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BACKGROUND AND PURPOSE: Inflammatory changes in the fat tissue surrounding the coronary arteries have been associated with coronary artery disease and high-risk vulnerable plaques. Our aim was to investigate possible correlations between the presence and degree of perivascular fat density and a marker of vulnerable carotid plaque, namely contrast plaque enhancement on CTA. MATERIALS AND METHODS: One-hundred patients (76 men, 24 women; mean age, 69 years) who underwent CT angiography for investigation of carotid artery stenosis were retrospectively analyzed. Contrast plaque enhancement and perivascular fat density were measured in 100 carotid arteries, and values were stratified according to symptomatic (ipsilateral-to-cerebrovascular symptoms)/asymptomatic status (carotid artery with the most severe degree of stenosis). Correlation coefficients (Pearson ρ product moment) were calculated between the contrast plaque enhancement and perivascular fat density. The differences among the correlation ρ values were calculated using the Fisher r-to-z transformation. Mann-Whitney analysis was also calculated to test differences between the groups. RESULTS: There was a statistically significant positive correlation between contrast plaque enhancement and perivascular fat density (ρ value = 0.6582, P value = .001). The correlation was stronger for symptomatic rather than asymptomatic patients (ρ value = 0.7052, P value = .001 versus ρ value = 0.4092, P value = .001). CONCLUSIONS: There was a positive association between perivascular fat density and contrast plaque enhancement on CTA. This correlation was stronger for symptomatic rather than asymptomatic patients. Our results suggest that perivascular fat density could be used as an indirect marker of plaque instability.

BACKGROUND AND PURPOSE: Our aim was to assess the relationship between volume and percentage of intraplaque hemorrhage measured using CT and the occurrence of cerebrovascular events at the time of CT. MATERIALS AND METHODS: One-hundred-twenty-three consecutive subjects (246 carotid arteries) with a mean age of 69 years who underwent CTA were included in this retrospective study. Plaque volume of components and subcomponents (including intraplaque hemorrhage volume) was quantified with dedicated software. RESULTS: Forty-six arteries were excluded because no plaque was identified. In the remaining 200 carotid arteries, a statistically significant difference was found between presentation with cerebrovascular events and lipid volume (P = .002), intraplaque hemorrhage volume (P = .002), percentage of lipid (P = .002), percentage of calcium (P = .001), percentage of intraplaque hemorrhage (P = .001), percentage of lipid-intraplaque hemorrhage (P = .001), and intraplaque hemorrhage/lipid ratio (P = .001). The highest receiver operating characteristic area under the curve was obtained with the intraplaque hemorrhage volume with a value of 0.793 (P = .001), percentage of intraplaque hemorrhage with an area under the curve of 0.812 (P = .001), and the intraplaque hemorrhage/lipid ratio with an area under the curve value of 0.811 (P = .001). CONCLUSIONS: Results of our study suggest that Hounsfield unit values

BACKGROUND AND PURPOSE: The presence of IPH is considered the most dangerous feature because it is significantly associated with clinical ipsilateral cerebrovascular events. Our aim was to explore the characterization of plaque with CT in symptomatic subjects with bilateral intraplaque hemorrhage. MATERIALS AND METHODS: Three-hundred-forty-three consecutive patients with recent anterior circulation ischemic events (

BACKGROUND AND PURPOSE: Intraplaque hemorrhage is considered a leading parameter of carotid plaque vulnerability. Our purpose was to assess the CT characteristics of intraplaque hemorrhage with histopathologic correlation to identify features that allow for confirming or ruling out the intraplaque hemorrhage. MATERIALS AND METHODS: This retrospective study included 91 patients (67 men; median age, 65 ± 7 years; age range, 41–83 years) who underwent CT angiography and carotid endarterectomy from March 2010 to May 2013. Histopathologic analysis was performed for the tissue characterization and identification of intraplaque hemorrhage. Two observers assessed the plaque9s attenuation values by using an ROI (≥ 1 and ≤2 mm2). Receiver operating characteristic curve, Mann-Whitney, and Wilcoxon analyses were performed. RESULTS: A total of 169 slices were assessed (59 intraplaque hemorrhage, 63 lipid-rich necrotic core, and 47 fibrous); the average values of the intraplaque hemorrhage, lipid-rich necrotic core, and fibrous tissue were 17.475 Hounsfield units (HU) and 18.407 HU, 39.476 HU and 48.048 HU, and 91.66 HU and 93.128 HU, respectively, before and after the administration of contrast medium. The Mann-Whitney test showed a statistically significant difference of HU values both in basal and after the administration of contrast material phase. Receiver operating characteristic analysis showed a statistical association between intraplaque hemorrhage and low HU values, and a threshold of 25 HU demonstrated the presence of intraplaque hemorrhage with a sensitivity and specificity of 93.22% and 92.73%, respectively. The Wilcoxon test showed that the attenuation of the plaque before and after administration of contrast material is different (intraplaque hemorrhage, lipid-rich necrotic core, and fibrous tissue had P values of .006, .0001, and .018, respectively). CONCLUSIONS: The results of this preliminary study suggest that CT can be used to identify the presence of intraplaque hemorrhage according to the attenuation. A threshold of 25 HU in the volume acquired after the administration of contrast medium is associated with an optimal sensitivity and specificity. Special care should be given to the correct identification of the ROI.

