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Acute myocarditis is a disease affecting the myocardial tissue, which is caused by infections, rheumatic diseases, especially sarcoidosis, or certain therapies. Its diagnosis may be difficult, owing to its variable clinical presentation. In this setting, cardiac magnetic resonance plays a pivotal role in detecting myocardial inflammation through qualitative, semiquantitative, and quantitative parameters, in particular with the new quantitative techniques such as T1 and T2 mapping, combined or not with late gadolinium enhancement evaluation. This is in accordance with the revised Lake Louise criteria. In this review, the emerging role of the new cutting-edge cardiac magnetic resonance imaging techniques in diagnosing myocarditis is extensively presented.

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The role of calcium in atherosclerosis is controversial and the relationship between vascular calcification and plaque vulnerability is not fully understood. Although calcifications are present in ≈50% to 60% of carotid plaques, their association with cerebrovascular ischemic events remains unclear. In this review, we summarize current understanding of carotid plaque calcification. We outline the role of calcium in atherosclerotic carotid disease by analyzing laboratory studies and histopathologic studies, as well as imaging findings to understand clinical implications of carotid artery calcifications. Differences in mechanism of calcium deposition express themselves into a wide range of calcification phenotypes in carotid plaques. Some patterns, such as rim calcification, are suggestive of plaques with inflammatory activity with leakage of the vasa vasourm and intraplaque hemorrhage. Other patterns such as dense, nodular calcifications may confer greater mechanical stability to the plaque and reduce the risk of embolization for a given degree of plaque size and luminal stenosis. Various distributions and patterns of carotid plaque calcification, often influenced by the underlying systemic pathological condition, have a different role in affecting plaque stability. Modern imaging techniques afford multiple approaches to assess geometry, pattern of distribution, size, and composition of carotid artery calcifications. Future investigations with these novel technologies will further improve our understanding of carotid artery calcification and will play an important role in understanding and minimizing stroke risk in patients with carotid plaques.

Background: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Methodology: Sixmodels, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. Results: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p< 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. Conclusions: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.

Background White-matter lesions (WMLs) are frequently found in magnetic resonance imaging (MRi), and the WML load tends to be higher in patients affected by cervical internal carotid artery (cICA) stenosis.Purpose This study aimed to investigate whether and how WMLs influence cerebral networking in patients with asymptomatic cICA stenosis eligible for carotid endarterectomy (CEA) by exploiting the connectometry technique.Methods The study was designed as a cross-sectional exploratory investigation, and 28 patients with cICA stenosis eligible for CEA were enrolled. All patients received an MRI scan, including a T1-weighted, a FLAIR and a diffusion-weighted (DW) sequence. The T1 and FLAIR sequences were analysed for quantification of WML burden (WMLB) and total number of WMLs (TNWMLs). The DW data were reconstructed in the MNI space using q-space diffeomorphic reconstruction, and were grouped to create a connectometry database. The connectometry analysis evaluated the influence of both the WMLB and TNWMLs to local connectivity in a multiple regression model that included age, WMLB and TNWMLs, adopting three different T-score thresholds (1, 2 and 3). A p-value corrected for false discovery rate of <0.05 was adopted as a threshold to identify statistically significant results.Results The connectometry analysis identified several white-matter bundles negatively correlated with WMLB; no statistically significant correlation was found for TNWMLs.Conclusion Results of our study suggest that WMLs influence brain connectivity measured by the connectometry technique in patients with cICA stenosis eligible for CEA. Further studies are warranted to understand the role of WMLs better as a marker of disease in patients with cICA stenosis.

Intraplaque hemorrhage (IPH) is one of the main factors involved in atherosclerotic plaque (AP) instability. Its recognition is crucial for the correct staging and management of patients with carotid artery plaques to limit ischemic stroke. Imaging plays a crucial role in identifying IPH, even if the great variability of intraplaque vascularization and the limitations of our current imaging technologies make it difficult. The intent of this review is to give a general overview of the main features of intraplaque vascularization and IPH on Ultrasound (US), Computed Tomography (CT), Magnetic Resonance (MR) and Nuclear Medicine, and a brief description on the future prospectives.

