Daniel Barbosa

Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform

Capsule endoscopy is an important tool to diagnose tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images

BackgroundWireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity.MethodThe set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis.ResultsThe proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions

This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts variability range. The platform remains open for new submissions.

Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.

Left Ventricular Myocardial Segmentation in 3-D Ultrasound Recordings: Effect of Different Endocardial and Epicardial Coupling Strategies

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy.Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Though development of automatic endocardial segmentation methods has received much attention, the same cannot be said about epicardial segmentation, in spite of the importance of full myocardial segmentation. In this paper, different ways of coupling the endocardial and epicardial segmentations are contrasted and compared with uncoupled segmentation. For this purpose, the B-spline explicit active surfaces framework was used; 27 3-D echocardiographic images were used to validate the different coupling strategies, which were compared with manual contouring of the endocardial and epicardial borders performed by an expert. It is shown that an independent segmentation of the endocardium followed by an epicardial segmentation coupled to the endocardium is the most advantageous. In this way, a framework for fully automatic 3-D myocardial segmentation is proposed using a novel coupling strategy.

Left-Atrial Segmentation From 3-D Ultrasound Using B-Spline Explicit Active Surfaces With Scale Uncoupling

Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from three-dimensional (3-D) ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, we propose an extension of the BEAS framework, introducing B-splines with uncoupled scaling. This formulation improves the shape support for less regular and more variable structures, by giving independent control over smoothness and number of control points. Semiautomatic segmentation of the LA endocardium using this framework was tested in a setup requiring little user input, on 20 volumetric sequences of echocardiographic data from healthy subjects. The segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semiautomatic LA segmentation in 3-D transthoracic US, providing reliable quantification of the LA morphology and function.

Cardiac Chamber Volumetric Assessment Using 3D Ultrasound - A Review.

When designing clinical trials for testing novel cardiovascular therapies, it is highly relevant to understand what a given technology can provide in terms of information on the physiologic status of the heart and vessels. Ultrasound imaging has traditionally been the modality of choice to study the cardiovascular system as it has an excellent temporal resolution; it operates in real-time; it is very widespread and - not unimportant - it is cheap. Although this modality is mostly known clinically as a two-dimensional technology, it has recently matured into a true three-dimensional imaging technique. In this review paper, an overview is given of the available ultrasound technology for cardiac chamber quantification in terms of volume and function and evidence is given why these parameters are of value when testing the effect of new cardiovascular therapies.

Aortic valve tract segmentation from 3D-TEE using shape-based B-spline explicit active surfaces

A novel semi-automatic algorithm for aortic valve (AV) wall segmentation is presented for 3D transesophageal echocardiography (TEE) datasets. The proposed methodology uses a 3D cylindrical formulation of the B-spline Explicit Active Surfaces (BEAS) framework in a dual-stage energy evolution process, comprising a threshold-based and a localized region-based stage. Hereto, intensity and shape-based features are combined to accurately delineate the AV wall from the ascending aorta (AA) to the left ventricular outflow tract (LVOT). Shape-prior information is included using a profile-based statistical shape model (SSM), and embedded in BEAS through two novel regularization terms: one confining the segmented AV profiles to shapes seen in the SSM (hard regularization) and another penalizing according to the profiles degree of likelihood (soft regularization). The proposed energy functional takes thus advantage of the intensity data in regions with strong image content, while complementing it with shape knowledge in regions with nearly absent image data. The proposed algorithm has been validated in 20 3D-TEE datasets with both stenotic and non-stenotic valves. It was shown to be accurate, robust and computationally efficient, taking less than 1 second to segment the AV wall from the AA to the LVOT with an average accuracy of 0.78 mm. Semi-automatically extracted measurements at four relevant anatomical levels (LVOT, aortic annulus, sinuses of Valsalva and sinotubular junction) showed an excellent agreement with experts ones, with a higher reproducibility than manually-extracted measures.

Automatic segmentation of the second cardiac sound by using wavelets and hidden Markov models

This paper is concerned with the segmentation of the second heart sound (S2) of the phonocardiogram (PCG), in its two acoustic events, aortic (A2) and pulmonary (P2) components. The aortic valve (A2) usually closes before the pulmonary valve (P2) and the delay between these two events is known as “split” and is typically less than 30 miliseconds. S2 splitting, reverse splitting or reverse occurrence of components A2 and P2 are the most important aspects regarding cardiac diagnosis carried out by the analysis of S2 cardiac sound. An automatic technique, based on discrete wavelet transform and hidden Markov models, is proposed in this paper to segment S2, to estimate de order of occurrence of A2 and P2 and finally to estimate the delay between these two components (split). A discrete density hidden Markov model (DDHMM) is used for phonocardiogram segmentation while embedded continuous density hidden Markov models are used for acoustic models, which allows segmenting S2. Experimental results were evaluated on data collected from five different subjects, using CardioLab system and a Dash family patient monitor. The ECG leads I, II and III and an electronic stethoscope signal were sampled at 977 samples per second.