CAROTID - a web-based platform for optimal personalized management of atherosclerotic patients.

Carotid atherosclerosis is the main cause of fatal cerebral ischemic events, thereby posing a major burden for public health and state economies. We propose a web-based platform named CAROTID to address the need for optimal management of patients with carotid atherosclerosis in a twofold sense: (a) objective selection of patients who need carotid-revascularization (i.e., high-risk patients), using a multifaceted description of the disease consisting of ultrasound imaging, biochemical and clinical markers, and (b) effective storage and retrieval of patient data to facilitate frequent follow-ups and direct comparisons with related cases. These two services are achieved by two interconnected modules, namely the computer-aided diagnosis (CAD) tool and the intelligent archival system, in a unified, remotely accessible system. We present the design of the platform and we describe three main usage scenarios to demonstrate the CAROTID utilization in clinical practice. Additionally, the platform was evaluated in a real clinical environment in terms of CAD performance, end-user satisfaction and time spent on different functionalities. CAROTID classification of high- and low-risk cases was 87%; the corresponding stenosis-degree-based classification would have been 61%. Questionnaire-based user satisfaction showed encouraging results in terms of ease-of-use, clinical usefulness and patient data protection. Times for different CAROTID functionalities were generally short; as an example, the time spent for generating the diagnostic decision was 5min in case of 4-s ultrasound video. Large datasets and future evaluation sessions in multiple medical institutions are still necessary to reveal with confidence the full potential of the platform.

Comparison of block matching and differential methods for motion analysis of the carotid artery wall from ultrasound images.

Motion of the carotid artery wall is important for the quantification of arterial elasticity and contractility and can be estimated with a number of techniques. In this paper, a framework for quantitative evaluation of motion analysis techniques from B-mode ultrasound images is introduced. Six synthetic sequences were produced using 1) a real image corrupted by Gaussian and speckle noise of 25 and 15 dB, and 2) the ultrasound simulation package Field II. In both cases, a mathematical model was used, which simulated the motion of the arterial wall layers and the surrounding tissue, in the radial and longitudinal directions. The performance of four techniques, namely optical flow (OF (HS)), weighted least-squares optical flow (OF (LK(WLS))), block matching (BM), and affine block motion model (ABMM), was investigated in the context of this framework. The average warping indices were lowest for OF (LK(WLS)) (1.75 pixels), slightly higher for ABMM (2.01 pixels), and highest for BM (6.57 pixels) and OF (HS) (11.57 pixels). Due to its superior performance, OF (LK(WLS)) was used to quantify motion of selected regions of the arterial wall in real ultrasound image sequences of the carotid artery. Preliminary results indicate that OF (LK(WLS)) is promising, because it efficiently quantified radial, longitudinal, and shear strains in healthy adults and diseased subjects.

Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound.

In this paper, a multiresolution approach is suggested for texture classification of atherosclerotic tissue from B-mode ultrasound. Four decomposition schemes, namely, the discrete wavelet transform, the stationary wavelet transform, wavelet packets (WP), and Gabor transform (GT), as well as several basis functions, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. The mean and standard deviation of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included 1) ranking the features in terms of their divergence values and 2) appropriately thresholding by a nonlinear correlation coefficient. The selected features were subsequently input into two classifiers using support vector machines (SVM) and probabilistic neural networks. WP analysis and the coiflet 1 produced the highest overall classification performance (90% for diastole and 75% for systole) using SVM. This might reflect WP's ability to reveal differences in different frequency bands, and therefore, characterize efficiently the atheromatous tissue. An interesting finding was that the dominant texture features exhibited horizontal directionality, suggesting that texture analysis may be affected by biomechanical factors (plaque strains).

Affine optical flow combined with multiscale image analysis for motion estimation of the arterial wall from B-mode ultrasound.

This paper investigated the performance of affine optical flow (AFOF) in motion tracking of the arterial wall from B-mode ultrasound images and the effect of its combination with multiscale image analysis on the accuracy of the process. Multiscale AFOF (MAFOF) exploits the information obtained with AFOF from the approximation sub-images at different spatial resolution levels of the images, obtained using a 2D discrete wavelet transform. Both AFOF and MAFOF were evaluated through their application to synthetic image sequences of the common carotid artery. Multiscale image analysis increased the accuracy in motion tracking, with MAFOF yielding average displacement error reductions of 9% with respect to AFOF. The methods were also effectively applied to real ultrasound image sequences of the carotid artery. The results showed that MAFOF could be considered as a reliable estimator for arterial wall motion from B-mode ultrasound images.

A semantically-aided approach for online annotation and retrieval of medical images.

The need for annotating the continuously increasing volume of medical image data is recognized from medical experts for a variety of purposes, regardless if this is medical practice, research or education. The rich information content latent in medical images can be made explicit and formal with the use of well-defined ontologies. Evolution of the Semantic Web now offers a unique opportunity of a web-based, service-oriented approach. Remote access to FMA and ICD-10 reference ontologies provides the ontological annotation framework. The proposed system utilizes this infrastructure to provide a customizable and robust annotation procedure. It also provides an intelligent search mechanism indicating the advantages of semantic over keyword search. The common representation layer discussed facilitates interoperability between institutions and systems, while semantic content enables inference and knowledge integration.