Purpose:To investigate the relationships between white matter hyperintensities (WMH) and hippocampal volume and their influence on brain networks by using resting-state functional connectivity (rs-fc) magnetic resonance (MR) according to their localization.Methods:In this exploratory cross-sectional study, 38 subjects from the public “Leipzig Study for Mind/Body/Emotion Interactions” (LEMON) data set were selected. Morphometric analyses of both WMH burden and the total hippocampal relative volume (tHRV) were performed for each subject with two automated software. The WMH were then categorized as total (tWMH), periventricular (pvWMH), deep (dWMH), and juxtacortical (jcWMH). Spearman's correlation analyses were performed to evaluate the relationships between the following variables: age, tWMH, pvWMH, dWMH, jcWMH, and tHRV. Subsequently, three different rs-fc MR group analyses were performed using a multiple regression model that included age, pvWMH, dWMH, and jcWMH as second-level covariates. The graph theoretical analysis was applied to evaluate the effects of pvWMH (analysis 1), jcWMH (analysis 2), and dWMH (analysis 3).Results:Spearman's correlation analysis revealed several statistically significant (p< 0.05) positive and negative correlations, in particular positive between age and tWMH, and negative between dWMH and tHRV. rs-fc MR analysis 1 and 2 did not reveal statistically significant results; analysis 3 revealed that dWMH influenced network properties of several cerebral regions, in particular global and local efficiency of both the hippocampi.Conclusion:The localization of WMH influences brain activity in healthy subjects. In particular, dWMH are inversely correlated with tHRV and influence several properties of different cerebral areas, included both the hippocampi.Impact statementIn this exploratory research we evidenced how both the load and the localization of white matter hyperintensities influence brain activity; in particular, we evidenced an inverse correlation between the volume of the deep white matter hyperintensities and hippocampal volume, as well as a direct influence on the connectivity properties of this important cerebral region. This finding represent a new element for understanding the effects of white matter hyperintensities on brain networking, and a cue that could be taken into account for possible future studies investigating brain connectivity and cognitive functions in healthy and pathological conditions.

Machine learning (ML)-based stroke risk stratification systems have typically focused on conventional risk factors (CRF) (AtheroRisk-conventional). Besides CRF, carotid ultrasound image phenotypes (CUSIP) have shown to be powerful phenotypes risk stratification. This is the first ML study of its kind that integrates CUSIP and CRF for risk stratification (AtheroRisk-integrated) and compares against AtheroRisk-conventional.

Introduction In the current paper, the “carotid artery calcium score” method is presented with the target to offer a metric method to quantify the amount of calcification in the carotid artery.Model and Definition The Volume of Interest (VOI) should be extracted and those voxels, with a Hounsfield Unit (HU) value ≥130, should be considered. The total weight value is determined by calculating the sum of the HU attenuation values of all voxels with values ≥130 HU. This value should be multiplied by the conversion factor (“or voxel size”) and divided by a weighting factor, the attenuation threshold to consider a voxel as calcified (and therefore 130 HU): this equation determines the Carotid Artery Calcium Score (CACS).Results In order to provide the demonstration of the potential feasibility of the model, the CACS was calculated in 131 subjects (94 males; mean age 72.7 years) for 235 carotid arteries (in 27 subjects, unilateral plaque was present) considered. The CACS value ranged from 0.67 to 11716. A statistically significant correlation was found (rho value = 0.663, pvalue = .0001) between the CACS in the right and left carotid plaques. Moreover, a statistically significant correlation between the age and the total CACS was present (rho value = 0.244, pvalue = .005), whereas no statistically significant difference was found in the distribution of CACS by gender (p= .148). The CACS was also tested at baseline and after contrast and no statistically significant difference was found.Conclusion In conclusion, this method is of easy application, and it weights at the same time the volume and the degree of calcification in a unique parameter. This method needs to be tested to verify its potential utility, similar to the coronary artery calcium score, for the risk stratification of the occurrence of cerebrovascular events of the anterior circulation. Further studies using this new diagnostic tool to determine the prognostic value of carotid calcium quantification are needed.