This paper overviews one of the most important, interesting, and challenging problems in oncology, early diagnosis of prostate cancer. Developing effective diagnostic techniques for prostate cancer is of great clinical importance and can improve the effectiveness of treatment and increase the patient's chance of survival. The main focus of this study is to overview the different in-vitro and in-vivo technologies for diagnosing prostate cancer. This review discusses the current clinically used in-vitro cancer diagnostic tools, such as biomarker tests and needle biopsies and including their applications, advantages, and limitations. Moreover, the current in-vitro research tools that focus on the role of nanotechnology in prostate cancer diagnosis have been detailed. In addition to the in-vitro techniques, the current study discusses in detail developed in-vivo non-invasive state-of-the-art Computer-Aided Diagnosis (CAD) systems for prostate cancer based on analyzing Transrectal Ultrasound (TRUS) and different types of magnetic resonance imaging (MRI), e.g., T2-MRI, Diffusion Weighted Imaging (DWI), Dynamic Contrast Enhanced (DCE)-MRI, and multi-parametric MRI, focusing on their implementation, experimental procedures, and reported outcomes. Furthermore, the paper addresses the limitations of the current prostate cancer diagnostic techniques, outlines the challenges that these techniques face, and introduces the recent trends to solve these challenges, which include biomarkers used in in-vitro lab-on-a-chip nanotechnology-based methods.

PurposeTo test a computer-aided diagnostic method for differentiating symptomatic from asymptomatic carotid B-mode ultrasonographic images. MethodsOur system (called Atheromatic) automatically computed the intima-media thickness (IMT) of the carotid far wall using AtheroEdge, calculated nonlinear features based on higher order spectra, and used these features and IMT and IMT variability (IMTVpoly) to associate each image to a feature vector that was then labeled as symptomatic or asymptomatic (Sym/Asym) by a multiclassifiers system. We tested this method on a database of 118 carotid artery images from 37 symptomatic and 22 asymptomatic patients ResultsThe highest accuracy (99.1) was obtained by the support vector machine classifier using seven features. These features, relevant to discriminate Sym/Asym, included IMT and IMTVpoly, along with the bispectral entropies of the distal wall image at 77 degrees, 78 degrees, and 79 degrees angles. ConclusionsClassification in Sym/Asym of the far carotid wall is feasible and accurate and could be useful for the early detection of atherosclerosis and to identify patients with higher cardiovascular risk. (c) 2014 Wiley Periodicals, Inc. J Clin Ultrasound 43:302-311, 2015

The selection of carotid atherosclerosis patients for surgery or stenting is a crucial task in atherosclerosis disease management. In order to select only those symptomatic cases who need surgery, we have, in this work, presented a computer-aided diagnostic technique to effectively classify symptomatic and asymptomatic plaques from B-mode ultrasound carotid images. We extracted several grayscale features that quantify the textural differences inherent in the manually delineated plaque regions and selected the most significant among these extracted features. These features, along with the degree of stenosis (DoS), were used to train and test a support vector machine (SVM) classifier using threefold stratified cross-validation using a data set consisting of 160 (50 symptomatic and 110 asymptomatic) images. Using 32 features in an SVM classifier with a polynomial kernel of order 1, we obtained the best accuracy of 90.66%, sensitivity of 83.33%, and specificity of 95.39%. The DoS was found to be a valuable feature in addition to other texture-based features. We have also proposed the plaque risk index (PRI) made up of a combination of significant features such that the PRI has unique ranges for both plaque classes. PRI can be used in monitoring the variations in features over a period of time which will provide evidence on how and which features change as asymptomatic plaques become symptomatic.

