 different symptomatic patients, including several classes, sizes and shapes of plaques. Bland-Altman plots of the mean intima-media thickness, for manual segmentations of two medical experts, show a high intra-observer and inter-observer agreement, with mean differences close to zero (mean between -0.10mm and 0.18mm) and with the large majority of differences within the limits of agreement (standard deviation between 0.10mm and 0.12mm). Similar plots reveal a good agreement between the automatic and the manual segmentations (mean between -0.07mm and 0.11mm and standard deviation between 0.11mm and 0.12mm).

Segmentation of ultrasound images of the carotid using RANSAC and cubic splines

A new algorithm is proposed for the semi-automatic segmentation of the near-end and the far-end adventitia boundary of the common carotid artery in ultrasound images. It uses the random sample consensus method to estimate the most significant cubic splines fitting the edge map of a longitudinal section. The consensus of the geometric model (a spline) is evaluated through a new gain function, which integrates the responses to different discriminating features of the carotid boundary: the proximity of the geometric model to any edge or to valley shaped edges; the consistency between the orientation of the normal to the geometric model and the intensity gradient; and the distance to a rough estimate of the lumen boundary. A set of 50 longitudinal B-mode images of the common carotid and their manual segmentations performed by two medical experts were used to assess the performance of the method. The image set was taken from 25 different subjects, most of them having plaques of different classes (class II to class IV), sizes and shapes. The quantitative evaluation showed promising results, having detection errors similar to the ones observed in manual segmentations for 95% of the far-end boundaries and 73% of the near-end boundaries.

Detection of Escherichia coli O157 by Peptide Nucleic Acid Fluorescence In Situ Hybridization (PNA-FISH) and Comparison to a Standard Culture Method

ABSTRACT Despite the emergence of non-O157 Shiga toxin-producing Escherichia coli (STEC) infections, E. coli serotype O157 is still the most commonly identified STEC in the world. It causes high morbidity and mortality and has been responsible for a number of outbreaks in many parts of the world. Various methods have been developed to detect this particular serotype, but standard bacteriological methods remain the gold standard. Here, we propose a new peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) method for the rapid detection of E. coli O157. Testing on 54 representative strains showed that the PNA probe is highly sensitive and specific to E. coli O157. The method then was optimized for detection in food samples. Ground beef and unpasteurized milk samples were artificially contaminated with E. coli O157 concentrations ranging from 1 × 10−2 to 1 × 102 CFU per 25 g or ml of food. Samples were then preenriched and analyzed by both the traditional bacteriological method (ISO 16654:2001) and PNA-FISH. The PNA-FISH method performed well in both types of food matrices with a detection limit of 1 CFU/25 g or ml of food samples. Tests on 60 food samples have shown a specificity value of 100% (95% confidence interval [CI], 82.83 to 100), a sensitivity of 97.22% (95% CI, 83.79 to 99.85%), and an accuracy of 98.33% (CI 95%, 83.41 to 99.91%). Results indicate that PNA-FISH performed as well as the traditional culture methods and can reduce the diagnosis time to 1 day.

Automatic detection of the carotid lumen axis in B-mode ultrasound images.

A new approach is introduced for the automatic detection of the lumen axis of the common carotid artery in B-mode ultrasound images. The image is smoothed using a Gaussian filter and then a dynamic programming scheme extracts the dominant paths of local minima of the intensity and the dominant paths of local maxima of the gradient magnitude with the gradient pointing downwards. Since these paths are possible estimates of the lumen axis and the far wall of a blood vessel, respectively, they are grouped together into pairs. Then, a pattern of two features is computed from each pair of paths and used as input to a linear discriminant classifier in order to select the pair of paths that correspond to the common carotid artery. The estimated lumen axis is the path of local minima of the intensity that belongs to the selected pair of paths. The proposed method is suited to real time processing, no user interaction is required and the number of parameters is minimal and easy to determine. The validation was performed using two datasets, with a total of 199 images, and has shown a success rate of 99.5% (100% if only the carotid regions for which a ground truth is available are considered). The datasets have a large diversity of images, including cases of arteries with plaque and images with heavy noise, text or other graphical markings inside the artery region.

Optimization of peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) for the detection of bacteria: The effect of pH, dextran sulfate and probe concentration

Fluorescence in situ hybridization (FISH) is a molecular technique widely used for the detection and characterization of microbial populations. FISH is affected by a wide variety of abiotic and biotic variables and the way they interact with each other. This is translated into a wide variability of FISH procedures found in the literature. The aim of this work is to systematically study the effects of pH, dextran sulfate and probe concentration in the FISH protocol, using a general peptide nucleic acid (PNA) probe for the Eubacteria domain. For this, response surface methodology was used to optimize these 3 PNA-FISH parameters for Gram-negative (Escherichia coli and Pseudomonas fluorescens) and Gram-positive species (Listeria innocua, Staphylococcus epidermidis and Bacillus cereus). The obtained results show that a probe concentration higher than 300nM is favorable for both groups. Interestingly, a clear distinction between the two groups regarding the optimal pH and dextran sulfate concentration was found: a high pH (approx. 10), combined with lower dextran sulfate concentration (approx. 2% [w/v]) for Gram-negative species and near-neutral pH (approx. 8), together with higher dextran sulfate concentrations (approx. 10% [w/v]) for Gram-positive species. This behavior seems to result from an interplay between pH and dextran sulfate and their ability to influence probe concentration and diffusion towards the rRNA target. This study shows that, for an optimum hybridization protocol, dextran sulfate and pH should be adjusted according to the target bacteria.