Currently, carotid intima-media thickness (cIMT) and geometric total plaque area (gTPA) are computed manually and thus are tedious and prone to interobserver and intraobserver variabilities. This study presents an intelligence-based automated deep learning (DL)–based technique for carotid wall interface detection, cIMT, and lumen diameter (LD) measurements, followed by a 3D cylindrical approach for TPA measurement. The observers were used for manual tracings of which were then used for the design of two DL-based systems. The DL boundaries for inner lumen wall and outer interadventitial borders were used for computing the cIMT and LD. Using cylindrical approach, we computed the gTPA. Furthermore, we compute the 10-year image-based cIMT and gTPA, using the progression rates. A total of 396 B-mode ultrasound right and left common carotid artery images were taken from 203 patients. The mean cIMT and gTPA using DL1 and DL2 is 0.91 mm, 20.52 mm2and 0.88 mm, 19.44 mm2, respectively. The coefficient of correlation between gTPA and cIMT using DL1 and DL2 is 0.92 (P< .001) and 0.94 (P< .001), respectively. The area under the curve (AUC) for gTPA showed an improvement over cIMT by 14.36% and 12.57% for DL1 and DL2, respectively. The corresponding 10-year risk improvements were 9.09% and 6.26%. Our statistical significance tests successfully passed ttest, Mann-Whitney, Wilcoxon, Kolmogorov-Smirnov, and Friedman. The study shows gTPA as an equally powerful carotid risk biomarker like cIMT. Given the cIMT and LD, cylindrical fitting is a fast method for gTPA measurements.

Common carotid artery lumen diameter (LD) ultrasound measurement systems are either manual or semi-automated and lack reproducibility and variability studies. This pilot study presents an automated and cloud-based LD measurements software system (AtheroCloud) and evaluates its: (i) intra/inter-operator reproducibility and (ii) intra/inter-observer variability.

The purpose of this work was to compare the image quality and radiation dose delivered to patients during computed tomography (CT) angiography (CTA) of the supra-aortic arteries using two single-source (SS) and two dual-energy (DE) CT scanners.

Objective Degree of carotid stenosis is an important predictor to assess risk of stroke. Systolic velocity-based methods for lumen diameter and stenosis measurement are subjective. Image-based methods face a challenge because of low gradients in media and intima walls.Methods This article presents AtheroEdge™ 2.0, a two-stage process for automated carotid lumen diameter measurement that combats the above challenges. Stage one uses spectral analysis based on the hypothesis that far-wall adventitia is brightest. Stage two uses lumen pixel region identification based on the assumption that blood flow has constant density. Using global and local processing, lumen boundaries are detected. This clinical system outputs lumen diameter along with stenosis severity index (SSI).Results Our database consists of institutional review board–approved 202 patients (males/females: 155/47) left and right common carotid artery images (404 images, Toshiba scanner). Two trained neuro radiologists performed manual lumen border tracings using ImgTracer™ software. The coefficient of correlation between automated and two manual readings was 0.91 and 0.92. Dice similarity and Jaccard index were 95.82%, 95.72% and 92.10%, 91.92%, respectively. The mean diameter error between automated and two manual readings was 0.27 ± 0.26 and 0.26 ± 0.28 mm, respectively. Precision of merit was 98.05% and 99.03% with respect to two readings. SSI showed 97% accuracy.Conclusions The image-based automated carotid lumen diameter and stenosis measurement system is fast, accurate, and reliable.

The relation between white matter loss (WML) and diabetes is still debated. The aim of this study was to investigate the correlation between typical WML– and diabetes-related magnetic resonance imaging (MRI) findings in a cohort of patients scheduled for carotid endarterectomy (CEA).

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given.

Ultrasonographic plaque echolucency has been studied as a stroke risk marker in carotid atherosclerotic disease. We performed a systematic review and meta-analysis to summarize the association between ultrasound-determined carotid plaque echolucency and future ipsilateral stroke risk.

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScanfor ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor.

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.

In this work, we have proposed an on-line computer-aided diagnostic system called “UroImage” that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future.

Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Preoperative determination of whether a tumor is benign or malignant has often been found to be difficult. Because of such inconclusive findings from ultrasound images and other tests, many patients with benign conditions have been offered unnecessary surgeries thereby increasing patient anxiety and healthcare cost. The key objective of our work is to develop an adjunct Computer Aided Diagnostic (CAD) technique that uses ultrasound images of the ovary and image mining algorithms to accurately classify benign and malignant ovarian tumor images. In this algorithm, we extract texture features based on Local Binary Patterns (LBP) and Laws Texture Energy (LTE) and use them to build and train a Support Vector Machine (SVM) classifier. Our technique was validated using 1000 benign and 1000 malignant images, and we obtained a high accuracy of 99.9% using a SVM classifier with a Radial Basis Function (RBF) kernel. The high accuracy can be attributed to the determination of the novel combination of the 16 texture based features that quantify the subtle changes in the images belonging to both classes. The proposed algorithm has the following characteristics: cost-effectiveness, complete automation, easy deployment, and good end-user comprehensibility. We have also developed a novel integrated index, Ovarian Cancer Index (OCI), which is a combination of the texture features, to present the physicians with a more transparent adjunct technique for ovarian tumor classification.

Purpose The increased thickness of the carotid artery is associated with the development of coronary and cerebrovascular events. In this study our purpose was to evaluate the carotid artery wall thickness (CAWT) by using multidetector-row computed tomography angiography (MDCTA) and the intima media thickness (IMT) by using semiautomated ultrasonography (SA-US) to evaluate the agreement between the two methods.Methods This is a retrospective study, and the institutional review board approval was obtained. Twenty-one patients (age range, 59–81 years) were analyzed with the use of a 16-detector row CT and a sonographic scanner. In total, 14 subjects had shown cerebral ischemic symptoms. The IMT was quantified by the use of specific semiautomated software (ImgTracer™, Global Biomedical Technologies, Roseville, CA) by four expert observers, and the CAWT was measured by use of the MDCTA. Data were compared with the Wilcoxon test for paired samples. Bland–Altman statistics was used to measure the agreement between MDCTA and SA-US. A pvalue < 0.05 was considered significant.Results Forty-two carotids were analyzed, and the CAWT ranged from 0.64 to 2.99 mm, with a mean value of 1.438 mm. By analyzing the Bland–Altman plots, we observed a good agreement between SA-US and correlation coefficient rwere 0.9250 (95% confidence interval [CI] 0.864–0.959; p< 0.0001), 0.9265 (95% CI 0.866–0.961; p< 0.0001), 0.9466 (95% CI 0.902–0.971; p< 0.0001), and 0.8621 (95% CI: 0.756–0.924; p< 0.0001) for observer 1, observer 2, observer 3 and observer 4 respectively.Conclusions Data of this preliminary study by using SA-US and MDCTA demonstrated a good agreement between in the measurement of CAWT and IMT.

The carotid intima-media thickness (IMT) is the most used marker for the progression of atherosclerosis and onset of the cardiovascular diseases. Computer-aided measurements improve accuracy, but usually require user interaction. In this paper we characterized a new and completely automated technique for carotid segmentation and IMT measurement based on the merits of two previously developed techniques. We used an integrated approach of intelligent image feature extraction and line fitting for automatically locating the carotid artery in the image frame, followed by wall interfaces extraction based on Gaussian edge operator. We called our system - CARES. We validated the CARES on a multi-institutional database of 300 carotid ultrasound images. IMT measurement bias was 0.032 ± 0.141 mm, better than other automated techniques and comparable to that of user-driven methodologies. Our novel approach of CARES processed 96% of the images leading to the figure of merit to be 95.7%. CARES ensured complete automation and high accuracy in IMT measurement; hence it could be a suitable clinical tool for processing of large datasets in multicenter studies involving atherosclerosis.pre-

Since 2005, our research team has been developing automated techniques for carotid artery (CA) wall segmentation and intima-media thickness (IMT) measurement. We developed a snake-based technique (which we named CULEX1,2), a method based on an integrated approach of feature extraction, fitting, and classification (which we named CALEX3), and a watershed transform based algorithm4. Each of the previous methods substantially consisted in two distinct stages: Stage-I - Automatic carotid artery detection. In this step, intelligent procedures were adopted to automatically locate the CA in the image frame. Stage-II - CA wall segmentation and IMT measurement. In this second step, the CA distal (or far) wall is segmented in order to trace the lumen-intima (LI) and media-adventitia (MA) boundaries. The distance between the LI/MA borders is the IMT estimation. The aim of this paper is the description of a novel and completely automated technique for carotid artery segmentation and IMT measurement based on an innovative multi-resolution approach.