The carotid intima-media thickness (IMT) is a validated marker of cerebrovascular disease risk. This paper presents a new parameter, the IMT variability (IMTV), and compares it between symptomatic and asymptomatic patients taken from a cohort of Italian population. One hundred forty-two patients were analyzed (age 59 ± 112 years, 59% males), 42 of these patients suffered from TIA or minor stroke. The lumen-intima (LI) and media-adventitia (MA) interfaces of the far wall were manually traced by a Reader. We also used a computer-based automated system (called AutoEdge) to obtain the LI/MA interfaces. The LI/MA interfaces were used to measure the IMT and the IMTV along the distal wall of the common carotid artery. Wilcoxon and Pearson correlation analyses were performed. The agreement between the Reader's IMT and the AutoEdge IMT values was 98.7% for the symptomatic (0.83 ± 0.44 mm for Reader, 0.82 ± 0.35 mm for AutoEdge) and 94.9% for the asymptomatic patients (0.78 ± 0.45 mm for Reader, 0.74 ± 0.30 mm for AutoEdge). Correlation was 65% for symptomatic and 68% for asymptomatic patients, respectively. The IMT measured using AutoEdge was 1.2% lower compared to manual measurements in symptomatic population, while 5.12% lower in asymptomatic. The IMTV was 11% higher in symptomatic patients compared to asymptomatic when using manual delineations, 8% higher when using AutoEdge. There was no statistical difference between the manual and automated IMTV measurements (Wilcoxon signed rank, P > 0.7). We conclude that the IMT and IMTV values were very similar between Reader and AutoEdge software when studying symptomatic and asymptomatic patients in Italian population.

BACKGROUND AND PURPOSE: CMBs have become increasingly recognized with the widespread use of MR imaging techniques that are sensitive to iron deposits. The purpose of this study was to correlate the presence of CMBs and carotid plaque characteristics. MATERIAL AND METHODS: Seventy consecutive patients (47 men; 23 women; mean age, 65 years) were prospectively analyzed. Carotid arteries were studied using a 16-detector row CT scanner, whereas the brain was explored with an MR imaging 1.5T system. CMBs were studied using a T2*-weighted GRE sequence. CMBs were classified by an ordinal scale and carotid plaques were characterized based on their composition as fatty, mixed, or calcified. Patients were classified as symptomatic and asymptomatic. Chi-square and multiple logistic regression analyses, as well as ROCs, were calculated. RESULTS: The prevalence of CMBs was 30%. A statistically significant difference in CMB prevalence was observed between symptomatic (46%) and asymptomatic (19%) patients (P value = .0021; OR = 3.7). Correlation analysis demonstrated an association between the number of CMBs and the symptoms (P = .0001). A statistically significant association was observed between the presence of fatty plaque and CMBs (P = .0019). CONCLUSIONS: The results of this study suggest an association between the presence of carotid artery fatty plaque, symptoms, and CMBs. Moreover, we found that the presence (and entity) of CMBs may represent an indicator of cerebrovascular symptom severity.

SUMMARY: The purpose of this work was to compare inter- and intraobserver agreement in the analysis of CAWT by using MDCTA. The CAWT in 35 patients was quantified by 4 observers. Bland-Altman statistics were used to measure the agreement between observers. The results of our study demonstrated that the CAWT measured by using MDCTA shows a good reproducibility between observers by considering inter- and intraobserver agreement.

In this paper, we review the different studies that developed Computer Aided Diagnostic (CAD) for automated classification of thyroid cancer into benign and malignant types. Specifically, we discuss the different types of features that are used to study and analyze the differences between benign and malignant thyroid nodules. These features can be broadly categorized into (a) the sonographic features from the ultrasound images, and (b) the non-clinical features extracted from the ultrasound images using statistical and data mining techniques. We also present a brief description of the commonly used classifiers in ultrasound based CAD systems. We then review the studies that used features based on the ultrasound images for thyroid nodule classification and highlight the limitations of such studies. We also discuss and review the techniques used in studies that used the non-clinical features for thyroid nodule classification and report the classification accuracies obtained in these studies.

