Several abnormalities of the shape of lung fields (depression and flattening of the diaphragmatic contours, increased retrosternal space) are indicative of emphysema and can be accurately imaged by digital chest radiography. In this work, we aimed at developing computational descriptors of the shape of the lung silhouette able to capture the alterations associated with emphysema. We analyzed two-sided digital chest radiographs from a sample of 160 patients with chronic obstructive pulmonary disease (COPD), 60 of which were affected by emphysema, and from 160 subjects with normal lung function. Two different description schemes were considered: a first one based on lung-silhouette curvature features, and a second one based on a minimal-polyline approximation of the lung shape. Both descriptors were employed to recognize alterations of the lung shape using classifiers based on multilayer neural networks of the feed-forward type.Results indicate that pulmonary emphysema can be reliably diagnosed or excluded by using digital chest radiographs and a proper computational aid. Two-sided chest radiographs provide more accurate discrimination than single-view analysis. The minimal-polyline approximation provided significantly better results than those obtained from curvature-based features. Emphysema was detected, in the entire dataset, with an accuracy of about 90% (sensitivity 88%, specificity 90%) by using the minimal-polyline approximation.

a b s t r a c tThe purpose of this work is twofold: (i) to develop a CAD system for the assessment of emphysema by digital chest radiography and (ii) to test it against CT imaging. The system is based on the analysis of the shape of lung silhouette as imaged in standard chest examination. Postero-anterior and lateral views are processed to extract the contours of the lung fields automatically. Subsequently, the shape of lung silhouettes is described by polyline approximation and the computed feature-set processed by a neural network to estimate the probability of emphysema.Images of radiographic studies from 225 patients were collected and properly annotated to build an experimental dataset named EMPH. Each patient had undergone a standard two-views chest radiography and CT for diagnostic purposes. In addition, the images (247) from JSRT dataset were used to evaluate lung segmentation in postero-anterior view.System performances were assessed by: (i) analyzing the quality of the automatic segmentation of the lung silhouette against manual tracing and (ii) measuring the capabilities of emphysema recognition. As to step i, on JSRT dataset, we obtained overlap percentage (˝) 92.7 ± 3.3%, Dice Similarity Coefficient (DSC) 95.5 ± 3.7% and average contour distance (ACD) 1.73 ± 0.87 mm. On EMPH dataset we had ˝ = 93.1 ± 2.9%, DSC = 96.1 ± 3.5% and ACD = 1.62 ± 0.92 mm, for the postero-anterior view, while we had ˝ = 94.5 ± 4.6%, DSC = 91.0 ± 6.3% and ACD = 2.22 ± 0.86 mm, for the lateral view. As to step ii, accuracy of emphysema recognition was 95.4%, with sensitivity and specificity 94.5% and 96.1% respectively. According to experimental results our system allows reliable and inexpensive recognition of emphysema on digital chest radiography.

Altered myocardial texture associated with inflammatory infiltration or fibrosis of the myocardium has already been described using qualitative and subjective analysis of two-dimensional echocardiograms. The aim of this work is to test whether quantitative analysis of regional image texture in two-dimensional echocardiograms would be an accurate method to identify myocarditis and myocardial fibrosis. A set of 20 two-dimensional studies with endomyocardial biopsy evaluation was examined in 13 patients. Biopsy-proven myocarditis was present in 8 studies; myocarditis and fibrosis in 4; fibrosis in 3; healing/healed myocarditis in 5. A control group of 8 normal subjects was also studied by echocardiography. After quantitative texture analysis of the first order, entropy appeared to consistently differentiate myocarditis from controls. Among second-order parameters, patients affected by myocarditis or fibrosis showed a decreased entropy and higher angular second moment versus controls. We conclude that myocarditis and fibrosis induce similar image texture alterations in ultrasonic images, with increased spatial heterogeneity of the gray level distribution, which can be differentiated from normal structures with digital image analysis techniques.