Introduction Better methods are needed to determine the course of intervention in patients with atherosclerosis; therefore, plaque characterization is increasing in importance. Current guidelines suggest that the degree of stenosis and symptoms are the only criteria for the selection of the surgical intervention. However, there remain some challenges. The characterization of plaque morphology may help determine the best course of therapy. Magnetic resonance imaging and computed tomography are current standard techniques to evaluate plaque morphology, but they are expensive and unsuitable for long term surveillance and monitoring.Objective In this research, an ultrasound-based methodology for the characterization of carotid plaques is shown. This technique requires the injection of a small volume (approximately 1.5 mL) of contrast agent and the acquisition of postcontrast images. The rationale of this technique is that poorly perfused tissues (such as lipids) show a lower contrast enhancement with respect to highly perfused tissues (such as fibrous and muscular tissue).Methods The technique consists of two steps. First, the plaque region is automatically segmented by a completely user-independent algorithm. Then, the portion of the wall corresponding to the plaque is analyzed and color-coded intensity is assigned to a specific tissue. Performance evaluation was performed against histology. Twenty plaque specimens were sent to pathology for reporting. Correlation of the histology report and of the contrast-enhanced ultrasound analysis was performed.Results Plaque components that could be effectively identified were thrombi, lipids, fibrous/muscular tissue, and calcium. Overall the errors on 20 plaques between automated classification and histology were: 3.1 ± 1.1% for thrombus, 4.2 ± 1.5% for lipids, 5 ± 3.4% for fibrous/muscular tissue, and 3.2 ± 1.0% for calcium.Conclusion Despite the need for further investigation and a quantitative evaluation of the results, this methodology showed encouraging results. This analysis architecture is undergoing validation in a neurology division and is aimed at being used for the follow-up of patients and quantification of drug therapy effects.

The intima–media thickness (IMT) of carotid arteries is a reliable indicator of cardiovascular risk. IMT is usually manually measured on longitudinal B-mode ultrasound images. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. Most of these, however, require a certain degree of user interaction. We recently developed two automated techniques for segmentation of carotid arteries in ultrasound images and its intima–media thickness (IMT) measurement. The first technique was determined on the basis of local statistics and signal analysis (we named it CULEXsa, ie, Completely User-independent Layer EXtraction based on signal analysis). The second technique was determined on the basis of an integrated approach consisting of feature extraction, line fitting, and classification (or CALEXia, Completely Automated Layers EXtraction based on integrated approach). Both the techniques automatically traced the lumen—intima (LI) and the media—adventitia (MA) boundaries of the carotid wall. IMT was measured as the distance between LI and MA. We observed that CULEXsa offers better performance in the LI segmentation and CALEXia offers better performance in MA segmentation. The goal of this research was to estimate the IMT measurement by using a greedy approach by fusing CULEXsa and CALEXia segmentations, given the ground truth (manually traced by experts). Starting from the technique with the lower system error (CULEXsa for LI and CALEXia for MA), we iteratively swapped the vertices of the profiles until we minimized its overall distance with respect to manual boundary. The fusion boundary, consisting of points of CULEXsa and points of CALEXia, was the Greedy boundary. We used the polyline distance as a metric for both performance evaluation and error minimization. We ran the segmentation protocol over the database of 200 carotid longitudinal B-mode ultrasound images and compared the performance of all the three techniques. The mean error of the greedy technique yielded 0.42 ± 0.89 pixel (26.3 ± 55.6 μm) for the LI boundary (a 12.5 ± 5.6% improvement over CULEXsa) and 0.26 ± 0.56 pixel (16.2 ± 31.3 μm) for MA boundary (a 16.1 ± 6.7% improvement over CALEXia). IMT measurement error is 1.33 ± 0.99 pixel (83.1 ± 61.8 μm), a 3.6 ± 1.4% improvement over CULEXsa.

Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of cerebral cortical segmentation both in 2-D and 3-D from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. In Part I of this research (see Suri et al [1]), an attempt was made to review the state-of-the-art in 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on two main classes: region- and boundary/surface-based. More than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions were shown using region-based techniques. We also showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes using boundary/surface-based techniques. This paper (Part II) focuses on presenting state-of-the-art systems based on the fusion of boundary/surface-based with region-based techniques, also called regional-geometric deformation models, which takes the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of these various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy. Special emphasis is placed on discussing the advantages, validation, challenges and neuro-science/clinical applications of cortical segmentation.

Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of the cerebral cortical segmentation, both in 2-D and 3-D, from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. This paper is an attempt to review the state-of-the-art 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on three main classes: region-based, boundary/surface-based and fusion of boundary/surface-based with region-based techniques. In the first class, region-based techniques, we demonstrated more than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions. In the second class, boundary/surface-based, we showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes. Particular emphasis will be placed by presenting four state-of-the-art systems in the third class, based on the fusion of boundary/surface-based with region-based techniques outlined in Part II of the paper, also called regional-geometric deformation models, which take the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy.

Abstract:: In the last decade, computer vision, pattern recognition, image processing and cardiac researchers have given immense attention to cardiac image analysis and modelling. This paper survets state-of-the-art computer vision and pattern recognition techniques for Left Ventricle (LV) segmentation and modelling during the second half of the twentieth century. The paper presents the key charateristics of successful model-based segmentation and modelling during the second half of the twentieth century. The paper presents the key characteristics of successful model-based segmentation techniques for LV modelling. This survey paper concludes the following: (1) any one pattern recognition or computer vision technique is not sufficient for accurate 2D, 3D or 4D modelling of LV; (2) fitting mathematical models for LV modelling have dominated in the last 15 tears; (3) knowledge extrated from the ground truth has lead to very successful attempts for LV modelling have dominated in the last 15 uears; (3) knowledge extracted from the ground truth has lead to very successful attempts at LV modelling;(4) spatial and temporal behaviour of LV through different imaging modalities has yielded information which has led to accurate LV modelling; and (5) not much attention has been paid to LC modelling validation.

Pathologists, microbiologists and cytologists are very interested to automatically identify and quantify the sperms, cells or nucleus in the cellular level images. Complexities like voluminous data sets, variability in data sets, millions of colors, and different kinds of artifacts make the detection and quantification process very difficult. This paper is an attempt to design a sophisticated cellular diagnostic system based on color image processing, mathematical morphology and connected components based on run length encoding. This system runs on Windows ‘98/NT PC platform, in Visual C++, 6.0 environment using three different architectures: Single document interface, multiple document interface and dialog based applications. The system takes around 1.5 seconds per image of 512 x 484 square pixels using high speed threading architecturewritten on the on 400 MHz Pentiumll processor. The system has an accuracy of 95%. The software has been validated tested on NASA and machine vision real world images.

High-resolution ultrasonography (HRUS) has potentialities in differential diagnosis between malignant and benign thyroid lesions, but interpretative pitfalls remain and accuracy is still poor. We developed an image processing technique for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (ten malignant) were analyzed by 3-D contrast-enhanced ultrasound imaging. The 3-D volumes were preprocessed and skeletonized. Seven vascular parameters were computed on the skeletons: number of vascular trees (NT); vascular density (VD); number of branching nodes (or branching points) (NB); mean vessel radius (MR); 2-D (DM) and 3-D (SOAM) tortuosity; and inflection count metric (ICM). Results showed that the malignant nodules had higher values of NT (83.1 vs. 18.1), VD (00.4 vs. 0.01), NB (1453 vs. 552), DM (51 vs. 18), ICM (19.9 vs. 8.7), and SOAM (26 vs. 11). Quantification of nodular vascularization based on 3-D contrast-enhanced ultrasound and skeletonization could help differential diagnosis of thyroid lesions.