Intima-Media Thickness (IMT) is now being considered as an indicator of atherosclerosis. Our group has developed several feature-based IMT measurement algorithms such as CALEX (a class of patented AtheroEdge™ Systems from Global Biomedical Technologies, Inc., CA, USA). These methods are based on the hypothesis that the highest pixel intensities are in the far wall of the Common Carotid Artery (CCA) or the Internal Carotid Artery (ICA). In this work, we verify that this hypothesis holds true for B-mode longitudinal ultrasound images of the carotid wall. This patented methodology consists of generating the composite image (arithmetic sum of images) from the database by first registering the carotid image frames with respect to a nearly straight carotid artery frame from the same database using (a) B-spline based nonrigid registration and (b) affine registration. Prior to registration, we segment the carotid artery lumen using a level set based algorithm followed by morphological image processing. The binary lumen images are registered and the transformations are applied to the original grayscale CCA images. We evaluated our technique using a database of 200 common carotid images of normal and pathologic carotids. The composite image presented the highest intensity distribution in the far wall of the CCA/ICA, validating our hypothesis. We have also demonstrated the accuracy and improvement in IMT segmentation result with our CALEX 3.0 system. The CALEX system, when run on newly acquired ultrasound images, shows the IMT error of about 30 μm. Thus, we have shown that the CALEX algorithm is able to exploit the far wall brightness for accurate IMT measurements

In 30% of stroke victims, the cause of stroke has been found to be the stenosis caused by plaques in the carotid artery. Early detection of plaque and subsequent classification of the same into symptomatic and asymptomatic can help the clinicians to choose only those patients who are at a higher risk of stroke for risky surgeries and stenosis treatments. Therefore, in this work, we have proposed a non-invasive computer-aided diagnostic technique to classify the detected plaque into the two classes. Computed tomography (CT) images of the carotid artery images were used to extract Local Binary Pattern (LBP) features and wavelet energy features. Significant features were then used to train and test several supervised learning algorithm based classifiers. The Support Vector Machine (SVM) classifier with various kernel configurations was evaluated using LBP and wavelet features. The SVM classifier presented the highest accuracy of 88%, sensitivity of 90.2%, and specificity of 86.5% for radial basis function (RBF) kernel function. The CT images of the carotid artery provide unique 3D images of the artery and plaque that could be used for calculating percentage of stenosis. Our proposed technique enables automatic classification of plaque into asymptomatic and symptomatic with high accuracy, and hence, it can be used for deciding the course of treatment. We have also proposed a single-valued integrated index ( Atheromatic Index) using the significant features which can provide a more objective and faster prediction of the class.

Hashimoto’s thyroiditis is the most common type of inflammation of the thyroid gland, and accurate diagnosis of Hashimoto’s thyroiditis would be helpful to better manage the disease process and predict thyroid failure. Most of the published computer-based techniques that use ultrasound thyroid images for Hashimoto’s thyroiditis diagnosis are limited by lack of procedure standardization because individual investigators use various initial ultrasound settings. This article presents a computer-aided diagnostic technique that uses grayscale features and classifiers to provide a more objective and reproducible classification of normal and Hashimoto’s thyroiditis-affected cases. In this paradigm, we extracted grayscale features based on entropy, Gabor wavelet, moments, image texture, and higher order spectra from the 100 normal and 100 Hashimoto’s thyroiditis-affected ultrasound thyroid images. Significant features were selected using t-test. The resulting feature vectors were used to build the following three classifiers using tenfold stratified cross validation technique: support vector machine, k-nearest neighbor, and radial basis probabilistic neural network. Our results show that a combination of 12 features coupled with support vector machine classifier with the polynomial kernel of order 1 and linear kernel gives the highest accuracy of 80%, sensitivity of 76%, specificity of 84%, and positive predictive value of 83.3% for the detection of Hashimoto’s thyroiditis. The proposed computer-aided diagnostic system uses novel features that have not yet been explored for Hashimoto’s thyroiditis diagnosis. Even though the accuracy is only 80%, the presented preliminary results are encouraging to warrant analysis of more such powerful features on larger databases.

Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Image mining algorithms have been predominantly used to give the physicians a more objective, fast, and accurate second opinion on the initial diagnosis made from medical images. The objective of this work is to develop an adjunct computer-aided diagnostic technique that uses 3D ultrasound images of the ovary to accurately characterize and classify benign and malignant ovarian tumors. In this algorithm, we first extract features based on the textural changes and higher-order spectra information. The significant features are then selected and used to train and evaluate the decision tree (DT) classifier. The proposed technique was validated using 1,000 benign and 1,000 malignant images, obtained from ten patients with benign and ten with malignant disease, respectively. On evaluating the classifier with tenfold stratified cross validation, the DT classifier presented a high accuracy of 97 %, sensitivity of 94.3 %, and specificity of 99.7 %. This high accuracy was achieved because of the use of the novel combination of the four features which adequately quantify the subtle changes and the nonlinearities in the pixel intensity variations. The rules output by the DT classifier are comprehensible to the end-user and, hence, allow the physicians to more confidently accept the results. The preliminary results show that the features are discriminative enough to yield good accuracy. Moreover, the proposed technique is completely automated, accurate, and can be easily written as a software application for use in any computer.

Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.

Computer-aided diagnosis (CAD) of carotid atherosclerosis into symptomatic or asymptomatic is useful in the analysis of cardiac health. This paper describes a patented CAD system called Atheromatic™ for symptomatic versus asymptomatic plaque classification in carotid ultrasound images. The system involves two steps: 1) feature extraction using a combination of discrete wavelet transform and averaging algorithms and 2) classification using a support vector machine (SVM) classifier for automated decision making. The CAD system was evaluated using a database consisting of 150 asymptomatic and 196 symptomatic plaque regions which were labeled using the ground truth based on the presence or absence of symptoms. Threefold cross-validation protocol was adapted for developing and testing the classifiers. We observed that the SVM classifier with a polynomial kernel of order 2 was to achieve a classification accuracy of 83.7%.

Intima-media thickness (IMT) is now being considered as an indicator of atherosclerosis. Our group has developed several feature-based IMT measurement algorithms such as the Completely Automated Layer EXtraction (CALEX) (which is a class of patented AtheroEdge Systems from Global Biomedical Technologies, Inc., CA, USA). These methods are based on the hypothesis that the highest pixel intensities are in the far wall of the common carotid artery (CCA) or the internal carotid artery (ICA). In this paper, we verify that this hypothesis holds true for B-mode longitudinal ultrasound (US) images of the carotid wall. This patented methodology consists of generating the composite image (the arithmetic sum of images) from the database by first registering the carotid image frames with respect to a nearly straight carotid-artery frame from the same database using: (1) B-spline-based nonrigid registration and (2) affine registration. Prior to registration, we segment the carotid-artery lumen using a level-set-based algorithm followed by morphological image processing. The binary lumen images are registered, and the transformations are applied to the original grayscale CCA images. We evaluated our technique using a database of 200 common carotid images of normal and pathologic carotids. The composite image presented the highest intensity distribution in the far wall of the CCA/ICA, validating our hypothesis. We have also demonstrated the accuracy and improvement in the IMT segmentation result with our CALEX 3.0 system. The CALEX system, when run on newly acquired US images, shows the IMT error of about 30 μm. Thus, we have shown that the CALEX algorithm is able to exploit the far-wall brightness for accurate IMT measurements.

The carotid plaques characterization is essential to decide about the possibility of surgical intervention (endarterectomy/stenting) on the patient. Soft and unstable plaques represent a major risk for the patient, as they are correlated with an augmented probability of brain infarction and emboli generation. Hence, the minimally-invasive characterization expecially of this type of carotid plaques is crucial in clinical practice. This paper presents an integrated system for the completely user-independent carotid plaque segmentation and characterization, based on ultrasound 2-D images. We show that using a ultrasound contrast agent, it is possible to segment also echolucent plaques with a percentage of misclassified pixels equal to 8%. After segmentation, the enhanced image is used to perform tissue characterization. We tested our system on 5 echolucent plaques and on 5 fibrous/stable plaques, showing that our system is capable of an accurate carotid wall segmentation and proper quantification of the percentages of blood, fat, calcium and fibrous tissue constituting the plaque. The system is very promising and it is being used in a neurology unit on patients already indicated for endarterectomy, with the purpose of correlating its output with histology.

Ultrasound has great potential to aid in the differential diagnosis of malignant and benign thyroid lesions, but interpretative pitfalls exist and the accuracy is still poor. To overcome these difficulties, we developed and analyzed a range of knowledge representation techniques, which are a class of ThyroScan™ algorithms from Global Biomedical Technologies Inc., California, USA, for automatic classification of benign and malignant thyroid lesions. The analysis is based on data obtained from twenty nodules (ten benign and ten malignant) taken from 3D contrast-enhanced ultrasound images. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture algorithms are used to extract relevant features from the thyroid images. The resulting feature vectors are fed to three different classifiers: K-Nearest Neighbor (K-NN), Probabilistic Neural Network (PNN), and Decision Tree (DeTr). The performance of these classifiers is compared using Receiver Operating Characteristic (ROC) curves. Our results show that combination of DWT and texture features coupled with K-NN resulted in good performance measures with the area of under the ROC curve of 0.987, a classification accuracy of 98.9%, a sensitivity of 98%, and a specificity of 99.8%. Finally, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI), which is made up of texture features, to diagnose benign or malignant nodules using just one index. We hope that this TMI will help clinicians in a more objective detection of benign and malignant thyroid lesions.