Qualitative and subjective analysis of two-dimensional echocardiographic images of the myocardial wall allows one to identify amyloid heart disease; the quantitative analysis of regional image texture might be an accurate method to differentiate normal from amyloid myocardial structures. To test this hypothesis, two-dimensional echocardiograms of nine normal subjects and six patients with histologically documented amyloid heart disease were evaluated. Quantitative texture measurements of the first order (mean gray level, skewness, kurtosis, energy and entropy) overlapped between the two groups. Among the second order statistics variables, entropy was significantly and consistently higher in amyloid versus normal patient data (septum in parasternal long-axis view: 6.3 +/- 0.3 versus 5.9 +/- 0.4; septum in apical four chamber view: 6.2 +/- 0.2 versus 5.8 +/- 0.3). Therefore, amyloid-involved myocardial walls show ultrasound image texture alterations that may be quantified with digital image analysis techniques.

Two-dimensional echocardiography is the best means of identifying early cardiac amyloid infiltration and gauging its subsequent progression. The early asymptomatic phase is characterized on echocardiography by a mild-to-moderate increase in left ventricular and/or right ventricular wall thicknesses. The distinctive combination of low electrocardiography voltage and increase in left ventricular mass on the echocardiogram, both compatible with substantial amyloid infiltration, is valuable in diagnosis and appears to indicate the severity of the disease. Other ancillary but common findings are left atrial dilatation, a small pericardial effusion, thickening of cardiac valves, papillary muscles, and interatrial septum. Finally, there is a peculiar texture of myocardial walls, with highly refractile areas that are typical, although not specific, of myocardial amyloidosis and can also be quantitatively described by digital image analysis techniques. The echocardiographic appearance of amyloidosis can closely mimic several other diseases. Asymmetric hypertrophy of the septum due to amyloid deposition may occur, simulating hypertrophic cardiomyopathy. The granular sparkling of myocardial walls is also found in myocarditis with severe fibrosis, and it is quite common in hypertrophic cardiomyopathy, as well as in other infiltrative diseases of the myocardium. It is not uncommon that the echocardiographic examination represents a turning point in the work-up of the patient, briskly orienting the clinician towards the correct diagnostic pathway. However, the likelihood of the cardiologist-echocardiographer to successfully and prospectively identify myocardial amyloidosis is substantially higher if all the clinical and electrocardiographic information is reviewed at the time of the echocardiographic examination.

Vardenafil enhanced power Doppler ultrasound enables excellent visualization of the microvasculature associated with cancer and can improve the detection rate compared to contrast enhanced power Doppler ultrasound and the random technique.

a b s t r a c tBackground: Computed tomography (CT) is the benchmark for diagnosis emphysema, but is costly and imparts a substantial radiation burden to the patient. Objective: To develop a computer-aided procedure that allows recognition of emphysema on digital chest radiography by using simple descriptors of the lung shape. The procedure was tested against CT. Methods: Patients (N = 225), who had undergone postero-anterior and lateral digital chest radiographs and CT for diagnostic purposes, were studied and divided in a derivation (N = 118) and in a validation sample (N = 107).CT images were scored for emphysema using the picture-grading method. Simple descriptors that measure the bending characteristics of the lung profile on chest radiography were automatically extracted from the derivation sample, and applied to train a neural network to assign a probability of emphysema between 0 and 1. The diagnostic performance of the procedure was described by the area under the receiver operating characteristic curve (AUC). Results: AUC was 0.985 (95% confidence interval, 0.965-0.998) in the derivation sample, and 0.975 (95% confidence interval, 0.936-0.998) in the validation sample. At a probability cutpoint of 0.55, the procedure yielded 92% sensitivity and 96% specificity in the derivation sample; 90% sensitivity and 97% specificity in the validation sample. False negatives on chest radiography had trace or mild emphysema on CT. Conclusions: The computer-aided procedure is simple and inexpensive, and permits quick recognition of emphysema on digital chest radiographs.

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