Segmentation of ultrasonic images of the carotid

A new algorithm for an effective and automatic segmentation of the carotid wall in ultrasonic images is proposed. It combines the speed of thresholding algorithms with the accuracy, flexibility and robustness of a successful geometric active contour model which incorporates an optimal image segmentation model in a level set framework. Due to the multiphase nature of these images, a sequential minimum cross entropy thresholding is used to get a first approximation of the segments, reducing the problem to a two phase segmentation. This thresholding solution is then used as a starting point for a two phase piecewise constant version of a geometric active contour model to reduce noise, smooth contours, improve their position accuracy and close eventual gaps in the carotid wall.

Novel strategy to detect and locate periodontal pathogens: The PNA-FISH technique

PURPOSE We aim to develop peptic nucleic acid (PNA) probes for the identification and localization of Aggregatibacter actinomycetemcomintans and Porphyromonas gingivalis in sub-gingival plaque and gingival biopsies by Fluorescence in situ Hybridization (FISH). METHODS A PNA probe was designed for each microorganism. The PNA-FISH method was optimized to allow simultaneous hybridization of both microorganisms with their probe (PNA-FISH multiplex). After being tested on representative strains of P. gingivalis and A. actinomycetemcomitans, the PNA-FISH method was then adapted to detect microorganisms in the subgingival plaque and gingival samples, collected from patients with severe periodontitis. RESULTS The best hybridization conditions were found to be 59°C for 150min for both probes (PgPNA1007 and AaPNA235). The in silico sensitivity and specificity was both 100% for PgPNA1007 probe and 100% and 99.9% for AaPNA235 probe, respectively. Results on clinical samples showed that the PNA-FISH method was able to detect and discriminate target bacteria in the mixed microbial population of the subgingival plaque and within periodontal tissues. CONCLUSION This investigation presents a new highly accurate method for P. gingivalis and A. actinomycetemcomitans detection and co-location in clinical samples, in just few hours. With this technique we were able to observe spatial distribution of these species within polymicrobial communities in the periodontal pockets and, for the first time with the FISH method, in the organized gingival tissue.

Segmentation of Carotid Ultrasound Images

This chapter surveys methodologies for the segmentation of carotid ultrasound images and describes a method for the semiautomatic detection of the lumen–intima and the media–adventitia interfaces of the near and far common carotid wall. The approach is based on feature extraction, fitting of cubic splines, dynamic programming, smooth intensity thresholding surfaces, and geometric snakes. A set of 47 B-mode images of the common carotid were used to assess the performance of the method. The detection errors are similar to the ones observed in manual segmentations for 95% of the far wall interfaces and 73% of the near wall interfaces.

Classification Approach for Measurement of Atherosclerosis Using B-Mode Ultrasound Carotid Images

This paper presents an approach for the detection and delineation of the interfaces at the near and far walls of carotid artery using B-mode ultrasound images. After the delineation the system measures automatically the carotid intima-media thickness (IMT) in order to aid the diagnosis of the atherosclerosis. In this method we start by the measurement, at a pixel level, of local features followed by the selection of the most discriminant ones. The next stage is a classification step which assigns a probability to the pixels to belong to an interface, enabling the detection of the carotid artery interfaces. The final artery boundaries are delineated using a dynamic programming approach. The final measurements of IMT produced by the automatic method proposed in this paper were compared with three manual tracings of experts. It was also compared with an automatic method previously developed. The results show that the two automatic detection methods have similar performance, although with slight improvements in the new method, particularly for the the far wall interface.

Joint Detection of the Carotid Boundaries in Longitudinal B-Mode Images

This paper proposes a new approach for the automatic segmentation of the carotid adventitia in longitudinal B-scans, with and without the presence of plaque. The top and bottom adventitia contours are jointly detected with a 3D dynamic programing scheme that searches for the best pair of boundaries according to a specified fuzzy cost function. Some discriminating features of the adventitia in B-mode images are used to reduce the attraction by other edges. The final contours are filtered with a smoothing spline fitting. The proposed approach was quantitatively evaluated in a set of 38 images. In order to avoid high correlation of the results, a maximum of two images was selected from each patient. The carotid boundaries manually traced by a medical expert were used as the ground truth. Several statistics show that the proposed algorithm gives good results in most of the cases, including many poor quality images. Examples of the detected contours are presented and compared with the ground truth.